Brachytherapy outcome modeling in cervical cancer patients: A predictive machine learning study on patient-specific clinical, physical and dosimetric parameters

医学 近距离放射治疗 接收机工作特性 逻辑回归 随机森林 支持向量机 机器学习 置信区间 宫颈癌 曲线下面积 核医学 人工智能 放射治疗 放射科 癌症 内科学 计算机科学
作者
Neda Abdalvand,Mahdi Sadeghi,Seied Rabi Mahdavi,Hamid Abdollahi,Younes Qasempour,Fatemeh Mohammadian,Mohammad Javad Tahmasebi Birgani,Khadijeh Hosseini
出处
期刊:Brachytherapy [Elsevier]
卷期号:21 (6): 769-782 被引量:6
标识
DOI:10.1016/j.brachy.2022.06.007
摘要

To predict clinical response in locally advanced cervical cancer (LACC) patients by a combination of measures, including clinical and brachytherapy parameters and several machine learning (ML) approaches.Brachytherapy features such as insertion approaches, source metrics, dosimetric, and clinical measures were used for modeling. Four different ML approaches, including LASSO, Ridge, support vector machine (SVM), and Random Forest (RF), were applied to extracted measures for model development alone or in combination. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristics curve, sensitivity, specificity, and accuracy. Our results were compared with a reference model developed by simple logistic regression applied to three distinct clinical features identified by previous papers.One hundred eleven LACC patients were included. Nine data sets were obtained based on the features, and 36 predictive models were built. In terms of AUC, the model developed using RF applied to dosimetric, physical, and total BT sessions features were found as the most predictive [AUC; 0.82 (0.95 confidence interval (CI); 0.79 -0.93), sensitivity; 0.79, specificity; 0.76, and accuracy; 0.77]. The AUC (0.95 CI), sensitivity, specificity, and accuracy for the reference model were found as 0.56 (0.52 ...0.68), 0.51, 0.51, and 0.48, respectively. Most RF models had significantly better performance than the reference model (Bonferroni corrected p-value < 0.0014).Brachytherapy response can be predicted using dosimetric and physical parameters extracted from treatment parameters. Machine learning algorithms, including Random Forest, could play a critical role in such predictive modeling.

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