عنوان انگلیسی مقاله:
A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies
ترجمه فارسی عنوان مقاله:
یک مدل پیش بینی مبتنی بر یادگیری ماشینی از تشکیل فیستول پس از براکی تراپی بینابینی برای بدخیمی های ژنتیکی بومی محلی
Sciencedirect - Elsevier - Brachytherapy, 18 (2019) 530-538: doi:10:1016/j:brachy:2019:04:004
Zhen Tian1,2, Allen Yen1, Zhiguo Zhou1, Chenyang Shen1, Kevin Albuquerque1, Brian Hrycushko1
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-learningebased 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.
Keywords: Machine learning | Support vector machine | Interstitial brachytherapy | Gynecologic cancer