Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
World J Urol. 2024 Aug 23;42(1):495. doi: 10.1007/s00345-024-05202-y.
To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy.
Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit.
A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively).
The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.
开发和验证一种预测模型，用于识别前列腺特异性抗原（PSA）≤20ng/ml 且 PI-RADS 分类≥4 级的活检初筛阴性患者中的非前列腺癌（非 PCa），以避免不必要的活检。
纳入 2018 年至 2022 年在华西医院接受经会阴前列腺活检的患者。将患者随机分为训练队列（70%）和验证队列（30%）。使用逻辑回归筛选非 PCa 的独立预测因子，并根据回归系数构建列线图。通过 C 指数和校准图评估判别和校准。决策曲线分析（DCA）和临床影响曲线（CIC）用于衡量临床净获益。
共纳入 1580 例患者，其中 634 例为非 PCa。年龄、前列腺体积、前列腺特异抗原密度（PSAD）、表观扩散系数（ADC）和病变区是纳入最佳预测模型的独立预测因子，并构建了相应的列线图（https://nomogramscu.shinyapps.io/PI-RADS-4-5/）。模型在验证队列中获得 0.931（95%CI，0.910-0.953）的 C 指数。DCA 和 CIC 表明，在广泛的阈值概率范围内，净获益增加。在无活检阈值为 60%、70%和 80%时，列线图可避免 74.0%、65.8%和 55.6%的不必要活检，而漏诊 PCa 率为 9.0%、5.0%和 3.6%（或分别为 35.9%、30.2%和 25.1%的错失活检）。
所开发的列线图具有良好的预测能力和临床实用性，有助于识别非 PCa，为临床决策提供支持，并减少不必要的前列腺活检。
