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

Abstract

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.