NIMG-43. LONGITUDINAL TRACKING AND GROWTH RATE CHARACTERIZATION OF BRAIN METASTASES ON MAGNETIC RESONANCE IMAGING. (11th November 2019)

• Record Type: Journal Article
• Authors: Patel, Jay; Beers, Andrew; Chang, Ken; Brown, James; Hoebel, Katharina; Rosen, Bruce; Huang, Raymond; Brastianos, Priscilla; Gerstner, Elizabeth; Kalpathy-Cramer, Jayashree
• Is Part Of: Neuro-oncology. Volume 21(2019)Supplement 6
• Page Start: vi170
• Page End: vi171
• Publication Date: 2019-11-11
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noz175.713
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: PURPOSE: Measuring treatment response is vital for assessing efficacy of treatment regimen for patients with brain metastases (BM). Unfortunately, manual delineation of all lesions on MRI across time-points is prohibitively time-consuming, making it infeasible to track individual lesion growth/shrinkage rates as part of the clinical workflow. To overcome this challenge, we propose a deep learning approach to segment all BM, and furthermore, show that certain brain regions are more prone to high-growth rate lesions. METHODS: 163 longitudinal MRIs from 77 patients with MPRAGE-post contrast imaging protocol were prospectively obtained from Massachusetts General Hospital (MGH). An expert neuro-oncologist provided ground truth segmentations for all patients. A 3D U-Net architecture was trained to automatically segment BM; training was stopped when validation set Dice score plateaued to prevent overfitting. To enable lesion tracking, all time-points per patient were affinely registered to each other. Every lesion was subsequently classified based on its growth rate (responder: overall lesion shrinkage; inconclusive: 0% to 40% lesion growth; non-responder: more than 40% lesion growth). Characterization of global lesion growth rate patterns was accomplished by affinely registering all time-points to the MNI brain atlas. Segmented lesions were projected onto the atlas, which was qualitatively analyzed to identify spatial regions composed primarily of one class of lesion. RESULTS: For automatic segmentation, we report a mean dice score of 0.778, 0.737, and 0.704 on training, validation, and testing sets respectively. Furthermore, we find that the largest BM with the highest average growth rate (non-responders) tend to be located in the posterior frontal/parietal lobes, while smaller, lower growth rate lesions (responders) tend to be localized in the frontal lobes. The posterior fossa was found to be heterogeneous in lesion size and growth rate. CONCLUSION: We developed automatic metastatic lesion tracking over time-points and identified brain regions associated with differing growth rate lesions.