Support Vector Machine Model Predicts Dose for Organs at Risk in High-Dose Rate Brachytherapy of Cervical Cancer - Zhou, Ping; Li, Xiaojie; Zhou, Hao; Fu, Xiao; Liu, Bo; Zhang, Yu; Lin, Sheng; Pang, Haowen (Frontiers in Oncology, 2021)
Introduction: This study aimed to establish a support vector machine (SVM) model to predict the dose for organs at risk (OARs) in intracavitary brachytherapy planning for cervical cancer with tandem and ovoid treatments. Methods: Fifty patients with loco-regionally advanced cervical cancer treated with 200 CT-based tandem and ovoid brachytherapy plans were included. The brachytherapy plans were randomly divided into the training (N = 160) and verification groups (N = 40). The bladder, rectum, sigmoid colon, and small intestine were divided into sub-OARs. The SVM model was established using MATLAB software based on the sub-OAR volume to predict the bladder, rectum, sigmoid colon, and small intestine D2cm3. Model performance was quantified by mean squared error (MSE) and δ (δ=|D2cm3/Dprescription(actual)–D2cm3/Dprescription(predicted)|). The goodness of fit of the model was quantified by the coefficient of determination (R2). The accuracy and validity of the SVM model were verified using the validation group. Results: The D2cm3 value of the bladder, rectum, sigmoid colon, and small intestine correlated with the volume of the corresponding sub-OARs in the training group. The mean squared error (MSE) in the SVM model training group was <0.05; the R2 of each OAR was >0.9. There was no significant difference between the D2cm3 -predicted and actual values in the validation group (all P > 0.05): bladder δ = 0.024 ± 0.022, rectum δ = 0.026 ± 0.014, sigmoid colon δ = 0.035 ± 0.023 and small intestine δ = 0.032 ± 0.025. Conclusion: The SVM model established in this study can effectively predict the D2cm3 for the bladder, rectum, sigmoid colon, and small intestine in cervical cancer brachytherapy.