Machine Learning of Dose-Volume Histogram Parameters Predicting Overall Survival in Patients with Cervical Cancer Treated with Definitive Radiotherapy.

Purpose: To analyze the effects of dosimetric parameters and clinical characteristics on overall survival (OS) by machine learning algorithms. Methods and Materials: 128 patients with cervical cancer were treated with definitive pelvic radiotherapy with or without chemotherapy followed by image-guided brachytherapy. The elastic-net models with integrating DVH parameters and baseline clinical factors, only DVH parameters and only baseline clinical factors were constructed in 5-folds cross-validations for 100 iteration bootstrapping, and then were compared using concordance index (C-index) criteria. Finally, the selected important factors were used to build multivariable Cox-pH models for OS and also shown in nomograms for clinical usage. Results: The median OS occurred was 25.78 months with 25 (19.53%) deaths. The elastic-net models integrating clinical and DVH factors had the best prediction performances (C-index 0.76 in the train set and C-index 0.74 in the test set). Three important factors were selected, including baseline hemoglobin level as the protective factor, primary tumor volume (GTV_P) volume, and body V5 as the risk factors. The final multivariable Cox-pH models were constructed using these important factors and had prediction performance (C-index: 0.78, 95%CI: 0.73-0.81). Conclusions: This is the first attempt to establish elastic-net models to study the contributions of DVH parameters for predicting OS in patients with cervical cancer. These results can facilitate individualized tailoring of radiation treatment in cervical cancer patients.