Shahait Mohammed, Usamentiaga Ruben, Tong Yubing, Sandberg Alex, Lee David I, Udupa Jayaram K, Torigian Drew A
Department of Surgery, Clemenceau Medical Center, Dubai, United Arab Emirates.
Department of Computer Science and Engineering, University of Oviedo, Gijon, Spain.
J Endourol. 2023 Oct;37(10):1156-1161. doi: 10.1089/end.2023.0215. Epub 2023 Aug 31.
Altered systemic and cellular lipid metabolism plays a pivotal role in the pathogenesis of prostate cancer (PCa). In this study, we aimed to characterize T1-magnetic resonance imaging (MRI)-derived radiomic parameters of periprostatic adipose tissue PPAT) associated with clinically significant PCa (Gleason score ≥7 [3 + 4]) in a cohort of men who underwent robot-assisted prostatectomy. Preoperative MRI scans of 98 patients were identified. The volume of interest was defined by identifying an annular shell-like region on each MRI slice to include all surgically resectable visceral adipose tissue. An optimal biomarker method was used to identify features from 7631 intensity- and texture-based properties that maximized the classification of patients into clinically significant PCa and indolent tumors at the final pathology analysis. Six highest ranked optimal features were derived, which demonstrated a sensitivity, specificity, and accuracy of association with the presence of clinically significant PCa, and area under a receiver operating characteristic curve of 0.95, 0.39 0.82, and 0.82, respectively. A highly independent set of PPAT features derived from MRI scans that predict patients with clinically significant PCa was developed and tested. With future external validation, these features may provide a more precise scientific basis for deciding to omit biopsies in patients with borderline prostate-specific antigen kinetics and multiparametric MRI readings and help in the decision of enrolling patients into active surveillance.
全身和细胞脂质代谢改变在前列腺癌（PCa）发病机制中起关键作用。在本研究中，我们旨在对接受机器人辅助前列腺切除术的男性队列中，与临床显著性PCa（ Gleason评分≥7 [3 + 4]）相关的前列腺周围脂肪组织（PPAT）的T1磁共振成像（MRI）衍生的放射组学参数进行特征描述。确定了98例患者的术前MRI扫描。通过在每个MRI切片上识别一个环形壳状区域来定义感兴趣的体积，以包括所有手术可切除的内脏脂肪组织。使用一种最佳生物标志物方法从7631个基于强度和纹理的属性中识别特征，这些特征在最终病理分析中能最大限度地将患者分类为临床显著性PCa和惰性肿瘤。得出了六个排名最高的最佳特征，它们与临床显著性PCa存在的关联的敏感性、特异性和准确性分别为0.95、0.39、0.82和0.82，以及受试者操作特征曲线下面积。开发并测试了一组高度独立的、源自MRI扫描的PPAT特征，用于预测临床显著性PCa患者。通过未来的外部验证，这些特征可能为决定在前列腺特异性抗原动力学临界和多参数MRI读数的患者中省略活检提供更精确的科学依据，并有助于决定将患者纳入主动监测。
