Machine Learning in Prediction of Prostate Brachytherapy Rectal Dose Classes at Day 30

A retrospective analysis of brachytherapy implant data was carried out on 351 patients that underwent permanent I125 brachytherapy for treatment of low-risk prostate cancer. For each patient, the dose received by 2cm3 of the rectum (D2cc) 30 days post implant was defined as belonging one of two classes, ”Low” and ”High” depending on whether or not it was above or below a particular dose threshold. The aim of the study was to investigate the application of a number of machine learning classification techniques to intra-operative implant dosimetry data for prediction of rectal dose classes determined 30 days post implant. Algorithm performance was assessed in terms of its true and false positive rates and Receiver Operator Curve area based on a 10-fold cross validation procedure using Weka software. This was repeated for a variety of dose class thresholds to determine the point at which the highest accuracy was achieved. The highest ROC areas were observed at a threshold of D2cc = 90 Gy, with the highest area achieved by Bayes Net (0.943). At more clinically useful thresholds of D2cc = 145 Gy, classification was less reliable, with the highest ROC area achieved by Bayes Net (0.613).