背景:前列腺特异性抗原(PSA)已广泛用于前列腺癌(PCa)的筛查和早期诊断。然而,在4-10ng/ml的PSA灰色区域,诊断PCa的敏感性和特异性有限,导致大量不必要的侵入性前列腺活检,这可能导致潜在的过度诊断和过度治疗。我们旨在通过结合基于68Ga-PSMAPET/CT的最大标准化摄取值(SUVmax)和具有灰色地带PSA水平的男性的临床指标来预测临床上有意义的PCa(CSPCa)。
方法:纳入81例基于血清总PSA(TPSA)水平升高4-10ng/mL的疑似PCa患者,这些患者接受了经直肠超声/磁共振成像(MRI)/PET融合引导活检。其中,经组织病理学证实的患者分为CSPCa组和非CSPCa组,和PSA浓度数据,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2.1版(PI-RADSv2.1)评分,68Ga-PSMAPET/CT成像评价成果与SUVmax停止了比拟。进行多因素logistic回归分析以确定CSPCa的独立预测因子。从而建立基于SUVmax的预测模型,该模型通过分析受试者工作特征(ROC)曲线和决策曲线分析来评估。
结果:与非CSPCa相比,CSPCa患者的PVs较小(中位数,31.40mL),较低的FPSA/TPSA(中位数,0.12),较大的PSAD(中位数,0.21ng/mL2)和更高的PI-RADS评分(P<0.05)。预测模型包括68Ga-PSMAPET/CT最大标准化摄取值,与其他预测因子相比,PV和FPSA/TPSA的AUC最高,为0.927(PSA的AUC为0.585,对于MPMRI为0.652,对于68Ga-PSMAPET/CT为0.850)。预测模型的诊断灵敏度和特异度分别为86.21%和86.54%,分别。
结论:鉴于常规PSA测试的诊断准确性低,基于68Ga-PSMAPET/CTSUVmax的新预测模型,开发并验证了PV和FPSA/TPSA,该模型可以为CSPCa提供更令人满意的预测精度。本研究为PSA灰色地带CSPCa的诊断提供了一种具有较高准确性的无创性预测模型,因此,可以更好地避免不必要的活检程序。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.