Predicting Outcomes of Pelvic Exenteration Using Machine Learning.

AIM We aim to compare machine learning (ML) with neural network performance in predicting R0 resection (R0), length of stay >14 days (LOS), major complication rates at 30 days post-operatively (COMP) and survival greater than one year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. METHOD A deep learning computer was built, and programming environment established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic Regression (LR), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were trained. 20% of the data was used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting Receiver Operating Characteristic (ROC) Curves and calculating the Area Under ROC (AUROC). RESULTS ML models and ANNs were trained on 1,147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting length of stay >14 days with an AUROC of 0.793 using preoperative and operative data. Visualised LR Model weights indicate varying impact of variables on the outcome in question. CONCLUSION This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods.