Prediction of Response to Neoadjuvant Chemoradiotherapy with Machine Learning in Rectal Cancer: A Pilot Study

OBJECTIVE In locally advanced rectal cancer, trimodality therapy comprising chemoradiotherapy, total mesorectal excision, and chemotherapy (CT) are accepted as standard treatment. However, standard "one-size-fits-all" therapy based on the TNM staging system may not be suitable for every patient. In cases with a good response, less invasive surgical treatments, such as sphincter-sparing local excision or the watch-and-wait approach may be more appropriate due to their lower recurrence rates. Therefore, it is very impor-tant to predict these cases and plan treatment accordingly to ensure effective personalized treatment. Machine learning can successfully predict these cases. Aim: The aim of the study was to predict the response to neoadjuvant chemoradiotherapy with machine learning in locally advanced rectal cancer. METHODS The study included 125 rectal cancer cases who underwent neoadjuvant radiotherapy (RT)+/- CT between 2010 and 2020, and the cases with a good response (grade 0-1) according to the Modified Ryan classifi-cation were predicted using machine learning. A total of 26 variables were evaluated. After determining key variables, the dataset was divided into training/test sets at 80%/20%. Logistic regression, artificial neural network-multilayer perceptron classifier, XGBoost, support vector classification, random for -est, and Gaussian Naive Bayes algorithms used to establish a prediction model. In the prediction of the group with a good response, 173 cases were created and evaluated with the synthetic minority oversam-pling technique method. RESULTS Of the 125 cases, 15 had a complete response and 33 had a good response (Modified Ryan grades 0 and 1). Six algorithms were tested in terms of their ability to predict a good response. Key variables for this prediction were found to be tumor localization, RT break time, age, gender, Karnofsky Performance Scale score, body mass index, pre-and post-treatment carcinoembryonic antigen levels, pre-treatment hemoglobin and neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio, radiological T and N stages, perineural and lymphatic invasion, tumor grade, radiological metastatic lymph node region, RT dose and technique, and presence and scheme of concurrent CT. The algorithm that showed the best performance was determined as logistic regression with an accuracy rate of 84% (CI: 0.69-0.98), sensi-tivity of 83%, and specificity of 85%. CONCLUSION It is very important to predict the cases with a good response and plan treatment accordingly to ensure effective personalized treatment. Machine learning can successfully predict these cases.