风险计算器 (RC) 通过临床/人口统计信息改善前列腺活检的患者选择，最近使用前列腺成像报告和数据系统 (PI-RADS) 进行前列腺 MRI。全自动深度学习 (DL) 可独立分析 MRI 数据，并已被证明与临床放射科医生相当，但尚未纳入 RC。本研究的目标是重新评估 RC 的诊断质量、用 DL 预测取代 PI-RADS 的影响，以及在 PI-RADS 之外添加 DL 的潜在性能增益。 自 2014 年起进行了 1,627 次连续检查这项回顾性单中心研究纳入了截至 2021 年的 517 项考试，其中 517 项考试被保留用于 RC 测试。委员会认证的放射科医生在临床常规过程中评估 PI-RADS，然后系统的 MRI/超声融合活检提供重要前列腺癌 (sPC) 的组织病理学基本事实。基于 nnUNet 的 DL 集成在双参数 MRI 上进行训练，预测 sPC 病变的存在（UNet 概率）和 PI-RADS 类似五点量表（UNet-Likert）。先前发布的 RC 已按原样进行验证； PI-RADS 被 UNet-Likert 取代（UNet-Likert 取代 RC）；以及 UNet 概率和 PI-RADS（UNet 概率扩展 RC）。与使用临床数据、PI-RADS 和 UNet 概率的新安装 RC 一起，通过接收者操作特征、校准和决策曲线分析对现有 RC 进行比较。 UNet-Likert 替代 RC 的诊断性能保持稳定。 DL 包含 PI-RADS 的补充诊断信息。新安装的 RC 避免了 49% [252/517] 的活检，同时保持阴性预测值 (94%)，而 PI-RADS ≥ 4 截止值则避免了 37% [190/517] (p < 0.001)将 DL 作为 RC 的独立诊断标志物可以改善活检前的患者分层，因为 DL 特征和临床 PI-RADS 评估有补充信息。对于前列腺筛查结果呈阳性的患者，进行全面的诊断检查，包括前列腺 MRI、DL分析和使用列线图进行个体分类可以识别前列腺癌风险最小的患者，因为他们从更具侵入性的活检程序中获益较少。当前基于 MRI 的列线图导致许多前列腺活检呈阴性。将 DL 添加到包含临床数据和 PI-RADS 的列线图中可改善活检前的患者分层。全自动深度学习可以替代 PI-RADS，而不会牺牲列线图预测的质量。前列腺列线图显示的癌症检测能力与之前的验证研究相当，同时适合添加 DL 分析。© 2024。作者。

Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS.One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis.Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49% [252/517] of biopsies while maintaining the negative predictive value (94%), compared to PI-RADS ≥ 4 cut-off which spared 37% [190/517] (p < 0.001).Incorporating DL as an independent diagnostic marker for RCs can improve patient stratification before biopsy, as there is complementary information in DL features and clinical PI-RADS assessment.For patients with positive prostate screening results, a comprehensive diagnostic workup, including prostate MRI, DL analysis, and individual classification using nomograms can identify patients with minimal prostate cancer risk, as they benefit less from the more invasive biopsy procedure.The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis.© 2024. The Author(s).