Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Journal of magnetic resonance imaging : JMRI

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 T1 -weighted imaging.


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.


Zhang Min, Young Geoffrey S, Chen Huai, Qin Lei, McFaline-Figueroa J Ricardo, Reardon David A, Cao Xinhua, Wu Xian, Xu Xiaoyin


Faster R-CNN, RUSBoost, brain metastases, deep learning