NIMG-61. IMPROVED GENERALIZABILITY OF RADIOPATHOMIC PROBABILISTIC MAPPING OF TREATMENT-INDUCED EFFECTS WITH PHYSIOLOGIC MR IMAGING AND DEEP LEARNING IN PATIENTS WITH RECURRENT GLIOBLASTOMA

Media type: E-Article

Title: NIMG-61. IMPROVED GENERALIZABILITY OF RADIOPATHOMIC PROBABILISTIC MAPPING OF TREATMENT-INDUCED EFFECTS WITH PHYSIOLOGIC MR IMAGING AND DEEP LEARNING IN PATIENTS WITH RECURRENT GLIOBLASTOMA

Contributor: Ellison, Jacob; Tran, Nate; Molinaro, Annette; Pedoia, Valentina; Phillips, Joanna J; Shai, Anny; Nair, Devika; Lafontaine, Marisa; Jakary, Angela; Luks, Tracy; Villanueva-Meyer, Javier; Chang, Susan M; Berger, Mitchel S; Hervey-Jumper, Shawn L; Aghi, Manish; Lupo, Janine

imprint: Oxford University Press (OUP), 2022

Language: English

DOI: 10.1093/neuonc/noac209.679

ISSN: 1522-8517; 1523-5866

Keywords: Cancer Research ; Neurology (clinical) ; Oncology

Origination:

Footnote:

Description: Abstract Although physiologic (diffusion-weighted and perfusion-weighted) MRI has shown promise in identifying regions of recurrent tumor (rTumor) in patients with glioblastoma suspected of progression, distinguishing treatment-induced effects (TxE) from rTumor on anatomical MRI remains a challenge. Whereas prior larger-scale machine learning (ML)-based studies mostly utilize anatomical imaging alone and/or perform lesion-level predictions, this study aimed to develop a non-invasive, radiopathomic tool for regional probabilistic mapping of TxE using 208 tissue-samples (55 pathologically-confirmed TxE, 153 recurrent glioblastoma) acquired from 107 patients with known spatial coordinates on pre-surgical MRI. We tested the hypothesis that applying a deep-learning (DL) model that included physiological MRI can: 1) more accurately identify areas of TxE that mimic rTumor on anatomical MRI and 2) better generalize to an independent test set than ML-models or a DL-model that uses anatomical MRI alone. An 80/20 split for training/validation was used after 1/3 of the patients were withheld for testing. Oversampling of TxE samples was employed to address class imbalance and an equal proportion of TxE samples was maintained across all datasets. Three ML-models, their ensemble, and a deep 4D-convolutional-neural-network were trained based on normalized anatomical (post-contrast T1, T2-FLAIR), diffusion-weighted (ADC, FA), and DSC perfusion-weighted (PeakHeight, %recovery) images cropped to 10mm-cubic patches centered on the coordinates from where tissue was obtained. Although Random Forest and voting-ensembled ML-models using all imaging and the anatomical DL-model had the best validation performance (AUC=0.81-0.82), these models did not generalize (test AUC=0.58-0.59). The DL-model including physiologic images had slightly lower validation AUC (0.78) but the best overall test AUC (0.795), indicating superior generalizability. Elevated blood volume (nPeakHeight) was the most important feature. Our DL-model’s interpretability was also demonstrated by disrupting class separation after shuffling voxels within each input patch. These results suggest that using deep-learning with physiologic MRI can improve intratumoral classification of TxE from rTumor.

Access State: Open Access

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