Abstract #3695

Towards predicting tumor pathology with radiopathomic analysis of multi-parametric MRI in patients with newly-diagnosed gliomas

Oluwaseun Shakirat Adegbite^1,2, Nate Tran^1,2, Annette M Molinaro³, Joanna J Phillips^3,4, Jacob Ellison^1,2, Yan Li^1,2, Tracy L Luks¹, Anny Shai³, Devika Nair¹, Javier E Villanueva-Meyer¹, Mitchel S Berger³, Shawn Hervey-Jumper³, Manish Aghi³, Susan M Chang³, and Janine Lupo^1,2

¹Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²UCSF/UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, United States, ³Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, United States, ⁴Department of Pathology, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Tumors (Pre-Treatment), Tumor

Motivation: Noninvasive identification of malignant regions in glioma can help guide diagnosis and subsequent treatment planning.

Goal(s): This study aims to create models to predict and elucidate limitations in radiopathomic mapping of invasiveness in glioma using multiparametric physiologic and metabolic MRI.

Approach: A large, unique multiparametric MRI dataset with tissue is leveraged to compare various machine learning models of %ki-67 and cellularity (cells/mm²).

Results: : The best binary model achieved a CV-AUC =0.82 and CV-AUC = 0.75 for a binarized ki-67 and cellularity. Best ki-67 continuous predictions were in the 10-fold CV SVM and 4-fold ensemble model for continuous cellularity.

Impact: Multiparametric MRI can non-invasively predict histopathology. Including physiologic and/or metabolic MRI boosts histopathological predictions, however performance is also impacted by standardization of data quality.

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