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Predicting meningioma grades and pathologic marker expression via deep learning.

Jiawei Chen¹, Yanping Xue¹, Leihao Ren¹

¹Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China.

European Radiology

|October 18, 2023

Summary

This study developed a deep learning (DL) model to predict meningioma tumor grades and expression of pathologic markers. The DL model shows promise for improving preoperative diagnosis and guiding treatment decisions for meningioma patients.

Keywords:

Deep learning Magnetic resonance imaging Meningioma Radiomics

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Area of Science:

Neuro-oncology
Medical Imaging
Artificial Intelligence

Background:

Meningiomas are the most common primary intracranial tumors.
Accurate preoperative grading and prediction of pathologic markers are crucial for treatment planning and patient management.

Purpose of the Study:

To establish a deep learning (DL) model for predicting tumor grades and expression of pathologic markers in meningioma.
To evaluate the predictive performance of the DL model in internal and external validation cohorts.

Main Methods:

Retrospective analysis of 1192 meningioma patients from two institutions.
Utilized a fine-tuned ResNet50 model based on transfer learning for classification and prediction.
External validation was performed using data from a separate institution.

Main Results:

The DL model achieved high predictive performance for WHO grade and pathologic markers (Ki-67 index, H3K27me3, PR status) in the internal testing set.
The model demonstrated moderate performance in the external validation cohort, indicating potential for generalization.
Area under the curve (AUC) for WHO grade prediction was 0.966 (internal) and 0.669 (external).

Conclusions:

Deep learning models can effectively predict meningioma grades and pathologic marker expression preoperatively.
This approach can aid in identifying high-risk patients and inform surgical and follow-up strategies.
Preoperative prediction using DL is beneficial for clinical decision-making in meningioma management.