Department of Urology, Gifu University Graduate School of Medicine, Gifu, Japan.
Department of Urology, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Ann Surg Oncol. 2023 Oct;30(11):6925-6933. doi: 10.1245/s10434-023-13747-2. Epub 2023 Jun 20.
We created a clinically applicable nomogram to predict locally advanced prostate cancer using preoperative parameters and performed external validation using an external independent validation cohort.
From a retrospective multicenter cohort study of 3622 Japanese patients with prostate cancer who underwent robot-assisted radical prostatectomy at ten institutions, the patients were divided into two groups (MSUG cohort and validation cohort). Locally advanced prostate cancer was defined as pathological T stage ≥ 3a. A multivariable logistic regression model was used to identify factors strongly associated with locally advanced prostate cancer. Bootstrap area under the curve was calculated to assess the internal validity of the prediction model. A nomogram was created as a practical application of the prediction model, and a web application was released to predict the probability of locally advanced prostate cancer.
A total of 2530 and 427 patients in the MSUG and validation cohorts, respectively, met the criteria for this study. On multivariable analysis, initial prostate-specific antigen, prostate volume, number of cancer-positive and cancer-negative biopsy cores, biopsy grade group, and clinical T stage were independent predictors of locally advanced prostate cancer. The nomogram predicting locally advanced prostate cancer was demonstrated (area under the curve 0.72). Using a nomogram cutoff of 0.26, 464 of 1162 patients (39.9%) could be correctly diagnosed with pT3, and 2311 of 2524 patients (91.6%) could avoid underdiagnosis.
We developed a clinically applicable nomogram with external validation to predict the probability of locally advanced prostate cancer in patients undergoing robot-assisted radical prostatectomy.
我们创建了一个临床适用的列线图,用于预测使用术前参数的局部晚期前列腺癌,并使用外部独立验证队列进行外部验证。
从十个机构的 3622 名接受机器人辅助根治性前列腺切除术的日本前列腺癌患者的回顾性多中心队列研究中,将患者分为两组(MSUG 队列和验证队列)。局部晚期前列腺癌定义为病理 T 分期≥3a。使用多变量逻辑回归模型确定与局部晚期前列腺癌强烈相关的因素。Bootstrap 曲线下面积用于评估预测模型的内部有效性。创建了一个列线图作为预测模型的实际应用,并发布了一个网络应用程序来预测局部晚期前列腺癌的概率。
MSUG 和验证队列中分别有 2530 名和 427 名患者符合本研究标准。多变量分析显示,初始前列腺特异性抗原、前列腺体积、癌阳性和癌阴性活检核心数、活检分级组和临床 T 分期是局部晚期前列腺癌的独立预测因素。预测局部晚期前列腺癌的列线图得到了验证(曲线下面积为 0.72)。使用列线图截断值 0.26,可正确诊断 1162 名患者中的 464 名(39.9%)为 pT3,可避免漏诊 2524 名患者中的 2311 名(91.6%)。
我们开发了一个具有外部验证的临床适用的列线图,用于预测接受机器人辅助根治性前列腺切除术的患者中局部晚期前列腺癌的概率。