Development and Cross-Institutional Validation of a Comprehensive Machine Learning Model Predicting Response to Neoadjuvant Therapy for Rectal Cancer

Abstract

Sha Li,1,2 Zhengxian Li,3 Shuai Li,2 Ping Jiang,4 Hongbin Han,1 Yibao Zhang,2 Yanye Lu1,5 1Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, People’s Republic of China; 2Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education / Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing Cancer Hospital & Institute, Beijing, People’s Republic of China; 3Department of Radiotherapy, Guowen Hospital, Jilin, People’s Republic of China; 4Department of Radiation Oncology, Peking University Third Hospital, Beijing, People’s Republic of China; 5National Biomedical Imaging Center, Peking University, Beijing, People’s Republic of ChinaCorrespondence: Yibao Zhang, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education / Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing Cancer Hospital & Institute, Beijing, People’s Republic of China, Email [email protected] Yanye Lu, Email [email protected]: Accurately identifying patients achieving pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC) not only ensures treatment efficacy but also helps avoid surgical risks. We developed a comprehensive multi-omics model to predict pCR before surgery.Methods: Clinical data, CT, MRI-T1WI and MRI-T2WI, and radiotherapy dose were collected from 183 LARC patients who underwent preoperative nCRT. Backward stepwise selection, logistic regression, and five-fold cross-validation were employed for the development and validation of a non-imaging model, three radiomics-based models and a dosiomics-based model. These were integrated into a final model, and its performance was tested on multi-center sets.Results: C_model, based on clinical characteristics, achieved an AUC of 0.85 in the validation set. Radiomics models (CT_model, T1_model, T2_model) exhibited AUCs of 0.66, 0.67, and 0.64, respectively. Dosiomics-based model, D_model, achieved an AUC of 0.75 in validation. The mean AUCs for F_model in the training sets, validation sets, internal and external test sets were 0.90, 0.88, 0.77, and 0.74, respectively.Conclusion: To assess the efficacy of nCRT in LARC patients, it is crucial to consider clinical characteristics, followed by dosiomics. While T1_model, T2_model and CT_model demonstrate relatively comparable performance, each contributes unique value to the final prediction model.Keywords: radiomics, dosiomics, nCRT, rectal cancer, predict therapy response

Keywords

• Radiomics
• Dosiomics
• nCRT
• Rectal cancer
• Predict therapy response