Ginsburg Shoshana B, Algohary Ahmad, Pahwa Shivani, Gulani Vikas, Ponsky Lee, Aronen Hannu J, Boström Peter J, Böhm Maret, Haynes Anne-Maree, Brenner Phillip, Delprado Warick, Thompson James, Pulbrock Marley, Taimen Pekka, Villani Robert, Stricker Phillip, Rastinehad Ardeshir R, Jambor Ivan, Madabhushi Anant
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
J Magn Reson Imaging. 2017 Jul;46(1):184-193. doi: 10.1002/jmri.25562. Epub 2016 Dec 19.
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).
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.
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.
3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:184-193.
在一项多机构研究中评估，从3特斯拉（T）多参数磁共振成像（mpMRI）检测前列腺癌（PCa）时，移行区（TZ）的影像组学特征是否与外周区（PZ）的不同。
回顾性收集了来自三个机构的80例患者的3T mpMRI，包括T2加权（T2w）、表观扩散系数（ADC）图和动态对比增强磁共振成像（DCE-MRI）。本研究经各参与机构的机构审查委员会批准。从T2w MRI和ADC图中提取一阶统计特征、共生特征和小波特征，并从DCE-MRI中提取对比动力学特征。进行特征选择以分别识别用于TZ和PZ中PCa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PCa，并通过受试者操作特征曲线下面积（AUC）进行评估。将分类器性能与不区分区域的分类器进行比较。
被确定对PCa检测有用的影像组学特征在TZ和PZ之间存在差异。当在每个体素基础上进行分类时，一个PZ特异性分类器在独立测试集上检测PZ肿瘤的准确性（AUC = 0.61 - 0.71）显著高于为在整个前列腺中检测癌症而训练的不区分区域的分类器（P < 0.05）。当在来自多个机构的MRI数据上评估分类器时，所有机构获得的AUC值在统计学上相似（P > 0.14）。
区分区域的分类器显著提高了PZ中癌症检测的准确性。
3 技术效能：2级 J. MAGN. RESON. IMAGING 2017;46:184 - 193.
