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Abstract #5026

Longitudinal voxel-wise analysis using a novel deep-learning-derived KI-67 map for early prediction of glioblastoma outcome

Nate Tran1, Tracy Luks1, Jacob Ellison1, Yan Li1, Annette Molinaro2, Devika Nair1, Angela Jakary1, Harshita Kukreja1, Sana Varizi1, Bo Liu1, Oluwaseun Adegbite1, Javier Villanueva-Meyer1, Nicholas Butowski2, Jennifer Clarke2, Susan Chang2, Hui Lin3, and Janine Lupo1
1Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 2Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States, 3Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Tumors (Pre-Treatment), Brain

Motivation: Early prediction of prognosis, treatment response, and overall-survival is imperative for adapting appropriate treatment strategies for GBM patients.

Goal(s): To predicting outcome and tumor progression using early imaging changes in underlying tumor pathology

Approach: We performed a longitudinal voxel-based analysis of metabolic and proliferation changes during the course of radiation therapy in terms of their ability to predict progression and survival compared to anatomical tumor volumes.

Results: Voxel-wise subtraction (MidRT-PreRT) of proliferation values was the most correlated with progression free (r=0.54/p=0.004) and overall (r=0.66/p=0.0002) survival, exceeding both tumor volume and CNI. Adding imaging-derived metrics of proliferation mid-RT can improve the prediction of progression.

Impact: Novel imaging-based proliferation maps and voxel-wise analyses show much stronger correlation with progression-free and overall-survival and regions of subsequent progression compared to conventional imaging-markers. These features can potentially aid in early prediction of response, and outcome of patients with GBM.

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Keywords