Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Department of Dermatology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Int J Med Sci. 2020 May 30;17(10):1366-1374. doi: 10.7150/ijms.45730. eCollection 2020.
To explore the prediction value of PI-RADS v2 in high-grade prostate cancer and establish a prediction model combined with related variables of prostate cancer. A total of 316 patients with newly discovered prostate cancer at Zhongnan Hospital of Wuhan University and Renmin Hospital of Wuhan University from December 2017 to August 2019 were enrolled in this study. The clinic information as age, tPSA, fPSA, prostate volume, Gleason score and PI-RADS v2 score have been collected. Univariate analysis was performed based on every variable to investigate the risk factors of high-grade prostate cancer. ROC curves were generated for the risk factors to distinguish the cut-off points. Logistic regression analyses were used to investigate the independent risk factors of high-grade prostate cancer. Nomogram prediction model was generated based on multivariate logistic regression analysis. The calibration curve, ROC curve, leave-one-out cross validation and independent external validation were performed to evaluate the discriminative ability, accuracy and stability of the nomogram prediction model. Of 316 patients, a total of 187 patients were diagnosed as high-grade prostate cancer. Univariate analysis showed tPSA, fPSA, prostate volume, PSAD and PI-RADS v2 score were significantly different between the high- and low-grade prostate cancer patients. Univariate and multivariate logistic regression analyses showed only tPSA, prostate volume and PI-RADS v2 score were the independent risk factors of high-grade prostate cancer. The nomogram could predict the probability of high-grade prostate cancer, with a sensitivity of 79.4% and a specificity of 77.6%. The calibration curve displayed good agreement of the predicted probability with the actual observed probability. AUC of the ROC curve was 0.840 (0.797-0.884). Leave-one-out cross validation indicated the nomogram prediction model could classify 81.4% cases accurately. External data validation was performed with a sensitivity of 80.6% and a specificity of 77.3%, the Kappa value was 0.5755. PI-RADS v2 score had the value in predicting high-grade prostate cancer and the nomogram prediction model may help early diagnose the high risk prostate cancer.
探讨 PI-RADS v2 对高级别前列腺癌的预测价值，并建立联合前列腺癌相关变量的预测模型。
本研究纳入了 2017 年 12 月至 2019 年 8 月期间武汉大学中南医院和武汉大学人民医院新诊断为前列腺癌的 316 例患者。收集了患者的临床信息，包括年龄、tPSA、fPSA、前列腺体积、Gleason 评分和 PI-RADS v2 评分。基于每个变量进行单因素分析，探讨高级别前列腺癌的危险因素。绘制 ROC 曲线以确定危险因素的截断值。使用逻辑回归分析探讨高级别前列腺癌的独立危险因素。基于多因素逻辑回归分析生成列线图预测模型。通过校准曲线、ROC 曲线、内部留一交叉验证和独立外部验证评估列线图预测模型的区分能力、准确性和稳定性。
在 316 例患者中，共有 187 例患者被诊断为高级别前列腺癌。单因素分析显示，tPSA、fPSA、前列腺体积、PSAD 和 PI-RADS v2 评分在高级别和低级别前列腺癌患者之间有显著差异。单因素和多因素逻辑回归分析显示，只有 tPSA、前列腺体积和 PI-RADS v2 评分是高级别前列腺癌的独立危险因素。该列线图可以预测高级别前列腺癌的概率，灵敏度为 79.4%，特异性为 77.6%。校准曲线显示预测概率与实际观察概率具有良好的一致性。ROC 曲线的 AUC 为 0.840（0.797-0.884）。内部留一交叉验证表明，列线图预测模型可以准确分类 81.4%的病例。外部数据验证的灵敏度为 80.6%，特异性为 77.3%，Kappa 值为 0.5755。PI-RADS v2 评分对预测高级别前列腺癌有一定价值，列线图预测模型有助于早期诊断高危前列腺癌。
