Decision tree algorithm in locally advanced rectal cancer: an example of over-interpretation and misuse of a machine learning approach.

We proposed a decision tree algorithm to identify known and new pre-treatment clinical predictors of survival in LARC. Our analysis confirmed that tree-based machine learning method, especially classification trees, can be easily interpreted even by a non-expert in the field, but controlling cross validation errors is mandatory to capture its statistical power. However, it is necessary to carefully analyze the classification error trend to chose the important predictor variables, especially in little data. Machine learning approach should be considered the new unexplored frontier in LARC. Based on big datasets, decision trees represent an opportunity to improve decision-making process in clinical practice.