A machine-learning–based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies

Purpose: External beam radiotherapy combined with interstitial brachytherapy is commonly used to treat patients with bulky, advanced gynecologic cancer. However, the high radiation dose needed to control the tumor may result in fistula development. There is a clinical need to identify patients at high risk for fistula formation such that treatment may be managed to prevent this toxic side effect. This work aims to develop a fistula prediction model framework using machine learning based on patient, tumor, and treatment features. Methods and Materials: This retrospective study included 35 patients treated at our institution using interstitial brachytherapy for various gynecological malignancies. Five patients developed rectovaginal fistula and two developed both rectovaginal and vesicovaginal fistula. For each patient, 31 clinical features of multiple data types were collected to develop a fistula prediction framework. A nonlinear support vector machine was used to build the prediction model. Sequential backward feature selection and sequential floating backward feature selection methods were used to determine optimal feature sets. To overcome data imbalance issues, the synthetic minority oversampling technique was used to generate synthetic fistula cases for model training. Results: Seven mixed data features were selected by both sequential backward selection and sequential floating backward selection methods. Our prediction model using these features achieved a high prediction accuracy, that is, 0.904 area under the curve, 97.1% sensitivity, and 88.5% specificity. Conclusions: A machine-learning–based prediction model of fistula formation has been developed for patients with advanced gynecological malignancies treated using interstitial brachytherapy. This model may be clinically impactful pending refinement and validation in a larger series.