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Abstract #0020

Fully-Automated Glioma Volumetric Segmentation and Treatment Response Assessment in MRI using Deep Learning

Ken Chang1, Andrew L Beers1, Harrison Bai2, James Brown1, K Ina Ly3, Xuejun Li4, Joeky Senders5, Vasileios Kavouridis5, Alessandro Boaro5, Chang Su6, Ena Agbodza2, Wenya Linda Bi5, Otto Rapalino3, Weihua Liao4, Qin Shen7, Hao Zhou4, Bo Xiao4, Yinyan Wang8, Paul Zhang2, Marco Pinho1, Patrick Wen9, Tracy Batchelor3, Omar Arnaout5, Bruce Rosen1, Elizabeth Gerstner3, Li Yang7, Raymond Huang5, and Jayashree Kalpathy-Cramer3

1Radiology, Massachusetts General Hospital, Boston, MA, United States, 2Hospital of the University of Pennsylvania, Philadelphia, PA, United States, 3Massachusetts General Hospital, Boston, MA, United States, 4Xiangya Hospital, Changsha, China, 5Brigham and Women’s Hospital, Boston, MA, United States, 6Yale School of Medicine, New Haven, CT, United States, 7The Second Xiangya Hospital, Changsha, China, 8Beijing Tiantan Hospital, Beijing, China, 9Dana-Farber Cancer Institute, Boston, MA, United States

Longitudinal assessment of glioma burden is important for evaluating treatment response and tumor progression. Delineation of tumor regions is typically performed manually but is time-consuming and subject to inter-rater and intra-rater variability. Therefore, there has been interest in developing automated approaches to calculate 1) glioma volume and 2) the product of maximum diameters of contrast-enhancing tumor (the key measure used in the Response Assessment for Neuro-Oncology (RANO) criteria). We present a fully automated pipeline for brain extraction, tumor segmentation, and RANO measurement (AutoRANO). We show the utility of this pipeline on 713 MRI scans from 54 post-operative glioblastoma patients, demonstrating capacity for tumor burden measurement.

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