DataSheet_1_Deep Neural Networks Outperform the CAPRA Score in Predicting Biochemical Recurrence After Prostatectomy.docx

BackgroundUse of predictive models for the prediction of biochemical recurrence (BCR) is gaining attention for prostate cancer (PCa). Specifically, BCR occurs in approximately 20–40% of patients five years after radical prostatectomy (RP) and the ability to predict BCR may help clinicians to make better treatment decisions. We aim to investigate the accuracy of CAPRA score compared to others models in predicting the 3-year BCR of PCa patients.Material and MethodsA total of 5043 men who underwent RP were analyzed retrospectively. The accuracy of CAPRA score, Cox regression analysis, logistic regression, K-nearest neighbor (KNN), random forest (RF) and a densely connected feed-forward neural network (DNN) classifier were compared in terms of 3-year BCR predictive value. The area under the receiver operating characteristic curve was mainly used to assess the performance of the predictive models in predicting the 3 years BCR of PCa patients. Pre-operative data such as PSA level, Gleason grade, and T stage were included in the multivariate analysis. To measure potential improvements to the model performance due to additional data, each model was trained once more with an additional set of post-operative surgical data from definitive pathology.ResultsUsing the CAPRA score variables, DNN predictive model showed the highest AUC value of 0.7 comparing to the CAPRA score, logistic regression, KNN, RF, and cox regression with 0.63, 0.63, 0.55, 0.64, and 0.64, respectively. After including the post-operative variables to the model, the AUC values based on KNN, RF, and cox regression and DNN were improved to 0.77, 0.74, 0.75, and 0.84, respectively.ConclusionsOur results showed that the DNN has the potential to predict the 3-year BCR and outperformed the CAPRA score and other predictive models.

背景：预测生化复发（BCR）的预测模型在前列腺癌（PCa）的治疗领域中日益受到关注。具体而言，BCR在根治性前列腺切除术（RP）后的五年内大约发生在20%至40%的患者中，而预测BCR的能力有助于临床医生做出更优的治疗决策。本研究旨在探究CAPRA评分相较于其他模型在预测PCa患者三年BCR准确率方面的表现。材料与方法：对接受了RP手术的5043名男性患者进行了回顾性分析。CAPRA评分、Cox回归分析、逻辑回归、K最近邻（KNN）、随机森林（RF）以及密集连接的前馈神经网络（DNN）分类器的三年BCR预测值进行了比较。主要使用受试者工作特征曲线下的面积来评估预测模型在预测PCa患者三年BCR方面的性能。多变量分析中包含了手术前的数据，如PSA水平、Gleason分级和T分期。为了衡量额外数据对模型性能的潜在提升，每个模型均使用来自最终病理学的术后手术数据集再次进行了一次训练。结果：使用CAPRA评分变量，DNN预测模型相较于CAPRA评分、逻辑回归、KNN、RF和Cox回归分别显示出最高的AUC值，分别为0.7、0.63、0.63、0.55和0.64。将术后变量纳入模型后，基于KNN、RF、Cox回归和DNN的AUC值分别提高至0.77、0.74、0.75和0.84。结论：我们的研究结果揭示了DNN在预测三年BCR方面具有潜在能力，并优于CAPRA评分及其他预测模型。