NIMG-82. DEVELOPMENT AND VALIDATION OF AN MRI-BASED DEEP LEARNING MODEL TO DIFFERENTIATE IDH -WILDTYPE GLIOBLASTOMA AND TUMEFACTIVE MULTIPLE SCLEROSIS

Clinical management of IDH wildtype glioblastoma (GBM) and tumefactive multiple sclerosis (tMS) is drastically different. GBM requires maximal safe resection followed by chemoradiation, while tMS outcome is worsened by surgery and radiotherapy. Noninvasive methods are needed to help with accurate di...

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Published in:Neuro-oncology (Charlottesville, Va.) Va.), 2024-11, Vol.26 (Supplement_8), p.viii214-viii214
Main Authors: Marco Conte, Gian, Moassefi, Mana, Decker, Paul, Kosel, Matthew, McCarthy, Christina, Fereidan, Mah, Nikanpour, Yalda, Pump, Heather, Jenkins, Robert, Lachance, Daniel, Erickson, Bradley, Tobin, W Oliver, Eckel-Passow, Jeanette
Format: Article
Language:English
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Summary:Clinical management of IDH wildtype glioblastoma (GBM) and tumefactive multiple sclerosis (tMS) is drastically different. GBM requires maximal safe resection followed by chemoradiation, while tMS outcome is worsened by surgery and radiotherapy. Noninvasive methods are needed to help with accurate diagnosis of tumor and non-tumor etiologies. To develop an MRI-based classification model, tMS subjects diagnosed prior to January 1, 2020, were matched to tMS by age at diagnosis, sex, index MRI date, and 2D/3D acquisition. Inclusion criteria included one cm minimal lesion size and pre-operative post-contrast T1 and T2 images available for analysis. A 3D-DenseNet121 was used to develop a classification model using prespecified parameters: 650 epochs, batch size 16, learning rate 10-3, cross-entropy loss, and AdamW optimizer. The stopping rule was defined as three sequential differences in epoch cross-entropy loss
ISSN:1522-8517
1523-5866
DOI:10.1093/neuonc/noae165.0846