Abstract #4265

A Predictive Model for Chronic Low Back Pain Objective Diagnosis Exploiting Multi-Modal Brain [11C]-PBR28 PET/MR Radiomic Features

Angel Torrado-Carvajal¹, Daniel S Albrecht¹, Ken Chang¹, Andrew L Beers¹, Oluwaseun Akeju², Minhae Kim¹, Courtney Bergan¹, Dunkan J Hodkinson³, Robert R Edwards⁴, Yi Zhang², Jacob M Hooker¹, Vitaly Napadow¹, Jayashree Kalpathy-Cramer¹, and Marco L Loggia¹

¹Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States, ²Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, ³Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States, ⁴Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States

Chronic pain affects more than 100 million individuals in the United States alone. However, our ability to diagnose and properly treat pain disorders is currently limited, including due to the lack of reliable biomarkers. In this work, we present a predictive model for the classification of chronic low back pain (cLBP) patients using multi-modal brain [¹¹C]-PBR28 PET/MR radiomic features extracted from structural, functional, and molecular imaging. Our results suggest that a PET/MR classifier (RF_PET/MR) performs better than single-modality classifiers (RF_PET and RF_MR) for AUC (p’s<0.01), accuracy (p’s<0.01), sensitivity (p’s<0.05), and specificity (p’s<0.01), highlighting the power of multi-modal over single-modality imaging.

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