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.