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2025
Thavanesan, Navamayooran; Naiseh, Mohammad; Terol, Miguel; Rahman, Saqib Andrew; Hill, Samuel Luke; Parfitt, Charlotte; Walters, Zoë S; Ramchurn, Sarvapali; Markar, Sheraz; Owen, Richard; Maynard, Nick; Azim, Tayyaba; Belkatir, Zehor; Perez, Elvira Vallejos; McCord, Mimi; Underwood, Tim; Vigneswaran, Ganesh
The oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning clinical decision support system Journal Article
In: EClinicalMedicine, vol. 89, pp. 103527, 2025, (For the purposes of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Decision support tool, machine learning, MDT, Multidisciplinary teams, Oesophageal cancer
@article{soton506614,
title = {The oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning clinical decision support system},
author = {Navamayooran Thavanesan and Mohammad Naiseh and Miguel Terol and Saqib Andrew Rahman and Samuel Luke Hill and Charlotte Parfitt and Zoë S Walters and Sarvapali Ramchurn and Sheraz Markar and Richard Owen and Nick Maynard and Tayyaba Azim and Zehor Belkatir and Elvira Vallejos Perez and Mimi McCord and Tim Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/506614/},
year = {2025},
date = {2025-09-01},
journal = {EClinicalMedicine},
volume = {89},
pages = {103527},
abstract = {ensuremath<pensuremath>BACKGROUND: The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.ensuremath</pensuremath>ensuremath<pensuremath>METHODS: Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.ensuremath</pensuremath>ensuremath<pensuremath>FINDINGS: Cohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 $±$ 0.048, XGB 0.909 $±$ 0.044 and RF 0.883 $±$ 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean AUCs were: MLR 0.805 $±$ 0.096, XGB 0.815 $±$ 0.081 and RF 0.793 $±$ 0.083 (k = 5 cross-validation) and MLR 0.736 (95% CI 0.734-0.737), XGB 0.799 (0.798-0.800), RF 0.781 (0.778-0.782) on bootstrapped resampling. On external validation (OUH cohort), AUCs were MLR 0.894, XGB 0.887 and RF 0.891 for the primary treatment model and MLR 0.711, XGB 0.742 and RF 0.730 for the palliative treatment classifier. Predicted survival probability from the palliative survival model was well calibrated over the first 12 months post-diagnosis in both cohorts. The RRI program provided a collaborative environment leading to valuable modifications to the CDSS including prediction explanations, visual aids for survival and integrated education for users producing a user-friendly and quick to use tool.ensuremath</pensuremath>ensuremath<pensuremath>INTERPRETATION: We present a novel, responsibly developed, externally validated AI CDSS trained to predict oesophageal cancer MDT decisions. It represents the foundations of a transformative application of ML, personalised, consistent and efficient MDT decision-support within OC which aligns to RRI principles.ensuremath</pensuremath>ensuremath<pensuremath>FUNDING: Doctoral Studentship for NT (Institute for Life Sciences (University of Southampton) & University Hospital Southampton), UKRI TAS Pump-Priming Grant (TAS_PP_00167).ensuremath</pensuremath>},
note = {For the purposes of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.},
keywords = {Artificial Intelligence, Decision support tool, machine learning, MDT, Multidisciplinary teams, Oesophageal cancer},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>BACKGROUND: The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.ensuremath</pensuremath>ensuremath<pensuremath>METHODS: Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.ensuremath</pensuremath>ensuremath<pensuremath>FINDINGS: Cohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 $±$ 0.048, XGB 0.909 $±$ 0.044 and RF 0.883 $±$ 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean AUCs were: MLR 0.805 $±$ 0.096, XGB 0.815 $±$ 0.081 and RF 0.793 $±$ 0.083 (k = 5 cross-validation) and MLR 0.736 (95% CI 0.734-0.737), XGB 0.799 (0.798-0.800), RF 0.781 (0.778-0.782) on bootstrapped resampling. On external validation (OUH cohort), AUCs were MLR 0.894, XGB 0.887 and RF 0.891 for the primary treatment model and MLR 0.711, XGB 0.742 and RF 0.730 for the palliative treatment classifier. Predicted survival probability from the palliative survival model was well calibrated over the first 12 months post-diagnosis in both cohorts. The RRI program provided a collaborative environment leading to valuable modifications to the CDSS including prediction explanations, visual aids for survival and integrated education for users producing a user-friendly and quick to use tool.ensuremath</pensuremath>ensuremath<pensuremath>INTERPRETATION: We present a novel, responsibly developed, externally validated AI CDSS trained to predict oesophageal cancer MDT decisions. It represents the foundations of a transformative application of ML, personalised, consistent and efficient MDT decision-support within OC which aligns to RRI principles.ensuremath</pensuremath>ensuremath<pensuremath>FUNDING: Doctoral Studentship for NT (Institute for Life Sciences (University of Southampton) & University Hospital Southampton), UKRI TAS Pump-Priming Grant (TAS_PP_00167).ensuremath</pensuremath>
2024
Thavanesan, Navamayooran; Farahi, Arya; Parfitt, Charlotte; Belkhatir, Zehor; Azim, Tayyaba; Vallejos, Elvira Perez; Walters, Zoë; Ramchurn, Sarvapali; Underwood, Timothy J.; Vigneswaran, Ganesh
Insights from explainable AI in oesophageal cancer team decisions Journal Article
In: Computers in Biology and Medicine, vol. 180, 2024, (For the purpose of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.).
Abstract | Links | BibTeX | Tags: Decision-making, machine learning, Multidisciplinary teams, Oesophageal cancer
@article{soton493238,
title = {Insights from explainable AI in oesophageal cancer team decisions},
author = {Navamayooran Thavanesan and Arya Farahi and Charlotte Parfitt and Zehor Belkhatir and Tayyaba Azim and Elvira Perez Vallejos and Zoë Walters and Sarvapali Ramchurn and Timothy J. Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/493238/},
year = {2024},
date = {2024-08-01},
journal = {Computers in Biology and Medicine},
volume = {180},
abstract = {ensuremath<pensuremath>Background: clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).ensuremath</pensuremath>ensuremath<pensuremath>Methods: retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.�ensuremath</pensuremath>ensuremath<pensuremath>Results: amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75?85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.�ensuremath</pensuremath>ensuremath<pensuremath>Conclusion: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.ensuremath</pensuremath>},
note = {For the purpose of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.},
keywords = {Decision-making, machine learning, Multidisciplinary teams, Oesophageal cancer},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>Background: clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).ensuremath</pensuremath>ensuremath<pensuremath>Methods: retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.�ensuremath</pensuremath>ensuremath<pensuremath>Results: amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75?85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.�ensuremath</pensuremath>ensuremath<pensuremath>Conclusion: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.ensuremath</pensuremath>
1.
Thavanesan, Navamayooran; Naiseh, Mohammad; Terol, Miguel; Rahman, Saqib Andrew; Hill, Samuel Luke; Parfitt, Charlotte; Walters, Zoë S; Ramchurn, Sarvapali; Markar, Sheraz; Owen, Richard; Maynard, Nick; Azim, Tayyaba; Belkatir, Zehor; Perez, Elvira Vallejos; McCord, Mimi; Underwood, Tim; Vigneswaran, Ganesh
The oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning clinical decision support system Journal Article
In: EClinicalMedicine, vol. 89, pp. 103527, 2025, (For the purposes of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.).
@article{soton506614,
title = {The oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning clinical decision support system},
author = {Navamayooran Thavanesan and Mohammad Naiseh and Miguel Terol and Saqib Andrew Rahman and Samuel Luke Hill and Charlotte Parfitt and Zoë S Walters and Sarvapali Ramchurn and Sheraz Markar and Richard Owen and Nick Maynard and Tayyaba Azim and Zehor Belkatir and Elvira Vallejos Perez and Mimi McCord and Tim Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/506614/},
year = {2025},
date = {2025-09-01},
journal = {EClinicalMedicine},
volume = {89},
pages = {103527},
abstract = {ensuremath<pensuremath>BACKGROUND: The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.ensuremath</pensuremath>ensuremath<pensuremath>METHODS: Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.ensuremath</pensuremath>ensuremath<pensuremath>FINDINGS: Cohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 $±$ 0.048, XGB 0.909 $±$ 0.044 and RF 0.883 $±$ 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean AUCs were: MLR 0.805 $±$ 0.096, XGB 0.815 $±$ 0.081 and RF 0.793 $±$ 0.083 (k = 5 cross-validation) and MLR 0.736 (95% CI 0.734-0.737), XGB 0.799 (0.798-0.800), RF 0.781 (0.778-0.782) on bootstrapped resampling. On external validation (OUH cohort), AUCs were MLR 0.894, XGB 0.887 and RF 0.891 for the primary treatment model and MLR 0.711, XGB 0.742 and RF 0.730 for the palliative treatment classifier. Predicted survival probability from the palliative survival model was well calibrated over the first 12 months post-diagnosis in both cohorts. The RRI program provided a collaborative environment leading to valuable modifications to the CDSS including prediction explanations, visual aids for survival and integrated education for users producing a user-friendly and quick to use tool.ensuremath</pensuremath>ensuremath<pensuremath>INTERPRETATION: We present a novel, responsibly developed, externally validated AI CDSS trained to predict oesophageal cancer MDT decisions. It represents the foundations of a transformative application of ML, personalised, consistent and efficient MDT decision-support within OC which aligns to RRI principles.ensuremath</pensuremath>ensuremath<pensuremath>FUNDING: Doctoral Studentship for NT (Institute for Life Sciences (University of Southampton) & University Hospital Southampton), UKRI TAS Pump-Priming Grant (TAS_PP_00167).ensuremath</pensuremath>},
note = {For the purposes of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>BACKGROUND: The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.ensuremath</pensuremath>ensuremath<pensuremath>METHODS: Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.ensuremath</pensuremath>ensuremath<pensuremath>FINDINGS: Cohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 $±$ 0.048, XGB 0.909 $±$ 0.044 and RF 0.883 $±$ 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean AUCs were: MLR 0.805 $±$ 0.096, XGB 0.815 $±$ 0.081 and RF 0.793 $±$ 0.083 (k = 5 cross-validation) and MLR 0.736 (95% CI 0.734-0.737), XGB 0.799 (0.798-0.800), RF 0.781 (0.778-0.782) on bootstrapped resampling. On external validation (OUH cohort), AUCs were MLR 0.894, XGB 0.887 and RF 0.891 for the primary treatment model and MLR 0.711, XGB 0.742 and RF 0.730 for the palliative treatment classifier. Predicted survival probability from the palliative survival model was well calibrated over the first 12 months post-diagnosis in both cohorts. The RRI program provided a collaborative environment leading to valuable modifications to the CDSS including prediction explanations, visual aids for survival and integrated education for users producing a user-friendly and quick to use tool.ensuremath</pensuremath>ensuremath<pensuremath>INTERPRETATION: We present a novel, responsibly developed, externally validated AI CDSS trained to predict oesophageal cancer MDT decisions. It represents the foundations of a transformative application of ML, personalised, consistent and efficient MDT decision-support within OC which aligns to RRI principles.ensuremath</pensuremath>ensuremath<pensuremath>FUNDING: Doctoral Studentship for NT (Institute for Life Sciences (University of Southampton) & University Hospital Southampton), UKRI TAS Pump-Priming Grant (TAS_PP_00167).ensuremath</pensuremath>
2.
Thavanesan, Navamayooran; Farahi, Arya; Parfitt, Charlotte; Belkhatir, Zehor; Azim, Tayyaba; Vallejos, Elvira Perez; Walters, Zoë; Ramchurn, Sarvapali; Underwood, Timothy J.; Vigneswaran, Ganesh
Insights from explainable AI in oesophageal cancer team decisions Journal Article
In: Computers in Biology and Medicine, vol. 180, 2024, (For the purpose of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.).
@article{soton493238,
title = {Insights from explainable AI in oesophageal cancer team decisions},
author = {Navamayooran Thavanesan and Arya Farahi and Charlotte Parfitt and Zehor Belkhatir and Tayyaba Azim and Elvira Perez Vallejos and Zoë Walters and Sarvapali Ramchurn and Timothy J. Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/493238/},
year = {2024},
date = {2024-08-01},
journal = {Computers in Biology and Medicine},
volume = {180},
abstract = {ensuremath<pensuremath>Background: clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).ensuremath</pensuremath>ensuremath<pensuremath>Methods: retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.�ensuremath</pensuremath>ensuremath<pensuremath>Results: amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75?85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.�ensuremath</pensuremath>ensuremath<pensuremath>Conclusion: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.ensuremath</pensuremath>},
note = {For the purpose of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>Background: clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).ensuremath</pensuremath>ensuremath<pensuremath>Methods: retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.�ensuremath</pensuremath>ensuremath<pensuremath>Results: amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75?85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.�ensuremath</pensuremath>ensuremath<pensuremath>Conclusion: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.ensuremath</pensuremath>
2025
Thavanesan, Navamayooran; Naiseh, Mohammad; Terol, Miguel; Rahman, Saqib Andrew; Hill, Samuel Luke; Parfitt, Charlotte; Walters, Zoë S; Ramchurn, Sarvapali; Markar, Sheraz; Owen, Richard; Maynard, Nick; Azim, Tayyaba; Belkatir, Zehor; Perez, Elvira Vallejos; McCord, Mimi; Underwood, Tim; Vigneswaran, Ganesh
The oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning clinical decision support system Journal Article
In: EClinicalMedicine, vol. 89, pp. 103527, 2025, (For the purposes of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Decision support tool, machine learning, MDT, Multidisciplinary teams, Oesophageal cancer
@article{soton506614,
title = {The oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning clinical decision support system},
author = {Navamayooran Thavanesan and Mohammad Naiseh and Miguel Terol and Saqib Andrew Rahman and Samuel Luke Hill and Charlotte Parfitt and Zoë S Walters and Sarvapali Ramchurn and Sheraz Markar and Richard Owen and Nick Maynard and Tayyaba Azim and Zehor Belkatir and Elvira Vallejos Perez and Mimi McCord and Tim Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/506614/},
year = {2025},
date = {2025-09-01},
journal = {EClinicalMedicine},
volume = {89},
pages = {103527},
abstract = {ensuremath<pensuremath>BACKGROUND: The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.ensuremath</pensuremath>ensuremath<pensuremath>METHODS: Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.ensuremath</pensuremath>ensuremath<pensuremath>FINDINGS: Cohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 $±$ 0.048, XGB 0.909 $±$ 0.044 and RF 0.883 $±$ 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean AUCs were: MLR 0.805 $±$ 0.096, XGB 0.815 $±$ 0.081 and RF 0.793 $±$ 0.083 (k = 5 cross-validation) and MLR 0.736 (95% CI 0.734-0.737), XGB 0.799 (0.798-0.800), RF 0.781 (0.778-0.782) on bootstrapped resampling. On external validation (OUH cohort), AUCs were MLR 0.894, XGB 0.887 and RF 0.891 for the primary treatment model and MLR 0.711, XGB 0.742 and RF 0.730 for the palliative treatment classifier. Predicted survival probability from the palliative survival model was well calibrated over the first 12 months post-diagnosis in both cohorts. The RRI program provided a collaborative environment leading to valuable modifications to the CDSS including prediction explanations, visual aids for survival and integrated education for users producing a user-friendly and quick to use tool.ensuremath</pensuremath>ensuremath<pensuremath>INTERPRETATION: We present a novel, responsibly developed, externally validated AI CDSS trained to predict oesophageal cancer MDT decisions. It represents the foundations of a transformative application of ML, personalised, consistent and efficient MDT decision-support within OC which aligns to RRI principles.ensuremath</pensuremath>ensuremath<pensuremath>FUNDING: Doctoral Studentship for NT (Institute for Life Sciences (University of Southampton) & University Hospital Southampton), UKRI TAS Pump-Priming Grant (TAS_PP_00167).ensuremath</pensuremath>},
note = {For the purposes of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.},
keywords = {Artificial Intelligence, Decision support tool, machine learning, MDT, Multidisciplinary teams, Oesophageal cancer},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>BACKGROUND: The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.ensuremath</pensuremath>ensuremath<pensuremath>METHODS: Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.ensuremath</pensuremath>ensuremath<pensuremath>FINDINGS: Cohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 $±$ 0.048, XGB 0.909 $±$ 0.044 and RF 0.883 $±$ 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean AUCs were: MLR 0.805 $±$ 0.096, XGB 0.815 $±$ 0.081 and RF 0.793 $±$ 0.083 (k = 5 cross-validation) and MLR 0.736 (95% CI 0.734-0.737), XGB 0.799 (0.798-0.800), RF 0.781 (0.778-0.782) on bootstrapped resampling. On external validation (OUH cohort), AUCs were MLR 0.894, XGB 0.887 and RF 0.891 for the primary treatment model and MLR 0.711, XGB 0.742 and RF 0.730 for the palliative treatment classifier. Predicted survival probability from the palliative survival model was well calibrated over the first 12 months post-diagnosis in both cohorts. The RRI program provided a collaborative environment leading to valuable modifications to the CDSS including prediction explanations, visual aids for survival and integrated education for users producing a user-friendly and quick to use tool.ensuremath</pensuremath>ensuremath<pensuremath>INTERPRETATION: We present a novel, responsibly developed, externally validated AI CDSS trained to predict oesophageal cancer MDT decisions. It represents the foundations of a transformative application of ML, personalised, consistent and efficient MDT decision-support within OC which aligns to RRI principles.ensuremath</pensuremath>ensuremath<pensuremath>FUNDING: Doctoral Studentship for NT (Institute for Life Sciences (University of Southampton) & University Hospital Southampton), UKRI TAS Pump-Priming Grant (TAS_PP_00167).ensuremath</pensuremath>
2024
Thavanesan, Navamayooran; Farahi, Arya; Parfitt, Charlotte; Belkhatir, Zehor; Azim, Tayyaba; Vallejos, Elvira Perez; Walters, Zoë; Ramchurn, Sarvapali; Underwood, Timothy J.; Vigneswaran, Ganesh
Insights from explainable AI in oesophageal cancer team decisions Journal Article
In: Computers in Biology and Medicine, vol. 180, 2024, (For the purpose of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.).
Abstract | Links | BibTeX | Tags: Decision-making, machine learning, Multidisciplinary teams, Oesophageal cancer
@article{soton493238,
title = {Insights from explainable AI in oesophageal cancer team decisions},
author = {Navamayooran Thavanesan and Arya Farahi and Charlotte Parfitt and Zehor Belkhatir and Tayyaba Azim and Elvira Perez Vallejos and Zoë Walters and Sarvapali Ramchurn and Timothy J. Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/493238/},
year = {2024},
date = {2024-08-01},
journal = {Computers in Biology and Medicine},
volume = {180},
abstract = {ensuremath<pensuremath>Background: clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).ensuremath</pensuremath>ensuremath<pensuremath>Methods: retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.�ensuremath</pensuremath>ensuremath<pensuremath>Results: amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75?85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.�ensuremath</pensuremath>ensuremath<pensuremath>Conclusion: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.ensuremath</pensuremath>},
note = {For the purpose of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.},
keywords = {Decision-making, machine learning, Multidisciplinary teams, Oesophageal cancer},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>Background: clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).ensuremath</pensuremath>ensuremath<pensuremath>Methods: retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.�ensuremath</pensuremath>ensuremath<pensuremath>Results: amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75?85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.�ensuremath</pensuremath>ensuremath<pensuremath>Conclusion: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.ensuremath</pensuremath>
1.
Thavanesan, Navamayooran; Naiseh, Mohammad; Terol, Miguel; Rahman, Saqib Andrew; Hill, Samuel Luke; Parfitt, Charlotte; Walters, Zoë S; Ramchurn, Sarvapali; Markar, Sheraz; Owen, Richard; Maynard, Nick; Azim, Tayyaba; Belkatir, Zehor; Perez, Elvira Vallejos; McCord, Mimi; Underwood, Tim; Vigneswaran, Ganesh
The oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning clinical decision support system Journal Article
In: EClinicalMedicine, vol. 89, pp. 103527, 2025, (For the purposes of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.).
@article{soton506614,
title = {The oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning clinical decision support system},
author = {Navamayooran Thavanesan and Mohammad Naiseh and Miguel Terol and Saqib Andrew Rahman and Samuel Luke Hill and Charlotte Parfitt and Zoë S Walters and Sarvapali Ramchurn and Sheraz Markar and Richard Owen and Nick Maynard and Tayyaba Azim and Zehor Belkatir and Elvira Vallejos Perez and Mimi McCord and Tim Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/506614/},
year = {2025},
date = {2025-09-01},
journal = {EClinicalMedicine},
volume = {89},
pages = {103527},
abstract = {ensuremath<pensuremath>BACKGROUND: The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.ensuremath</pensuremath>ensuremath<pensuremath>METHODS: Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.ensuremath</pensuremath>ensuremath<pensuremath>FINDINGS: Cohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 $±$ 0.048, XGB 0.909 $±$ 0.044 and RF 0.883 $±$ 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean AUCs were: MLR 0.805 $±$ 0.096, XGB 0.815 $±$ 0.081 and RF 0.793 $±$ 0.083 (k = 5 cross-validation) and MLR 0.736 (95% CI 0.734-0.737), XGB 0.799 (0.798-0.800), RF 0.781 (0.778-0.782) on bootstrapped resampling. On external validation (OUH cohort), AUCs were MLR 0.894, XGB 0.887 and RF 0.891 for the primary treatment model and MLR 0.711, XGB 0.742 and RF 0.730 for the palliative treatment classifier. Predicted survival probability from the palliative survival model was well calibrated over the first 12 months post-diagnosis in both cohorts. The RRI program provided a collaborative environment leading to valuable modifications to the CDSS including prediction explanations, visual aids for survival and integrated education for users producing a user-friendly and quick to use tool.ensuremath</pensuremath>ensuremath<pensuremath>INTERPRETATION: We present a novel, responsibly developed, externally validated AI CDSS trained to predict oesophageal cancer MDT decisions. It represents the foundations of a transformative application of ML, personalised, consistent and efficient MDT decision-support within OC which aligns to RRI principles.ensuremath</pensuremath>ensuremath<pensuremath>FUNDING: Doctoral Studentship for NT (Institute for Life Sciences (University of Southampton) & University Hospital Southampton), UKRI TAS Pump-Priming Grant (TAS_PP_00167).ensuremath</pensuremath>},
note = {For the purposes of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>BACKGROUND: The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.ensuremath</pensuremath>ensuremath<pensuremath>METHODS: Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.ensuremath</pensuremath>ensuremath<pensuremath>FINDINGS: Cohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 $±$ 0.048, XGB 0.909 $±$ 0.044 and RF 0.883 $±$ 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean AUCs were: MLR 0.805 $±$ 0.096, XGB 0.815 $±$ 0.081 and RF 0.793 $±$ 0.083 (k = 5 cross-validation) and MLR 0.736 (95% CI 0.734-0.737), XGB 0.799 (0.798-0.800), RF 0.781 (0.778-0.782) on bootstrapped resampling. On external validation (OUH cohort), AUCs were MLR 0.894, XGB 0.887 and RF 0.891 for the primary treatment model and MLR 0.711, XGB 0.742 and RF 0.730 for the palliative treatment classifier. Predicted survival probability from the palliative survival model was well calibrated over the first 12 months post-diagnosis in both cohorts. The RRI program provided a collaborative environment leading to valuable modifications to the CDSS including prediction explanations, visual aids for survival and integrated education for users producing a user-friendly and quick to use tool.ensuremath</pensuremath>ensuremath<pensuremath>INTERPRETATION: We present a novel, responsibly developed, externally validated AI CDSS trained to predict oesophageal cancer MDT decisions. It represents the foundations of a transformative application of ML, personalised, consistent and efficient MDT decision-support within OC which aligns to RRI principles.ensuremath</pensuremath>ensuremath<pensuremath>FUNDING: Doctoral Studentship for NT (Institute for Life Sciences (University of Southampton) & University Hospital Southampton), UKRI TAS Pump-Priming Grant (TAS_PP_00167).ensuremath</pensuremath>
2.
Thavanesan, Navamayooran; Farahi, Arya; Parfitt, Charlotte; Belkhatir, Zehor; Azim, Tayyaba; Vallejos, Elvira Perez; Walters, Zoë; Ramchurn, Sarvapali; Underwood, Timothy J.; Vigneswaran, Ganesh
Insights from explainable AI in oesophageal cancer team decisions Journal Article
In: Computers in Biology and Medicine, vol. 180, 2024, (For the purpose of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.).
@article{soton493238,
title = {Insights from explainable AI in oesophageal cancer team decisions},
author = {Navamayooran Thavanesan and Arya Farahi and Charlotte Parfitt and Zehor Belkhatir and Tayyaba Azim and Elvira Perez Vallejos and Zoë Walters and Sarvapali Ramchurn and Timothy J. Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/493238/},
year = {2024},
date = {2024-08-01},
journal = {Computers in Biology and Medicine},
volume = {180},
abstract = {ensuremath<pensuremath>Background: clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).ensuremath</pensuremath>ensuremath<pensuremath>Methods: retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.�ensuremath</pensuremath>ensuremath<pensuremath>Results: amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75?85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.�ensuremath</pensuremath>ensuremath<pensuremath>Conclusion: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.ensuremath</pensuremath>},
note = {For the purpose of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>Background: clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).ensuremath</pensuremath>ensuremath<pensuremath>Methods: retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.�ensuremath</pensuremath>ensuremath<pensuremath>Results: amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75?85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.�ensuremath</pensuremath>ensuremath<pensuremath>Conclusion: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.ensuremath</pensuremath>
Selected Publications
Multi-agent signal-less intersection management with dynamic platoon formation
No data available.
https://eprints.soton.ac.uk/478647/
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response
No data available.
https://eprints.soton.ac.uk/477553/
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The effect of data visualisation quality and task density on human-swarm interaction
No data available.
https://eprints.soton.ac.uk/479970/
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Demonstrating performance benefits of human-swarm teaming
Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.
https://eprints.soton.ac.uk/479903/
@inproceedings{soton479903,
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2025
Thavanesan, Navamayooran; Naiseh, Mohammad; Terol, Miguel; Rahman, Saqib Andrew; Hill, Samuel Luke; Parfitt, Charlotte; Walters, Zoë S; Ramchurn, Sarvapali; Markar, Sheraz; Owen, Richard; Maynard, Nick; Azim, Tayyaba; Belkatir, Zehor; Perez, Elvira Vallejos; McCord, Mimi; Underwood, Tim; Vigneswaran, Ganesh
The oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning clinical decision support system Journal Article
In: EClinicalMedicine, vol. 89, pp. 103527, 2025, (For the purposes of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.).
@article{soton506614,
title = {The oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning clinical decision support system},
author = {Navamayooran Thavanesan and Mohammad Naiseh and Miguel Terol and Saqib Andrew Rahman and Samuel Luke Hill and Charlotte Parfitt and Zoë S Walters and Sarvapali Ramchurn and Sheraz Markar and Richard Owen and Nick Maynard and Tayyaba Azim and Zehor Belkatir and Elvira Vallejos Perez and Mimi McCord and Tim Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/506614/},
year = {2025},
date = {2025-09-01},
journal = {EClinicalMedicine},
volume = {89},
pages = {103527},
abstract = {ensuremath<pensuremath>BACKGROUND: The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.ensuremath</pensuremath>ensuremath<pensuremath>METHODS: Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.ensuremath</pensuremath>ensuremath<pensuremath>FINDINGS: Cohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 $±$ 0.048, XGB 0.909 $±$ 0.044 and RF 0.883 $±$ 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean AUCs were: MLR 0.805 $±$ 0.096, XGB 0.815 $±$ 0.081 and RF 0.793 $±$ 0.083 (k = 5 cross-validation) and MLR 0.736 (95% CI 0.734-0.737), XGB 0.799 (0.798-0.800), RF 0.781 (0.778-0.782) on bootstrapped resampling. On external validation (OUH cohort), AUCs were MLR 0.894, XGB 0.887 and RF 0.891 for the primary treatment model and MLR 0.711, XGB 0.742 and RF 0.730 for the palliative treatment classifier. Predicted survival probability from the palliative survival model was well calibrated over the first 12 months post-diagnosis in both cohorts. The RRI program provided a collaborative environment leading to valuable modifications to the CDSS including prediction explanations, visual aids for survival and integrated education for users producing a user-friendly and quick to use tool.ensuremath</pensuremath>ensuremath<pensuremath>INTERPRETATION: We present a novel, responsibly developed, externally validated AI CDSS trained to predict oesophageal cancer MDT decisions. It represents the foundations of a transformative application of ML, personalised, consistent and efficient MDT decision-support within OC which aligns to RRI principles.ensuremath</pensuremath>ensuremath<pensuremath>FUNDING: Doctoral Studentship for NT (Institute for Life Sciences (University of Southampton) & University Hospital Southampton), UKRI TAS Pump-Priming Grant (TAS_PP_00167).ensuremath</pensuremath>},
note = {For the purposes of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>BACKGROUND: The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.ensuremath</pensuremath>ensuremath<pensuremath>METHODS: Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.ensuremath</pensuremath>ensuremath<pensuremath>FINDINGS: Cohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 $±$ 0.048, XGB 0.909 $±$ 0.044 and RF 0.883 $±$ 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean AUCs were: MLR 0.805 $±$ 0.096, XGB 0.815 $±$ 0.081 and RF 0.793 $±$ 0.083 (k = 5 cross-validation) and MLR 0.736 (95% CI 0.734-0.737), XGB 0.799 (0.798-0.800), RF 0.781 (0.778-0.782) on bootstrapped resampling. On external validation (OUH cohort), AUCs were MLR 0.894, XGB 0.887 and RF 0.891 for the primary treatment model and MLR 0.711, XGB 0.742 and RF 0.730 for the palliative treatment classifier. Predicted survival probability from the palliative survival model was well calibrated over the first 12 months post-diagnosis in both cohorts. The RRI program provided a collaborative environment leading to valuable modifications to the CDSS including prediction explanations, visual aids for survival and integrated education for users producing a user-friendly and quick to use tool.ensuremath</pensuremath>ensuremath<pensuremath>INTERPRETATION: We present a novel, responsibly developed, externally validated AI CDSS trained to predict oesophageal cancer MDT decisions. It represents the foundations of a transformative application of ML, personalised, consistent and efficient MDT decision-support within OC which aligns to RRI principles.ensuremath</pensuremath>ensuremath<pensuremath>FUNDING: Doctoral Studentship for NT (Institute for Life Sciences (University of Southampton) & University Hospital Southampton), UKRI TAS Pump-Priming Grant (TAS_PP_00167).ensuremath</pensuremath>
2024
Thavanesan, Navamayooran; Farahi, Arya; Parfitt, Charlotte; Belkhatir, Zehor; Azim, Tayyaba; Vallejos, Elvira Perez; Walters, Zoë; Ramchurn, Sarvapali; Underwood, Timothy J.; Vigneswaran, Ganesh
Insights from explainable AI in oesophageal cancer team decisions Journal Article
In: Computers in Biology and Medicine, vol. 180, 2024, (For the purpose of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.).
@article{soton493238,
title = {Insights from explainable AI in oesophageal cancer team decisions},
author = {Navamayooran Thavanesan and Arya Farahi and Charlotte Parfitt and Zehor Belkhatir and Tayyaba Azim and Elvira Perez Vallejos and Zoë Walters and Sarvapali Ramchurn and Timothy J. Underwood and Ganesh Vigneswaran},
url = {https://eprints.soton.ac.uk/493238/},
year = {2024},
date = {2024-08-01},
journal = {Computers in Biology and Medicine},
volume = {180},
abstract = {ensuremath<pensuremath>Background: clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).ensuremath</pensuremath>ensuremath<pensuremath>Methods: retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.�ensuremath</pensuremath>ensuremath<pensuremath>Results: amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75?85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.�ensuremath</pensuremath>ensuremath<pensuremath>Conclusion: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.ensuremath</pensuremath>},
note = {For the purpose of open access, the authors have applied a Creative Commons attribution license (CC-BY) to any Author Accepted Manuscript version arising from this submission.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ensuremath<pensuremath>Background: clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).ensuremath</pensuremath>ensuremath<pensuremath>Methods: retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.�ensuremath</pensuremath>ensuremath<pensuremath>Results: amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75?85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.�ensuremath</pensuremath>ensuremath<pensuremath>Conclusion: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.ensuremath</pensuremath>