Advancing non-invasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization release_eacslw5vajandm4ku45nkj23wu

by Martha Foltyn-Dumitru, Marianne Schell, Felix Sahm, Tobias Kessler, Wolfgang Wick, Martin Bendszus, Aditya Rastogi, Gianluca Brugnara, Philipp Kickingereder

Published in Neuro-Oncology Advances by Oxford University Press (OUP).

2024   Volume 6, Issue 1, vdae043

Abstract

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> This study investigates the influence of diffusion-weighted MRI (DWI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability. </jats:sec> <jats:sec> <jats:title>Methods</jats:title> Radiomic features, compliant with IBSI standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted, IDH-mutant 1p/19q non-codeleted, IDH-wildtype). Four approaches were compared: anatomical, anatomical + ADC naive, anatomical + ADC N4, and anatomical + ADC N4/z-score. The UCSF-glioma dataset (n=409) was used for external validation. </jats:sec> <jats:sec> <jats:title>Results</jats:title> Naïve-Bayes algorithms yielded overall the best performance on the internal test-set. Adding ADC radiomics significantly improved AUC from 0.79 to 0.86 (p= .011) for the IDH-wildtype subgroup, but not for the other two glioma subgroups (p&amp;gt;0.05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wildtype subgroup (p≤ .001): 0.80 (N4/WS anatomical alone), 0.81 (anatomical + ADC naive), 0.81 (anatomical + ADC N4) and 0.88 (anatomical + ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup (p&amp;lt; .012 each). </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wildtype glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data. </jats:sec>
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