A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor

• Published in: Frontiers in neuroinformatics Vol. 16; p. 973698
• Main Authors: Shi, Jiaxin, Zhao, Zilong, Jiang, Tao, Ai, Hua, Liu, Jiani, Chen, Xinpu, Luo, Yahong, Fan, Huijie, Jiang, Xiran
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 03-08-2022; Frontiers Media S.A
• Subjects: Brain cancer; brain metastasis; Brain tumors; Breast cancer; Cancer therapies; Deep learning; Epidermal growth factor receptors; ErbB-2 protein; Image processing; Lung cancer; Magnetic resonance imaging; Medical imaging; Metastases; Metastasis; MRI; Mutation; Neuroimaging; Neuroscience; Non-small cell lung carcinoma; NSCLC; Patients; Regression analysis; Tumors
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2022.973698

Purpose: To propose a deep learning network with subregion partition for predicting metastatic origins and EGFR/HER2 status in patients with brain metastasis. Methods: We retrospectively enrolled 140 patients with clinico-pathologically confirmed brain metastasis originated from primary NSCLC (n=60), breast cancer (BC, n=60) and other tumor types (n=20). All patients underwent contrast-enhanced brain MRI scans. The brain metastasis was subdivided into phenotypically consistent subregions using patient-level and population-level clustering. A residual network with a global average pooling layer (RN-GAP) was proposed to calculate deep learning-based features. Features from each subregion were selected with least absolute shrinkage and selection operator (LASSO) to build logistic regression models (LRs) for predicting primary tumor types (LR-NSCLC for the NSCLC origin and LR-BC for the BC origin), EGFR mutation status (LR-EGFR) and HER2 status (LR-HER2). Results: The brain metastasis can be partitioned into a marginal subregion (S1) and an inner subregion (S2) in the MRI image. The developed models showed good predictive performance in the training (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.860 vs. 0.909 vs. 0.850 vs. 0.900) and validation (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.819 vs. 0.872 vs. 0.750 vs. 0.830) set. Conclusion: Our proposed deep learning network with subregion partitions can accurately predict metastatic origins and EGFR/HER2 status of brain metastasis, and hence may have the potential to be non-invasive and preoperative new markers for guiding personalized treatment plans in patients with brain metastasis.