对疑似前列腺癌 (PCa) 但 MRI (PI-RADS ≤ 3) 不确定的男性进行诊断评估是一个常见的临床挑战。 [68Ga]Ga标记的前列腺特异性膜抗原([68Ga]Ga-PSMA)正电子发射断层扫描/计算机断层扫描(PET/CT)在识别具有临床意义的前列腺癌(csPCa)方面显示出了前景。我们的目标是建立一个结合 PSMA-PET 的诊断模型，以增强 PI-RADS ≤ 3 男性中 csPCa 的诊断过程。这项回顾性研究包括 151 名临床怀疑患有 PCa 且 PI-RADS ≤ 3 MRI 的男性。所有男性均接受了 [68Ga]Ga-PSMA PET/CT 扫描和超声/MRI/PET 融合引导活检。 csPCa 被定义为等级组 ≥ 2。评估 PSMA-PET 扫描的主要分数。开发了一种结合 PSMA-PET 和前列腺特异性抗原 (PSA) 衍生参数的诊断模型。与传统方法比较了判别性能和临床实用性。使用五重交叉验证进行 1000 次迭代进行内部验证。在这个 PI-RADS ≤ 3 队列中，检测 csPCa 的曲线下面积 (AUC) 为 0.796 (95% CI, 0.738-0.853)、0.851 (主要评分、SUVmax 和常规临床 PSMA-PET 评估分别为 95%CI，0.783-0.918）和 0.806（95%CI，0.742-0.870）。与基于 PRIMARY 评分或 SUVmax 的策略相比，包含 PRIMARY 评分、SUVmax 和血清游离 PSA/总 PSA (fPSA/tPSA) 的诊断模型获得了显着更高的 AUC 0.906 (95% CI, 0.851-0.961) (P < 0.05），并且明显优于通常基于 PSA 密度的传统策略（P < 0.001）。 1000 次迭代的平均五重交叉验证 AUC 为 0.878（95% CI，0.820-0.954）。理论上，使用 21.6% 的阈值，该模型可以防止 78% 的不必要的活检，同时仅漏掉该队列中 7.8% 的 csPCa 病例。一种新颖的诊断模型，结合了 PSMA-PET 衍生指标（主要评分和 SUVmax）以及血清 fPSA/tPSA 已得到开发和验证。集成模型可以帮助临床决策，并比单个传统指标提高诊断准确性。它具有巨大的潜力，可以减少 PI-RADS ≤ 3 MRI 结果的男性不必要的活检，并值得进一步的前瞻性和外部评估。© 2024。作者获得 Springer-Verlag GmbH 德国（Springer Nature 旗下公司）的独家许可。

The diagnostic evaluation of men with suspected prostate cancer (PCa) yet inconclusive MRI (PI-RADS ≤ 3) presents a common clinical challenge. [68Ga]Ga-labelled prostate-specific membrane antigen ([68Ga]Ga-PSMA) positron emission tomography/computed tomography (PET/CT) has shown promise in identifying clinically significant PCa (csPCa). We aim to establish a diagnostic model incorporating PSMA-PET to enhance the diagnostic process of csPCa in PI-RADS ≤ 3 men.This study retrospective included 151 men with clinical suspicion of PCa and PI-RADS ≤ 3 MRI. All men underwent [68Ga]Ga-PSMA PET/CT scans and ultrasound/MRI/PET fusion-guided biopsies. csPCa was defined as Grade Group ≥ 2. PRIMARY-scores from PSMA-PET scans were evaluated. A diagnostic model incorporating PSMA-PET and prostate-specific antigen (PSA)-derived parameters was developed. The discriminative performance and clinical utility were compared with conventional methods. Internal validation was conducted using a fivefold cross-validation with 1000 iterations.In this PI-RADS ≤ 3 cohort, areas-under-the-curve (AUCs) for detecting csPCa were 0.796 (95%CI, 0.738-0.853), 0.851 (95%CI, 0.783-0.918) and 0.806 (95%CI, 0.742-0.870) for PRIMARY-score, SUVmax and routine clinical PSMA-PET assessment, respectively. The diagnostic model comprising PRIMARY-score, SUVmax and serum free PSA/total PSA (fPSA/tPSA) achieved a significantly higher AUC of 0.906 (95%CI, 0.851-0.961) compared to strategies based on PRIMARY-score or SUVmax (P < 0.05) and markedly superior to conventional strategies typically based on PSA density (P < 0.001). The average fivefold cross-validated AUC with 1000 iterations was 0.878 (95%CI, 0.820-0.954). Theoretically, using a threshold of 21.6%, the model could have prevented 78% of unnecessary biopsies while missing only 7.8% of csPCa cases in this cohort.A novel diagnostic model incorporating PSMA-PET derived metrics-PRIMARY-score and SUVmax-along with serum fPSA/tPSA, has been developed and validated. The integrated model may assist clinical decision-making with enhanced diagnostic accuracy over the individual conventional metrics. It has great potential to reduce unnecessary biopsies for men with PI-RADS ≤ 3 MRI results and warrants further prospective and external evaluations.© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.