Jobczyk Mateusz, Stawiski Konrad, Kaszkowiak Marcin, Rajwa Paweł, Różański Waldemar, Soria Francesco, Shariat Shahrokh F, Fendler Wojciech
Department of Urology, Copernicus Memorial Hospital, Medical University of Lodz, Lodz, Poland; Department of Urology, The Hospital Ministry of the Interior and Administration, Lodz, Poland.
Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland.
Eur Urol Oncol. 2022 Feb;5(1):109-112. doi: 10.1016/j.euo.2021.05.006. Epub 2021 Jun 3.
Despite being standard tools for decision-making, the European Organisation for Research and Treatment of Cancer (EORTC), European Association of Urology (EAU), and Club Urologico Espanol de Tratamiento Oncologico (CUETO) risk groups provide moderate performance in predicting recurrence-free survival (RFS) and progression-free survival (PFS) in non-muscle-invasive bladder cancer (NMIBC). In this retrospective combined-cohort data-mining study, the training group consisted of 3570 patients with de novo diagnosed NMIBC. Predictors included gender, age, T stage, histopathological grading, tumor burden and diameter, EORTC and CUETO scores, and type of intravesical treatment. The models developed were externally validated using an independent cohort of 322 patients. Models were trained using Cox proportional-hazards deep neural networks (deep learning; DeepSurv) with a proprietary grid search of hyperparameters. For patients treated with surgery and bacillus Calmette-Guérin-treated patients, the models achieved a c index of 0.650 (95% confidence interval [CI] 0.649-0.650) for RFS and 0.878 (95% CI 0.873-0.874) for PFS in the training group. In the validation group, the c index was 0.651 (95% CI 0.648-0.654) for RFS and 0.881 (95% CI 0.878-0.885) for PFS. After inclusion of patients treated with mitomycin C, the c index for RFS models was 0.6415 (95% CI 0.6412-0.6417) for the training group and 0.660 (95% CI 0.657-0.664) for the validation group. Models for PFS achieved a c index of 0.885 (95% CI 0.885-0.885) for the training set and 0.876 (95% CI 0.873-0.880) for the validation set. Our tool outperformed standard-of-care risk stratification tools and showed no evidence of overfitting. The application is open source and available at https://biostat.umed.pl/deepNMIBC/. PATIENT SUMMARY: We created and validated a new tool to predict recurrence and progression of early-stage bladder cancer. The application uses advanced artificial intelligence to combine state-of-the-art scales, outperforms these scales for prediction, and is freely available online.
尽管欧洲癌症研究与治疗组织（EORTC）、欧洲泌尿外科学会（EAU）和西班牙肿瘤泌尿治疗俱乐部（CUETO）风险分组是决策的标准工具，但在预测非肌层浸润性膀胱癌（NMIBC）的无复发生存期（RFS）和无进展生存期（PFS）方面，其表现一般。在这项回顾性联合队列数据挖掘研究中，训练组由3570例初诊NMIBC患者组成。预测因素包括性别、年龄、T分期、组织病理学分级、肿瘤负荷和直径、EORTC和CUETO评分以及膀胱内治疗类型。所开发的模型在一个由322例患者组成的独立队列中进行了外部验证。模型使用Cox比例风险深度神经网络（深度学习；DeepSurv）进行训练，并对超参数进行了专有网格搜索。对于接受手术治疗的患者和卡介苗治疗的患者，训练组中RFS模型的c指数为0.650（95%置信区间[CI]0.649 - 0.650），PFS模型的c指数为0.878（95%CI 0.873 - 0.874）。在验证组中，RFS模型的c指数为0.651（95%CI 0.648 - 0.654），PFS模型的c指数为0.881（95%CI 0.878 - 0.885）。纳入丝裂霉素C治疗的患者后，训练组RFS模型的c指数为0.6415（95%CI 0.6412 - 0.6417），验证组为0.660（95%CI 0.657 - 0.664）。PFS模型在训练集的c指数为0.885（95%CI 0.885 - 0.885），在验证集为0.876（95%CI 0.873 - 0.880）。我们的工具优于标准治疗风险分层工具，且无过度拟合的迹象。该应用程序是开源的，可在https://biostat.umed.pl/deepNMIBC/获取。患者总结：我们创建并验证了一种预测早期膀胱癌复发和进展的新工具。该应用程序使用先进的人工智能来整合最先进的量表，在预测方面优于这些量表，并且可在网上免费获取。
