NIMG-46. TOWARDS PREDICTING TUMOR AGGRESSIVENESS WITH RADIOPATHOMIC ANALYSIS OF MULTI-PARAMETRIC ANATOMICAL, DIFFUSION-WEIGHTED, AND METABOLIC MRI IN PATIENTS WITH NEWLY-DIAGNOSED GLIOMAS

Autor: Oluwaseun Adegbite, Nate Tran, Annette Molinaro, Joanna J Phillips, Jacob Ellison, Yan Li, Tracy Luks, Anny Shai, Devika Nair, Valentina Pedoia, Javier Villanueva-Meyer, Mitchel S Berger, Shawn L Hervey-Jumper, Manish Aghi, Janine Lupo
Rok vydání: 2022
Předmět:
Zdroj: Neuro-Oncology. 24:vii173-vii174
ISSN: 1523-5866
1522-8517
DOI: 10.1093/neuonc/noac209.664
Popis: INTRODUCTION Pathologically aggressive tumor biology can extend beyond the contrast-enhancing or non-enhancing anatomical lesions in patients with glioma. Identification of malignant regions can help guide diagnosis and subsequent treatment planning. This study leverages a unique multi-parametric MRI dataset with tissue samples of known spatial coordinates to noninvasively predict cellular proliferation (KI-67) and a novel index of tumor aggressiveness (TAI), that combines proliferation, cellularity, and tumor-score. METHODS 420 tissue samples were collected from 162 patients with newly-diagnosed glioma (47% IDH-wildtype). Clinical imaging consisted of T2-weighted, T2-FLAIR, T1-weighted pre- and post-contrast images, and apparent diffusion coefficient (ADC) and fractional anisotropy (FA) from diffusion-weighted imaging. Mean normalized imaging metrics were quantified from 5mm spheres centered at the location of the tissue sample. A single spectrum was reconstructed at the location of each tissue sample from 3D 1H-MR Spectroscopic Imaging (MRSI) before quantifying normalized metabolite peak-heights for choline, creatine, NAA, lactate/lipid, and relative indices. Univariate mixed-effects linear regression models were employed and features with p< 0.2 were selected for subsequent model building. Support vector machine (SVM), random forest, and gradient boosting machine-learning algorithms were trained and tested on a ⅔-⅓ train-test split with 4-fold cross-validation in training to predict a high/low KI-67 and TAI. RESULTS Although none of the individual imaging metrics were significantly associated with KI-67 in the univariate analysis, all diffusion and several MRSI metrics (ncholine, nNAA, CNI, excess choline and creatine) were significantly associated with cellularity. Preliminary multivariate analyses to date suggest that the best radiopathomic model performance is achieved when an SVM was used along with T1-precontrast, nADC, and all metabolite levels (mean cross-validation AUC=0.73 and accuracy=.77). CONCLUSION Our results suggest that multi-parametric physiologic and metabolic MRI are useful for radiopathomic-mapping of tumor aggressiveness and are currently being optimized in a larger cohort.
Databáze: OpenAIRE
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