评估移行区和外周带前列腺特异性抗原（PSA）密度相对于全腺PSA密度对多参数磁共振成像检测前列腺癌的影响。

Assessing the Impact of Transition and Peripheral Zone PSA Densities Over Whole-Gland PSA Density for Prostate Cancer Detection on Multiparametric MRI.

作者信息

Esengur Omer Tarik, Stevenson Emma, Stecko Hunter, Lay Nathan S, Yang Dong, Tetreault Jesse, Xu Ziyue, Xu Daguang, Yilmaz Enis C, Gelikman David G, Harmon Stephanie A, Merino Maria J, Gurram Sandeep, Wood Bradford J, Choyke Peter L, Pinto Peter A, Turkbey Baris

机构信息

Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.

NVIDIA Corporation, Santa Clara, California, USA.

出版信息

Prostate. 2025 May;85(6):612-624. doi: 10.1002/pros.24863. Epub 2025 Feb 25.

DOI:10.1002/pros.24863

PMID:39996409

Abstract

BACKGROUND

Whole-gland (WG) prostate-specific antigen (PSA) density (PSAD) has proven useful in diagnosing to be beneficial in localized prostate cancer (PCa). This study aimed to evaluate the predictive performance of WG and zonal (transition zone [TZ] and peripheral zone [PZ]) PSAD in predicting PCa and clinically significant PCa (csPCa) in prostate MRI.

METHODS

A retrospective analysis was conducted on consecutive patients who underwent multiparametric MRI and MRI/US fusion-guided biopsy between March 2019 and July 2024. TZ-PSAD, PZ-PSAD, and WG-PSAD were calculated using in-house AI models. Optimal thresholds for TZ-PSAD and PZ-PSAD were determined using the Youden index from receiver operating characteristic (ROC) curve analyses with five-fold cross-validation, whereas 0.15 ng/mL was applied as the threshold for WG-PSAD. Statistical comparisons were performed using Wilcoxon rank-sum, χ, and Fisher's exact tests. Logistic regression (LR) and area under the ROC curve (AUC) analyses with DeLong's test were conducted to evaluate diagnostic performance.

RESULTS

The study cohort included 774 consecutive patients (median age = 67 years [interquartile range {IQR}: 61-71], median WG-PSAD = 0.11 ng/mL [IQR: 0.07-0.17], median TZ-PSAD = 0.22 ng/mL [IQR: 0.12-0.41], median PZ-PSAD = 0.13 ng/mL [IQR: 0.16-0.34]). Among these patients, 475 had PCa and 341 had csPCa. The mean optimal thresholds for TZ-PSAD and PZ-PSAD were 0.20 ng/mL and 0.21 ng/mL, respectively, for PCa, whereas they were 0.26 and 0.23, respectively, for csPCa. Multivariable LR identified TZ-PSAD (OR = 2.00, p = 0.03) and WG-PSAD (OR = 2.40, p = 0.02) as significant predictors of PCa. For csPCa, TZ-PSAD was the only independent predictor (OR = 2.13, p = 0.02) among PSAD measurements. TZ-PSAD showed a superior AUC for both PCa (0.79 ± 0.05) and csPCa (0.77 ± 0.02) compared to WG-PSAD (0.77 ± 0.06 for PCa, 0.76 ± 0.03 for csPCa) and PZ-PSAD (0.69 ± 0.06 for PCa, 0.70 ± 0.04 for csPCa; p < 0.001).

CONCLUSIONS

Both TZ-PSAD and WG-PSAD are strong predictors of PCa, but TZ-PSAD is a superior predictor of csPCa compared to WG-PSAD and PZ-PSAD. Further prospective studies are warranted to validate these findings.

TRIAL REGISTRATION

NCT03354416.

摘要

背景

全腺（WG）前列腺特异性抗原（PSA）密度（PSAD）已被证明有助于诊断局限性前列腺癌（PCa）。本研究旨在评估WG及分区（移行区[TZ]和外周区[PZ]）PSAD在前列腺MRI中预测PCa及临床显著前列腺癌（csPCa）的性能。

方法

对2019年3月至2024年7月期间连续接受多参数MRI及MRI/超声融合引导活检的患者进行回顾性分析。使用内部人工智能模型计算TZ-PSAD、PZ-PSAD和WG-PSAD。通过五折交叉验证的受试者操作特征（ROC）曲线分析中的约登指数确定TZ-PSAD和PZ-PSAD的最佳阈值，而WG-PSAD的阈值设定为0.15 ng/mL。采用Wilcoxon秩和检验、χ检验和Fisher精确检验进行统计学比较。进行逻辑回归（LR）及采用DeLong检验的ROC曲线下面积（AUC）分析以评估诊断性能。

结果

研究队列包括774例连续患者（中位年龄 = 67岁[四分位间距{IQR}：61 - 71]，中位WG-PSAD = 0.11 ng/mL[IQR：0.07 - 0.17]，中位TZ-PSAD = 0.22 ng/mL[IQR：0.12 - 0.41]，中位PZ-PSAD = 0.13 ng/mL[IQR：0.06 - 0.34]）。这些患者中，475例患有PCa，341例患有csPCa。PCa的TZ-PSAD和PZ-PSAD的平均最佳阈值分别为0.20 ng/mL和0.21 ng/mL，而csPCa的分别为0.26和0.23。多变量LR确定TZ-PSAD（OR = 2.00，p = 0.03）和WG-PSAD（OR = 2.40，p = 0.02）为PCa的显著预测因子。对于csPCa，TZ-PSAD是PSAD测量值中唯一的独立预测因子（OR = 2.13，p = 0.02）。与WG-PSAD（PCa为0.77±0.06，csPCa为0.76±0.03）和PZ-PSAD（PCa为0.69±0.06，csPCa为0.70±0.04；p < 0.001）相比，TZ-PSAD在PCa（0.79±0.05）和csPCa（0.77±0.02）方面均显示出更高的AUC。