Quantitative mapping of individual voxels in the peritumoral region of IDH-wildtype glioblastoma to distinguish between tumor infiltration and edema

• Published in: Journal of neuro-oncology Vol. 153; no. 2; pp. 251 - 261
• Main Authors: Dasgupta, Archya, Geraghty, Benjamin, Maralani, Pejman Jabehdar, Malik, Nauman, Sandhu, Michael, Detsky, Jay, Tseng, Chia-Lin, Soliman, Hany, Myrehaug, Sten, Husain, Zain, Perry, James, Lau, Angus, Sahgal, Arjun, Czarnota, Gregory J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2021; Springer Nature B.V
• Subjects: Brain cancer; Brain tumors; Clinical Study; Diffusion coefficient; Edema; Glioblastoma; Glioma; Magnetic resonance imaging; Medicine; Medicine & Public Health; Metastases; Neuroimaging; Neurology; Oncology; Radiomics; Magnetic resonance imaging (MRI); Edema; Peritumoral region; Glioblastoma multiforme (GBM); Radiomics
• ISSN: 0167-594X; 1573-7373
• DOI: 10.1007/s11060-021-03762-2

Purpose The peritumoral region (PTR) in glioblastoma (GBM) represents a combination of infiltrative tumor and vasogenic edema, which are indistinguishable on magnetic resonance imaging (MRI). We developed a radiomic signature by using imaging data from low grade glioma (LGG) (marker of tumor) and PTR of brain metastasis (BM) (marker of edema) and applied it on the GBM PTR to generate probabilistic maps. Methods 270 features were extracted from T1-weighted, T2-weighted, and apparent diffusion coefficient maps in over 3.5 million voxels of LGG (36 segments) and BM (45 segments) scanned in a 1.5T MRI. A support vector machine classifier was used to develop the radiomics model from approximately 50% voxels (downsampled to 10%) and validated with the remaining. The model was applied to over 575,000 voxels of the PTR of 10 patients with GBM to generate a quantitative map using Platt scaling (infiltrative tumor vs. edema). Results The radiomics model had an accuracy of 0.92 and 0.79 in the training and test set, respectively (LGG vs. BM). When extrapolated on the GBM PTR, 9 of 10 patients had a higher percentage of voxels with a tumor-like signature over radiological recurrence areas. In 7 of 10 patients, the areas under curves (AUC) were > 0.50 confirming a positive correlation. Including all the voxels from the GBM patients, the infiltration signature had an AUC of 0.61 to predict recurrence. Conclusion A radiomic signature can demarcate areas of microscopic tumors from edema in the PTR of GBM, which correlates with areas of future recurrence. Graphic abstract