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

Purpose To analyse the classification performances of a decision tree method applied to predictor variables in survival outcome in patients with locally advanced rectal cancer (LARC). The aim was to offer a critical analysis to better apply tree-based approach in clinical practice and improve its interpretation. Materials and methods Data concerning patients with histological proven LARC between 2007 and 2014 were reviewed. All patients were treated with trimodality approach with a curative intent. The Kaplan–Meier method was used to estimate overall survival (OS). Decision tree methods were was used to select important variables in outcome prediction. Results A total of 100 patients were included. The 5-year and 7-year OS rates were 76.4% and 71.3%, respectively. Age, co-morbidities, tumor size, clinical tumor classification (cT) and clinical nodes classification (cN) were the important predictor variables to the tree’s construction. Overall, 13 distinct groups of patients were defined. Patients aged