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Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging
Junjie Li1, Peng Zhang2, Liying Qu1
1Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Background:
Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG.
Purpose:
This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images.
Study Type:
Retrospective and prospective.
Population:
For diffuse midline brain gliomas, 341 patients from Center-1 (27 ± 19 years, 184 males), 42 patients from Center-2 (33 ± 19 years, 27 males) and 35 patients (37 ± 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 ± 15 years, 80 males).
Field Strength/Sequence:
5T and 3T, T2-weighted turbo spin echo imaging.
Assessment:
Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve.
Statistical Tests:
Pearson's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value <0.05 was considered statistically significant.
Results:
In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specificities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%.
Data Conclusion:
In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making.
Evidence Level:
2 Technical Efficacy: Stage 2.
Insights
A deep learning model accurately predicts H3 K27M mutations in diffuse midline gliomas using T2-weighted MRI. This noninvasive approach aids in prognosis and clinical trial stratification for H3 K27M mutant DMG.
Area of Science:
- Neuro-oncology
- Medical imaging
- Artificial intelligence in medicine
Background:
- H3 K27M mutation is crucial for diffuse midline glioma (DMG) prognosis and clinical trial stratification.
- MRI offers noninvasive insights into the morphological and metabolic traits of H3 K27M mutant DMG.
Purpose of the Study:
- To develop a deep learning (DL) model for noninvasive prediction of H3 K27M mutation in DMG using T2-weighted MRI.
- To assess the model's performance in internal and external validation cohorts.
Main Methods:
- A retrospective and prospective study involving 341 (Center-1), 42 (Center-2), and 35 patients with diffuse midline brain gliomas, and 133 patients with diffuse spinal cord gliomas.
- T2-weighted turbo spin echo imaging at 5T and 3T field strengths.
- DL model development for tumor segmentation and H3 K27M mutation prediction, validated using Dice coefficient, accuracy, sensitivity, and specificity.
Main Results:
- The DL model achieved high segmentation performance (Dice 0.87 for brain, 0.81 for spinal cord gliomas).
- Internal prospective testing showed high predictive accuracy (92.1% for brain, 85.4% for spinal cord gliomas).
- The model demonstrated robust generalization to external institutions with accuracies ranging from 85.7% to 90.5%.
Conclusions:
- An automated DL framework effectively predicts H3 K27M mutation status from T2-weighted MRI.
- This noninvasive approach can aid clinical decision-making for DMG patients.
