NIMG-65. IMPROVED SPATIAL MAPPING OF TUMOR AGGRESSIVENESS WITH 1H MAGNETIC RESONANCE SPECTROSCOPY AND DEEP LEARNING IN PATIENTS WITH NEWLY-DIAGNOSED GLIOMA. (14th November 2022)

• Record Type: Journal Article
• Authors: Tran, Nate; Li, Yan; Ellison, Jacob; Adegbite, Oluwaseun; Phillips, Joanna J; Molinaro, Annette; Pedoia, Valentina; Shai, Anny; Nair, Devika; Jakary, Angela; Lafontaine, Marisa; Villanueva-Meyer, Javier; Berger, Mitchel S; Hervey-Jumper, Shawn L; Aghi, Manish; Chang, Susan M; Lupo, Janine
• Is Part Of: Neuro-oncology. Volume 24(2022)Supplement 7
• Page Start: vii179
• Page End: vii179
• Publication Date: 2022-11-14
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noac209.683
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: INTRODUCTION: Noninvasive, radiopathomic mapping of tumor aggressiveness can benefit patients with glioma by guiding the selection of tissue samples for diagnosis, increasing extent of resection, and non-invasively characterizing residual tumor burden for subsequent treatment. Although prior studies have demonstrated the utility of metabolic metrics quantified from 1 H-MR Spectroscopy (MRS) in probing tumor pathology, this study evaluated the benefit of using the entire 1D-spectrum and deep learning for radiopathomic mapping of intratumoral cellularity, proliferation (ki-67), and a new tumor aggressiveness index (TAI) defined as log((n(ki−67)+n(cellularity))*tumor-score). METHODS: Multi-voxel 1 H-MRS was acquired on 281 patients newly diagnosed with a glioma (47% IDH-wildtype) immediately before surgical resection. After reconstructing individual spectra at the locations where tissue samples were obtained during surgery and normalizing by NAA in contralateral normal-appearing-white-matter, 607 spectra with corresponding histopathology were deemed of sufficient quality for analysis. A 1D convolutional-neural-network with bidirectional long- and short-term memory deep-learning model using the entire spectrum (0.6-3.6ppm) was compared to mixed-effects regression (with choline-to-NAA index[CNI]) and Random Forest (with CNI+normalized peak heights) models for predicting ki-67, cellularity, and TAI. Results & DISCUSSION: Using deep-learning on the entire spectrum resulted in 10.3%-22.1% lower mean absolute error (MAE) and 0.32-0.37 higher R 2 values compared to using CNI alone or a random forest model with multiple metabolic metrics. MAE values for all 3 deep-learning models were 26-44% < 1 standard deviation of the ground truth, demonstrating reasonable prediction accuracy within the test data set. Although the lowest MAE (0.16) and highest R 2 (0.41) was attained when predicting TAI with deep-learning, the prediction of cellularity resulted in the lowest %MAE. Colormaps of predicted pathology identified regions of heightened aggressiveness surrounding tissue samples with most abnormal pathological features that sometimes extended beyond the non-enhancing lesion. Current work is evaluating the clinical utility of our deep-learning model and predicted maps of aggressiveness.