基于多模态特征和多分类器融合的前列腺癌放疗中直肠并发症预测模型

目的为了评估前列腺癌放疗中直肠并发症的预后，提出一种新型的基于多模态特征及多分类器融合的预测模型。方法 本研究回顾性收集了44例接受外照射放疗的前列腺癌患者的临床数据，从中提取临床参数特征和剂量学特征两种不同模态特 征，并利用筛选后的特征子集分别对五种基分类器（向量机、决策树、K近邻、随机森林和XGBoost）进行训练得到不同模态下的 多个基分类器，然后采用一种新型的基于多准则决策的权重分配算法依次对同一模态下多个基分类器以及不同模态信息的模 型进行融合，最终实现基于多模态特征及多分类器融合的预测模型。本研究采用五折交叉验证方法和ROC曲线下所围面积 （AUC）、准确率、灵敏度和特异性四种评价指标来定量评价所提出的预测模型。此外，本研究还将所提出模型与不同特征选择 方法、不同的权重分配算法、基于单模态单分类器的模型，以及两种使用其他融合方法的集成模型进行定量比较。结果五折交 叉验证结果显示本研究所提出的模型的平均准确率、AUC、特异性、灵敏度分别为：0.78、0.83、0.79、0.76。结论与基于单模态单 分类器的模型以及其他融合模型相比，本文所提出的基于多模态特征及多分类器融合的模型能更准确地预测前列腺癌放疗中 的直肠并发症。

Prediction of rectal toxicity of radiotherapy for prostate cancer based on multi-modality feature and multi-classifiers

Objective To evaluate rectal toxicity of radiotherapy for prostate cancer using a novel predictive model based on multi-modality and multi-classifier fusion. Methods We retrospectively collected the clinical data from 44 prostate cancer patients receiving external beam radiation (EBRT), including the treatment data, clinical parameters, planning CT data and the treatment plans. The clinical parameter features and dosimetric features were extracted as two different modality features, and a subset of features was selected to train the 5 base classifiers (SVM, Decision Tree, K-nearest-neighbor, Random forests and XGBoost). To establish the multi-modality and multi-classifier fusion model, a multi-criteria decision-making based weight assignment algorithm was used to assign weights for each base classifier under the same modality. A repeat 5-fold cross-validation and the 4 indexes including the area under ROC curve (AUC), accuracy, sensitivity and specificity were used to evaluate the proposed model. In addition, the proposed model was compared quantitatively with different feature selection methods, different weight allocation algorithms, the model based on single mode single classifier, and two integrated models using other fusion methods. Results Repeated (5 times) 5-fold cross validation of the proposed model showed an accuracy of 0.78 for distinguishing toxicity from non-toxicity with an AUC of 0.83, a specificity of 0.79 and a sensitivity of 0.76. Conclusion Compared with the models based on a single mode or a single classifier and other fusion models, the proposed model can more accurately predict rectal toxicity of radiotherapy for prostate cancer.