Midya Abhishek, Tirumani Sreeharsha, Bittencourt Leonardo Kayat, Azamat Sena, Balakrishnan Siddharth, Hiremath Amogh, Wido Sarah, Fu Pingfu, Ponsky Lee, Madabhushi Anant, Shiradkar Rakesh
Emory University and Georgia Institute of Technology, Atlanta, Georgia.
University Hospitals Cleveland Medical Center, Cleveland, Ohio.
JU Open Plus. 2025 Jul;3(7). doi: 10.1097/ju9.0000000000000310. Epub 2025 Jul 3.
To quantify population-specific differences in prostate cancer (PCa) presentation between African American (AA) and White (W) men on MRI using radiomics.
We identified N = 149 men with PCa who underwent 3T MRI, a confirmatory biopsy and for whom self-reported race was available. Patient studies were partitioned into training (D) and hold-out test set (D). Three hundred radiomic features quantifying textural patterns were extracted from radiologist delineated PCa regions of interest (ROI) on biparametric MRI. Features with significant differences ( < .05) between clinically significant (csPCa) and insignificant (ciPCa) PCa were identified. Machine learning models were trained separately for AA and W men (C, C) on D to distinguish csPCa and ciPCa. Validation on D was assessed for AUC and compared against a population agnostic model (C) in combination with clinical parameters (age, PSA, Prostate Imaging Reporting and Diagnostic System and tumor volume).
Radiomic features from PCa ROIs on biparametric MRI associated with csPCa were observed to be different in AA compared with W men, especially in the peritumoral region. Population-specific radiomic models outperformed similarly trained C models (AUC = 0.84, 0.57 with C, C; < .05) in AA men on D. Similar findings were observed for W men (AUC = 0.71, 0.60 with C, C; < .05). Integrating clinical and radiomics further improved the risk stratification for AA men (AUC = 0.90) and W men (AUC = 0.75).
Accounting for population-specific differences in radiomics may enable improved PCa risk stratification at MRI among AA men compared with a population agnostic approach.
利用影像组学量化非裔美国（AA）男性和白人（W）男性在前列腺癌（PCa）MRI表现上的种族特异性差异。
我们纳入了149例接受3T MRI检查、确诊活检且有自我报告种族信息的PCa男性患者。将患者研究分为训练集（D）和保留测试集（D）。从放射科医生在双参数MRI上勾勒出的PCa感兴趣区域（ROI）中提取300个量化纹理模式的影像组学特征。确定临床显著（csPCa）和非显著（ciPCa）PCa之间存在显著差异（<.05）的特征。在训练集D上分别为AA男性和W男性训练机器学习模型（C，C）以区分csPCa和ciPCa。在测试集D上评估AUC进行验证，并与结合临床参数（年龄、前列腺特异性抗原、前列腺影像报告和数据系统以及肿瘤体积）的非种族特异性模型（C）进行比较。
观察到双参数MRI上PCa ROI的影像组学特征与csPCa相关，AA男性与W男性不同，尤其是在肿瘤周围区域。在测试集D上，种族特异性影像组学模型在AA男性中表现优于类似训练的非种族特异性模型（AUC = 0.84，非种族特异性模型为0.57；<.05）。在W男性中也观察到类似结果（AUC = 0.71，非种族特异性模型为0.60；<.05）。整合临床和影像组学进一步改善了AA男性（AUC = 0.90）和W男性（AUC = 0.75）的风险分层。
与非种族特异性方法相比，考虑影像组学中的种族特异性差异可能有助于改善AA男性在MRI上的PCa风险分层。
