Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet
Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet
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DOI：
10.1109/tmi.2019.2901928
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发表时间：
2019-11-01
影响因子：
10.6
通讯作者：
Sung, Kyunghyun
中科院分区：
文献类型：
--
作者：
Cao, Ruiming;Bajgiran, Amirhossein Mohammadian;Sung, Kyunghyun
多参数MRI（mp-MRI）被认为是诊断前列腺癌（PCa）的最佳非侵入性成像方式。然而，目前用于PCa诊断的mp-MRI受到定性或半定量解释标准的限制，导致阅片者之间的变异性和评估病变侵袭性的次优能力。卷积神经网络（CNN）是自动学习各种任务（包括癌症检测）的区分特征的强大方法。我们提出了一种新的多类CNN，FocalNet，以联合检测PCa病变并使用Gleason评分（GS）预测其侵袭性。FocalNet描述了病变的侵袭性，并充分利用了mp-MRI的独特知识。我们收集了417例在机器人辅助腹腔镜前列腺切除术前接受3 T mp-MRI检查的患者的前列腺mp-MRI数据集。FocalNet在这个大型研究队列中进行了训练和评估，并进行了五重交叉验证。在病变检测的自由应答受试者工作特征（FROC）分析中，FocalNet在每例患者出现1个假阳性时，索引病变和临床显著病变的灵敏度分别为89.7和87.9。对于GS分类，通过受试者工作特征（ROC）分析进行评估，FocalNet获得的临床显著PCa（GS >= 3 4）和GS >= 4 3的PCa分类曲线下面积分别为0.81和0.79。与使用当前诊断指南的放射科医生的前瞻性表现相比，FocalNet对索引病变和临床显著病变的检测灵敏度相当，仅比经验丰富的放射科医生低3.4和1.5，无统计学意义。
Multi-parametric MRI (mp-MRI) is considered the best non-invasive imaging modality for diagnosing prostate cancer (PCa). However, mp-MRI for PCa diagnosis is currently limited by the qualitative or semi-quantitative interpretation criteria, leading to inter-reader variability and a suboptimal ability to assess lesion aggressiveness. Convolutional neural networks (CNNs) are a powerful method to automatically learn the discriminative features for various tasks, including cancer detection. We propose a novel multi-class CNN, FocalNet, to jointly detect PCa lesions and predict their aggressiveness using Gleason score (GS). FocalNet characterizes lesion aggressiveness and fully utilizes distinctive knowledge from mp-MRI. We collected a prostate mp-MRI dataset from 417 patients who underwent 3T mp-MRI exams prior to robotic-assisted laparoscopic prostatectomy. FocalNet was trained and evaluated in this large study cohort with fivefold cross validation. In the free-response receiver operating characteristics (FROC) analysis for lesion detection, FocalNet achieved 89.7 and 87.9 sensitivity for index lesions and clinically significant lesions at one false positive per patient, respectively. For the GS classification, evaluated by the receiver operating characteristics (ROC) analysis, FocalNet received the area under the curve of 0.81 and 0.79 for the classifications of clinically significant PCa (GS >= 3 4) and PCa with GS >= 4 3, respectively. With the comparison to the prospective performance of radiologists using the current diagnostic guideline, FocalNet demonstrated comparable detection sensitivity for index lesions and clinically significant lesions, only 3.4 and 1.5 lower than highly experienced radiologists without statistical significance.