开发和验证预测模型，用于在 PI-RADS 类别 ≥ 4 类病变且 PSA ≤ 20 ng/ml 的未进行活检的患者中识别非前列腺癌 (non-PCa)，以避免不必要的活检。 2018年至2022年期间的华西医院纳入其中。患者被随机分为训练队列（70%）和验证队列（30%）。使用逻辑回归筛选非 PCa 的独立预测因素，并根据回归系数构建列线图。区分度和校准分别通过 C 指数和校准图进行评估。应用决策曲线分析（DCA）和临床影响曲线（CIC）来衡量临床净效益。总共纳入了 1580 名患者，其中 634 名非 PCa 患者。年龄、前列腺体积、前列腺特异性抗原密度（PSAD）、表观扩散系数（ADC）和病变区域作为独立预测因子纳入最佳预测模型，并构建了相应的列线图（https://nomogramscu.shinyapps.io/ PI-RADS-4-5/）。该模型在验证队列中的 C 指数为 0.931（95% CI，0.910-0.953）。 DCA 和 CIC 在广泛的阈值概率范围内表现出增加的净收益。在 60%、70% 和 80% 的免活检阈值下，列线图能够避免 74.0%、65.8% 和 55.6% 的不必要活检，而漏诊 PCa 的比例为 9.0%、5.0% 和 3.6%（或 35.9%）。分别占放弃活检的 %、30.2% 和 25.1%）。开发的列线图具有良好的预测能力和临床实用性，可以帮助识别非 PCa，支持临床决策并减少不必要的前列腺活检。© 2024。作者，获得 Springer-Verlag GmbH 德国（Springer Nature 旗下公司）的独家许可。

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.© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.