Sargos Paul, Leduc Nicolas, Giraud Nicolas, Gandaglia Giorgio, Roumiguié Mathieu, Ploussard Guillaume, Rozet Francois, Soulié Michel, Mathieu Romain, Artus Pierre Mongiat, Niazi Tamim, Vinh-Hung Vincent, Beauval Jean-Baptiste
Department of Radiation Oncology, Institut Bergonié, Bordeaux, France.
Division of Radiation Oncology, Department of Oncology, McGill University, Montreal, QC, Canada.
Front Oncol. 2021 Feb 11;10:607923. doi: 10.3389/fonc.2020.607923. eCollection 2020.
Use 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.
A 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.
Using 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.
Our results showed that the DNN has the potential to predict the 3-year BCR and outperformed the CAPRA score and other predictive models.
预测模型用于预测前列腺癌（PCa）的生化复发（BCR）正受到越来越多的关注。具体而言，约20%-40%的患者在根治性前列腺切除术（RP）后五年会发生BCR，而预测BCR的能力可能有助于临床医生做出更好的治疗决策。我们旨在研究CAPRA评分与其他模型相比在预测PCa患者3年BCR方面的准确性。
对总共5043例行RP的男性患者进行回顾性分析。比较了CAPRA评分、Cox回归分析、逻辑回归、K近邻（KNN）、随机森林（RF）和密集连接前馈神经网络（DNN）分类器在3年BCR预测值方面的准确性。主要使用受试者工作特征曲线下面积来评估预测模型在预测PCa患者3年BCR方面的性能。多变量分析纳入了术前数据，如前列腺特异性抗原（PSA）水平、Gleason分级和T分期。为了衡量由于额外数据对模型性能的潜在改善，每个模型都使用一组来自最终病理的术后手术数据再次进行训练。
使用CAPRA评分变量时，DNN预测模型的曲线下面积（AUC）值最高，为0.7，而CAPRA评分、逻辑回归、KNN、RF和Cox回归的AUC值分别为0.63、0.63、0.55、0.64和0.64。在模型中纳入术后变量后，基于KNN、RF、Cox回归和DNN的AUC值分别提高到0.77、0.74、0.75和0.84。
我们的结果表明，DNN有潜力预测3年BCR，并且优于CAPRA评分和其他预测模型。
