目的：通过综合措施预测局部晚期宫颈癌（LACC）患者的临床反应，包括临床和近距离放射治疗参数以及几种机器学习(ML)方法。
方法：近距离放射治疗的特征，如插入方法，源度量，剂量测定,和临床措施用于建模。四种不同的机器学习方法，包括LASSO,里奇,支持向量机(SVM)，和随机森林(RF)，单独或组合应用于模型开发的提取度量。使用接收器工作特性曲线的曲线下面积（AUC）评估模型性能，灵敏度,特异性，和准确性。我们的结果与通过简单逻辑回归开发的参考模型进行了比较，该模型应用于先前论文确定的三个不同的临床特征。
结果：纳入了111例LACC患者。根据这些特征获得了9个数据集，并建立了36个预测模型。就AUC而言，使用RF开发的模型应用于剂量测定，物理，和总BT会话特征被发现是最具预测性的[AUC；0.82(0.95置信区间(CI)；0.79-0.93)，灵敏度；0.79，特异性；0.76，准确性；0.77]。AUC（0.95CI），灵敏度,特异性，参考模型的准确性为0.56(0.52。..0.68），分别为0.51、0.51和0.48。大多数RF模型的性能明显优于参考模型（Bonferroni校正p值<0.0014）。
结论：可以使用从治疗参数中提取的剂量学和物理参数来预测近距离放射治疗反应。机器学习算法，包括随机森林，可以在这种预测建模中发挥关键作用。
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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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