Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging
Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging
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DOI：
10.1118/1.4918318
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发表时间：
2015-05-01
期刊：
影响因子：
3.8
通讯作者：
Summers, Ronald M.
中科院分区：
文献类型：
--
作者：
Kwak, Jin Tae;Xu, Sheng;Summers, Ronald M.
目的：作者提出一种前列腺癌计算机辅助诊断（CAD）系统，以帮助提高多参数磁共振成像（MRI）的准确性、可重复性和标准化。方法：该系统利用两种MRI序列[t2加权MRI和高b值（b = 2000 s/mm(2)）弥散加权成像（DWI）]和基于局部二值模式的纹理特征。采用三阶段特征选择方法提供最具判别性的特征。作者共纳入了244名患者。对108例患者（78例磁共振阳性前列腺癌和105例良性磁共振阳性病变）进行CAD系统培训，对136例患者（68例磁共振阳性前列腺癌，111例磁共振良性阳性病变，117例磁共振阴性良性病变）进行回顾性验证研究。结果：在区分肿瘤与mr阳性良性病变时，受试者工作特征曲线下面积（AUC）为0.83[95%可信区间（CI）： 0.76-0.89]。对于癌症与mr阳性或mr阴性良性病变，作者获得的AUC为0.89 AUC （95% CI: 0.84-0.93）。CAD系统的性能不依赖于前列腺的特定区域，例如外周区或过渡区。此外，CAD系统优于其他MRI序列组合：T2W MRI，高b值DWI和DWI的标准表观扩散系数（ADC）图。结论：该CAD系统能够检测出具有鉴别性的纹理特征，用于前列腺癌的检测和定位，是提高前列腺癌诊断质量和效率的一种有前景的工具。
Purpose: The authors propose a computer-aided diagnosis (CAD) system for prostate cancer to aid in improving the accuracy, reproducibility, and standardization of multiparametric magnetic resonance imaging (MRI).Methods: The proposed system utilizes two MRI sequences [T2-weighted MRI and high-b-value (b = 2000 s/mm(2)) diffusion-weighted imaging (DWI)] and texture features based on local binary patterns. A three-stage feature selection method is employed to provide the most discriminative features. The authors included a total of 244 patients. Training the CAD system on 108 patients (78 MR-positive prostate cancers and 105 benign MR-positive lesions), two validation studies were retrospectively performed on 136 patients (68 MR-positive prostate cancers, 111 benign MR-positive lesions, and 117 MR-negative benign lesions).Results: In distinguishing cancer from MR-positive benign lesions, an area under receiver operating characteristic curve (AUC) of 0.83 [95% confidence interval (CI): 0.76-0.89] was achieved. For cancer vs MR-positive or MR-negative benign lesions, the authors obtained an AUC of 0.89 AUC (95% CI: 0.84-0.93). The performance of the CAD system was not dependent on the specific regions of the prostate, e.g., a peripheral zone or transition zone. Moreover, the CAD system outperformed other combinations of MRI sequences: T2W MRI, high-b-value DWI, and the standard apparent diffusion coefficient (ADC) map of DWI.Conclusions: The novel CAD system is able to detect the discriminative texture features for cancer detection and localization and is a promising tool for improving the quality and efficiency of prostate cancer diagnosis.