Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer

Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to address inconsistency and standardize care. The aim of this experime...

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Bibliographic Details
Published in:European journal of surgical oncology 2023-11, Vol.49 (11), p.106986-106986, Article 106986
Main Authors: Thavanesan, Navamayooran, Bodala, Indu, Walters, Zoë, Ramchurn, Sarvapali, Underwood, Timothy J, Vigneswaran, Ganesh
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Artificial Intelligence
Esophageal Neoplasms - therapy
Health Status Disparities
Humans
Machine Learning
Middle Aged
Patient Care Team
Pilot Projects
Retrospective Studies
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Summary:Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to address inconsistency and standardize care. The aim of this experimental pilot study was to develop ML models able to predict curative OC MDT treatment decisions and determine the relative importance of underlying decision-critical variables. Retrospective complete-case analysis of oesophagectomy patients ± neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) between 2010 and 2020. Established ML algorithms (Multinomial Logistic regression (MLR), Random Forests (RF), Extreme Gradient Boosting (XGB)) and Decision Tree (DT) were used to train models predicting OC MDT treatment decisions: surgery (S), NACT + S or NACRT + S. Performance metrics included Area Under the Curve (AUC), Accuracy, Kappa, LogLoss, F1 and Precision -Recall AUC. Variable importance was calculated for each model. We identified 399 cases with a male-to-female ratio of 3.6:1 and median age of 66.1yrs (range 32-83). MLR outperformed RF, XGB and DT across performance metrics (mean AUC of 0.793 [±0.045] vs 0.757 [±0.068], 0.740 [±0.042], and 0.709 [±0.021] respectively). Variable importance analysis identified age as a major factor in the decision to offer surgery alone or NACT + S across models (p 
ISSN:0748-7983
1532-2157
DOI:10.1016/j.ejso.2023.106986