Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization.
Neurooncol Adv
; 6(1): vdae043, 2024.
Article
in En
| MEDLINE
| ID: mdl-38596719
ABSTRACT
Background:
This study investigates the influence of diffusion-weighted Magnetic Resonance Imaging (DWI-MRI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability.Methods:
Radiomic features, compliant with image biomarker standardization initiative 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-wild type). Four approaches were compared Anatomical, anatomicalâ +â ADC naive, anatomicalâ +â ADC N4, and anatomicalâ +â ADC N4/z-score. The University of California San Francisco (UCSF)-glioma dataset (nâ =â 409) was used for external validation.Results:
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 (Pâ =â .011) for the IDH-wild-type subgroup, but not for the other 2 glioma subgroups (Pâ >â .05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wild-type subgroup (Pâ ≤â .001) 0.80 (N4/WS anatomical alone), 0.81 (anatomicalâ +â ADC naive), 0.81 (anatomicalâ +â ADC N4), and 0.88 (anatomicalâ +â ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup (Pâ <â .012 each).Conclusions:
ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wild-type glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Neurooncol Adv
Year:
2024
Document type:
Article
Affiliation country:
Germany
Country of publication:
United kingdom