Midya Abhishek, Hiremath Amogh, Huber Jacob, Sankar Viswanathan Vidya, Omil-Lima Danly, Mahran Amr, Bittencourt Leonardo K, Harsha Tirumani Sree, Ponsky Lee, Shiradkar Rakesh, Madabhushi Anant
Department of Biomedical Engineering, Emory University, Atlanta, GA, United States.
Picture Health, Cleveland, OH, United States.
Front Oncol. 2023 Sep 5;13:1166047. doi: 10.3389/fonc.2023.1166047. eCollection 2023.
The aim of this study was to quantify radiomic changes in prostate cancer (PCa) progression on serial MRI among patients on active surveillance (AS) and evaluate their association with pathologic progression on biopsy.
This retrospective study comprised = 121 biopsy-proven PCa patients on AS at a single institution, of whom = 50 at baseline conformed to the inclusion criteria. ISUP Gleason Grade Groups (GGG) were obtained from 12-core TRUS-guided systematic biopsies at baseline and follow-up. A biopsy upgrade (AS+) was defined as an increase in GGG (or in number of positive cores) and no upgrade (AS-) was defined when GGG remained the same during a median period of 18 months. Of = 50 patients at baseline, = 30 had MRI scans available at follow-up (median interval = 18 months) and were included for delta radiomic analysis. A total of 252 radiomic features were extracted from the PCa region of interest identified by board-certified radiologists on 3T bi-parametric MRI [T2-weighted (T2W) and apparent diffusion coefficient (ADC)]. Delta radiomic features were computed as the difference of radiomic feature between baseline and follow-up scans. The association of AS+ with age, prostate-specific antigen (PSA), Prostate Imaging Reporting and Data System (PIRADS v2.1) score, and tumor size was evaluated at baseline and follow-up. Various prediction models were built using random forest (RF) classifier within a threefold cross-validation framework leveraging baseline radiomics (), baseline radiomics + baseline clinical (), delta radiomics (), delta radiomics + baseline clinical (), and delta radiomics + delta clinical ().
An AUC of 0.64 ± 0.09 was obtained for , which increased to 0.70 ± 0.18 with the integration of clinical variables (). yielded an AUC of 0.74 ± 0.15. Integrating delta radiomics with baseline clinical variables yielded an AUC of 0.77 ± 0.23. resulted in the best AUC of 0.84 ± 0.20 ( < 0.05) among all combinations.
Our preliminary findings suggest that delta radiomics were more strongly associated with upgrade events compared to PIRADS and other clinical variables. Delta radiomics on serial MRI in combination with changes in clinical variables (PSA and tumor volume) between baseline and follow-up showed the strongest association with biopsy upgrade in PCa patients on AS. Further independent multi-site validation of these preliminary findings is warranted.
本研究旨在量化主动监测（AS）患者系列磁共振成像（MRI）上前列腺癌（PCa）进展的影像组学变化，并评估其与活检病理进展的相关性。
本回顾性研究纳入了一家机构121例经活检证实接受AS的PCa患者，其中50例基线时符合纳入标准。国际泌尿病理学会（ISUP）Gleason分级组（GGG）通过基线和随访时12针经直肠超声（TRUS）引导下系统活检获得。活检升级（AS+）定义为GGG增加（或阳性针数增加），在18个月的中位随访期内GGG保持不变则定义为未升级（AS-）。基线时的50例患者中，30例随访时有MRI扫描（中位间隔时间 = 18个月），纳入进行影像组学差异分析。由认证放射科医生在3T双参数MRI（T2加权成像（T2W）和表观扩散系数（ADC））上确定的PCa感兴趣区域共提取了252个影像组学特征。影像组学差异特征计算为基线和随访扫描之间影像组学特征的差值。在基线和随访时评估AS+与年龄、前列腺特异性抗原（PSA）、前列腺影像报告和数据系统（PIRADS v2.1）评分及肿瘤大小的相关性。在三倍交叉验证框架内使用随机森林（RF）分类器构建了各种预测模型，利用基线影像组学（）、基线影像组学 + 基线临床指标（）、影像组学差异（）、影像组学差异 + 基线临床指标（）以及影像组学差异 + 临床指标差异（）。
单独使用基线影像组学（）时AUC为0.64 ± 0.09，纳入临床变量（）后升至0.70 ± 0.18。影像组学差异（）的AUC为0.74 ± 0.15。影像组学差异与基线临床变量结合时AUC为0.77 ± 0.23。在所有组合中，影像组学差异 + 临床指标差异（）的AUC最佳，为0.84 ± 0.20（P < 0.05）。
我们的初步研究结果表明，与PIRADS和其他临床变量相比，影像组学差异与升级事件的相关性更强。系列MRI上的影像组学差异与基线和随访之间临床变量（PSA和肿瘤体积）的变化相结合，显示出与接受AS的PCa患者活检升级的最强相关性。这些初步研究结果需要进一步独立的多中心验证。
