基于磁共振成像的深度学习检测脑癌转移灶
Deep-Learning Detection of Cancer Metastases to the Brain on MRI.
作者信息
Zhang Min, Young Geoffrey S, Chen Huai, Li Jing, Qin Lei, McFaline-Figueroa J Ricardo, Reardon David A, Cao Xinhua, Wu Xian, Xu Xiaoyin
机构信息
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
出版信息
J Magn Reson Imaging. 2020 Oct;52(4):1227-1236. doi: 10.1002/jmri.27129. Epub 2020 Mar 13.
BACKGROUND
Approximately one-fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, planning of radiotherapy, and the monitoring of treatment response. Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective.
PURPOSE
To develop a deep-learning-based approach for finding brain metastasis on MRI.
STUDY TYPE
Retrospective.
SEQUENCE
Axial postcontrast 3D T -weighted imaging.
FIELD STRENGTH
1.5T and 3T.
POPULATION
A total of 361 scans of 121 patients were used to train and test the Faster region-based convolutional neural network (Faster R-CNN): 1565 lesions in 270 scans of 73 patients for training; 488 lesions in 91 scans of 48 patients for testing. From the 48 outputs of Faster R-CNN, 212 lesions in 46 scans of 18 patients were used for training the RUSBoost algorithm (MatLab) and 276 lesions in 45 scans of 30 patients for testing.
ASSESSMENT
Two radiologists diagnosed and supervised annotation of metastases on brain MRI as ground truth. This data were used to produce a 2-step pipeline consisting of a Faster R-CNN for detecting abnormal hyperintensity that may represent brain metastasis and a RUSBoost classifier to reduce the number of false-positive foci detected.
STATISTICAL TESTS
The performance of the algorithm was evaluated by using sensitivity, false-positive rate, and receiver's operating characteristic (ROC) curves. The detection performance was assessed both per-metastases and per-slice.
RESULTS
Testing on held-out brain MRI data demonstrated 96% sensitivity and 20 false-positive metastases per scan. The results showed an 87.1% sensitivity and 0.24 false-positive metastases per slice. The area under the ROC curve was 0.79.
CONCLUSION
Our results showed that deep-learning-based computer-aided detection (CAD) had the potential of detecting brain metastases with high sensitivity and reasonable specificity.
LEVEL OF EVIDENCE
3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1227-1236.
背景
在所有癌症转移病例中，约四分之一发生于脑部。磁共振成像（MRI）是检测脑转移瘤、规划放射治疗以及监测治疗反应的主要技术。肿瘤治疗的进展现在需要在肿瘤治疗最有效的时候，检测出亚厘米大小的新的或正在生长的转移瘤。
目的
开发一种基于深度学习的方法来在MRI上发现脑转移瘤。
研究类型
回顾性研究。
序列
轴位增强后三维T加权成像。
场强
1.5T和3T。
研究对象
共使用了121例患者的361次扫描来训练和测试基于区域的快速卷积神经网络（Faster R-CNN）：73例患者的270次扫描中的1565个病灶用于训练；48例患者的91次扫描中的488个病灶用于测试。从Faster R-CNN的48个输出中，18例患者的46次扫描中的212个病灶用于训练RUSBoost算法（MatLab），30例患者的45次扫描中的276个病灶用于测试。
评估
两名放射科医生对脑MRI上的转移瘤进行诊断并监督标注作为金标准。这些数据被用于构建一个两步流程，包括一个用于检测可能代表脑转移瘤的异常高信号的Faster R-CNN和一个用于减少检测到的假阳性病灶数量的RUSBoost分类器。
统计学检验
使用灵敏度、假阳性率和受试者工作特征（ROC）曲线来评估算法的性能。检测性能按转移瘤逐个和按切片逐个进行评估。
结果
对保留的脑MRI数据进行测试显示，灵敏度为96%，每次扫描有20个假阳性转移瘤。结果显示，每切片的灵敏度为87.1%，假阳性转移瘤为0.24个。ROC曲线下面积为0.79。
结论
我们的结果表明，基于深度学习的计算机辅助检测（CAD）有潜力以高灵敏度和合理的特异性检测脑转移瘤。
证据水平
3 技术效能阶段：2 《磁共振成像杂志》2020年；52:1227 - 1236。
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