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MRI-based prediction of DNA methylation grade in IDH-mutant astrocytomas using qualitative imaging features and tumor
Kanwar Partap Bir Singh1, Matthew D Lee2, Matthew G Young2
1Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States. kanwar.singh@nyulangone.org.
Purpose:
Histopathological grading of IDH-mutant astrocytomas demonstrates limited prognostic accuracy. However, DNA methylation subclassification has demonstrated improved prognostication beyond histological grading. This study aimed to investigate the associations between imaging features, tumor volumetric data, and DNA methylation grade in IDH-mutant astrocytomas.
Methods:
We analyzed imaging features and volumetric data for 72 patients diagnosed with IDH-mutant astrocytomas, who underwent preoperative MRI and DNA methylation profiling. VASARI features and multicompartmental volumetrics were evaluated. Logistic regression was used to identify imaging predictors of methylation subclass, WHO histologic grade, copy number variation (CNV), and CDKN2A/B homozygous deletion. Univariable and multivariable Cox proportional hazard models were also developed to assess these variables' influence on overall survival and progression-free survival.
Results:
Patients were classified into 27 methylation high-grade (A_IDH_HG) and 45 methylation low-grade (A_IDH_LG) tumors. Tumor volumes and proportions varied by methylation grade, CNV status, and WHO histologic grade, but not by CDKN2A/B status. Imaging features distinguished methylation subclasses with 75% accuracy (AUC = 0.77). Methylation high-grade subclass was associated with imaging features such as midline crossing, ependymal extension, and poorly defined enhancing margins. Predictive performance for WHO histologic grade, CNV status, and CDKN2A/B deletion was moderate (AUC = 0.67, 0.69, and 0.65, respectively). Methylation grade, CDKN2A/B status, VASARI features, and proportions of edema and non-contrast enhancing tumor were significantly associated with survival.
Conclusion:
MRI-derived imaging features facilitate noninvasive prediction of DNA methylation subclass in IDH-mutant astrocytomas.
Insights
Magnetic Resonance Imaging (MRI) features can predict DNA methylation subclasses in IDH-mutant astrocytomas. This noninvasive approach improves prognostication beyond traditional histological grading for these brain tumors.
Area of Science:
- Neuro-oncology
- Radiology
- Molecular Pathology
Background:
- Histopathological grading of IDH-mutant astrocytomas has limited prognostic value.
- DNA methylation subclassification offers improved prognostication compared to histological grading.
- Accurate grading is crucial for effective treatment strategies in brain tumors.
Purpose of the Study:
- To investigate the association between imaging features, tumor volumetric data, and DNA methylation grade in IDH-mutant astrocytomas.
- To determine if MRI can noninvasively predict DNA methylation subclasses.
- To correlate imaging and methylation data with patient survival outcomes.
Main Methods:
- Analysis of preoperative MRI and DNA methylation profiling data from 72 patients with IDH-mutant astrocytomas.
- Evaluation of VASARI features and multicompartmental volumetrics.
- Logistic regression and Cox proportional hazard models to identify predictors of methylation subclass, WHO grade, CNV, CDKN2A/B deletion, and survival.
Main Results:
- Imaging features distinguished methylation subclasses with 75% accuracy (AUC=0.77).
- High-grade methylation subclass was associated with specific imaging features like midline crossing and ependymal extension.
- Methylation grade, CDKN2A/B status, VASARI features, and tumor proportions significantly impacted survival.
Conclusions:
- MRI-derived imaging features can noninvasively predict DNA methylation subclass in IDH-mutant astrocytomas.
- This imaging-based approach enhances the prognostic accuracy for brain tumors.
- Integrating imaging and molecular data may optimize personalized treatment strategies.
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