Radiomic Features for Prostate Cancer Detection on MRI Differ Between the Transition and Peripheral Zones: Preliminary Findings from a Multi-Institutional Study.
发表日期： 2016.12.19 来源： Journal of Magnetic Resonance Imaging Jmri, 2016, 46(1).
Ginsburg SB1, Algohary A1, Pahwa S2, Gulani V2, Ponsky L3, Aronen HJ4, Boström PJ5, Böhm M6, Haynes AM6, Brenner P7, Delprado W8, Thompson J6, Pulbrock M6, Taimen P9, Villani R10, Stricker P7, Rastinehad AR11, Jambor I4, Madabhushi A1.
1. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
2. Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
3. Department of Urology, Case Western Reserve University, Cleveland, Ohio, USA.
4. Department of Diagnostic Radiology, University of Turku, Turku, Finland.
5. Department of Urology, Turku University Hospital, Turku, Finland.
6. Garvan Institute of Medical Research, Sydney, Australia.
7. Department of Urology, St. Vincent's Hospital, Sydney, Australia.
8. Douglass Hanly Moir Pathology, Sydney, Australia.
9. Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland.
10. Department of Radiology, Hofstra North Shore-LIJ, New Hyde Park, New York, USA.
11. Department of Radiology, Icahn School of Medicine at Mount Sinai, Manhattan, New York, USA.
To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ).
从三个机构的80名患者回顾性地获得了3T mpMRI，包括T2加权（T2w），表观扩散系数（ADC）图和动态造影增强MRI（DCE-MRI）。本研究由各参与机构的机构审查委员会批准。从T2w MRI和ADC图中提取了一阶统计，共现和小波特征，并从DCE-MRI中提取了对比动力学特征。进行特征选择，分别在移行带和周边区域中识别前列腺癌检测的10个特征。两个逻辑回归分类器使用这些特征来检测前列腺癌，并根据受体-工作特征曲线下面积（AUC）进行评估。将分类器性能与无知区域分类器进行比较。
Materials and Methods:
3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First-order statistical, co-occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE-MRI. Feature selection was performed to identify 10 features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver-operating characteristic curve (AUC). Classifier performance was compared with a zone-ignorant classifier.
用于区分PCa检测中的移行带和周边区域的影像组学特征经鉴定为有用的。当基于单一体素进行分类时，PZ特异性分类器在独立测试组上检测PZ肿瘤比无知分类器在整个前列腺区域进行癌症检测具有更高精度（AUC = 0.61-0.71） P<0.05）。当对来自多个机构的MRI数据进行分类器评估时，在统计学上所有机构均获得相似的AUC值（P> 0.14）。
Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per-voxel basis, a PZ-specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC = 0.61–0.71) than a zone-ignorant classifier trained to detect cancer throughout the entire prostate (P < 0.05). When classifiers were evaluated on MRI data from multiple institutions, statistically similar AUC values (P > 0.14) were obtained for all institutions.
A zone-aware classifier significantly improves the accuracy of cancer detection in the PZ.
magnetic resonance imaging; multi-institutional; prostate cancer; radiomics