NIMG-11. DIFFERENTIATING TREATMENT-INDUCED EFFECTS FROM TRUE RECURRENT HIGH GRADE GLIOMA USING MULTIPARAMETRIC MRI TECHNIQUES. (5th November 2018)

• Record Type: Journal Article
• Authors: Cluceru, Julia; Nelson, Sarah; Molinaro, Annette; Phillips, Joanna J; Olson, Beck; Lafontaine, Marisa; Jakary, Angela; Nair, Devika; Chang, Susan; Alcaide-Leon, Paula; Berger, Mitchel; Lupo, Janine
• Is Part Of: Neuro-oncology. Volume 20(2018)Supplement 6
• Page Start: vi177
• Page End: vi178
• Publication Date: 2018-11-05
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noy148.738
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Recurrent high grade gliomas (rHGG) are difficult to diagnose accurately as they are often confounded with treatment-induced effects (TxE) because on conventional T1w and T2w anatomical MR imaging these two phenomena appear identical. This could result in removing a patient from an effective second-line therapy, confound the results of a clinical trial of a new therapeutic, and expose a patient to unnecessary surgical intervention. Advanced MR imaging modalities have shown promise in distinguishing between TxE and rHGG, but prior studies are unable to account for heterogeneity within the same lesion and often lack pathological confirmation. The goal of this study was to identify imaging parameters that could spatially map directly to pathology to account for heterogeneity within lesions. 1–8 tissue samples were collected upon surgical resection of suspected rHGG. A total of 484 samples were collected from 183 patients; 332 of these (163 patients) were labeled as either pathologically confirmed treatment effect or having no tumor cells (tumor score=0) within the imaging abnormality (TxE; 80/51 samples/patients) or rHGG (tumor score=2–3; 252/118 samples/patients) by a pathologist. Preoperative 3T MRI scans included T1w-Gd and T2w FLAIR; diffusion tensor imaging (b=1000s/mm2); 3D MRSI; and dynamic susceptibility-contrast perfusion MRI. Univariate models were created using generalized estimating equations to evaluate each variable's ability to distinguish TxE from rHGG tissue samples. Normalized choline (median=1.1 TxE & 1.2 rHGG), choline-to-NAA index (CNI, median=2.4 TxE & 3.5 rHGG), and normalized cerebral blood volume (nCBV, median=1.2 TxE & 1.4 rHGG) were significantly associated with biopsy-level classification (p=0.034, 0.015, 0.017). These results are being consolidated into a multiparametric algorithm for predicting TxE or rHGG, which could be of clinical importance in managing patients with recurrent rHGG. Downstream analysis will create a spatial map of rHGG to help guide future surgical sampling.