前列腺特异性抗原（PSA）已广泛应用于前列腺癌（PCa）的筛查和早期诊断。然而，在4-10 ng/mL的PSA灰区，用于诊断PCa的灵敏度和特异度有限，导致了大量不必要和侵入性的前列腺生物检查，可能导致潜在的过度诊断和过度治疗。我们旨在结合基于68Ga-PSMA PET/CT的最大标准摄取值（SUVmax）和临床指标，在灰区PSA水平的男性中预测临床意义的PCa（CSPCa）。我们招募了81例疑似PCa的患者，基于血清总PSA（TPSA）水平增高（4-10 ng/mL）进行经直肠超声/磁共振成像（MRI）/PET融合引导生物检查。其中，经组织病理学证实的患者被分为CSPCa组和非CSPCa组，并且比较了PSA浓度、前列腺体积（PV）、PSA密度（PSAD）、游离PSA（FPSA）/TPSA、前列腺成像报告和数据系统第2.1版（PI-RADS v2.1）评分、68Ga-PSMA PET/CT成像评估结果和SUVmax的数据。进行多元逻辑回归分析，以确定CSPCa的独立预测因子，从而建立了基于SUVmax的预测模型，并通过分析受试者工作特征曲线（ROC曲线）和决策曲线分析来评估该模型。与非CSPCa相比，CSPCa患者的PV较小（中位数31.40 mL），FPSA/TPSA较低（中位数0.12），PSAD较大（中位数0.21 ng/mL2），并且PI-RADS评分较高（P < 0.05）。预测模型包括68Ga-PSMA PET/CT最大标准摄取值、PV和FPSA/TPSA，在所有预测因子中具有最高的AUC值（0.927），而其他单独预测因子的AUC值分别为0.585（PSA）、0.652（mpMRI）和0.850（68Ga-PSMA PET/CT）。预测模型的诊断敏感性和特异性分别为86.21%和86.54%。鉴于常规PSA测试的低诊断准确率，我们开发和验证了一种基于68Ga-PSMA PET/CT SUVmax、PV和FPSA/TPSA的新型预测模型，该模型可以更满意地预测CSPCa。本研究提供了一种在PSA灰区具有高准确性的非侵入性预测模型，从而可能更好地避免不必要的活检程序。© 2023年。国际癌症影像学学会（ICIS）。

The prostate-specific antigen (PSA) has been widely used in screening and early diagnosis of prostate cancer (PCa). However, in the PSA grey zone of 4-10 ng/ml, the sensitivity and specificity for diagnosing PCa are limited, resulting in considerable number of unnecessary and invasive prostate biopsies, which may lead to potential overdiagnosis and overtreatment. We aimed to predict clinically significant PCa (CSPCa) by combining the maximal standardized uptake value (SUVmax) based on 68Ga‑PSMA PET/CT and clinical indicators in men with gray zone PSA levels.81 patients with suspected PCa based on increased serum total PSA (TPSA) levels of 4 - 10 ng/mL who underwent transrectal ultrasound/magnetic resonance imaging (MRI)/PET fusion-guided biopsy were enrolled. Among them, patients confirmed by histopathology were divided into the CSPCa group and the non-CSPCa group, and data on PSA concentration, prostate volume (PV), PSA density (PSAD), free PSA (FPSA)/TPSA, Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) score, 68Ga-PSMA PET/CT imaging evaluation results and SUVmax were compared. Multivariate logistic regression analysis was performed to identify the independent predictors for CSPCa, thereby establishing a predictive model based on SUVmax that was evaluated by analyzing the receiver operating characteristic (ROC) curve and decision curve analysis.Compared to non-CSPCa, CSPCa patients had smaller PVs (median, 31.40 mL), lower FPSA/TPSA (median, 0.12), larger PSADs (median, 0.21 ng/mL2) and higher PI-RADS scores (P < 0.05). The prediction model comprising 68Ga-PSMA PET/CT maximal standardized uptake value, PV and FPSA/TPSA had the highest AUC of 0.927 compared with that of other predictors alone (AUCs of 0.585 for PSA, 0.652 for mpMRI and 0.850 for 68Ga-PSMA PET/CT). The diagnostic sensitivity and specificity of the prediction model were 86.21% and 86.54%, respectively.Given the low diagnostic accuracy of regular PSA tests, a new prediction model based on the 68Ga-PSMA PET/CT SUVmax, PV and FPSA/TPSA was developed and validated, and this model could provide a more satisfactory predictive accuracy for CSPCa. This study provides a noninvasive prediction model with high accuracy for the diagnosis of CSPCa in the PSA gray zone, thus may be better avoiding unnecessary biopsy procedures.© 2023. International Cancer Imaging Society (ICIS).