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
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&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&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>
In application/xml+jats
format
Archived Files and Locations
application/pdf
892.8 kB
file_j2zncikmszfdnct5hfksbajwhy
|
watermark.silverchair.com (publisher) web.archive.org (webarchive) |
access all versions, variants, and formats of this works (eg, pre-prints)
Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar