Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI.
Jae Hyun ParkLe Thanh QuangWoong YoonByung Hyun BaekIlwoo ParkSeul Kee KimPublished in: Biomedicines (2023)
Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively reviewed patients with intracranial meningiomas from two hospitals. Preoperative MRIs were analyzed for tumor and edema volumes, enhancement patterns, margins, and tumor-brain interfaces. Radiomics features were extracted, and machine learning models were employed to predict meningioma grades. A total of 212 patients were included. In the training group (Hospital 1), significant differences were observed between low-grade and high-grade meningiomas in terms of tumor volume ( p = 0.012), edema volume ( p = 0.004), enhancement ( p = 0.001), margin ( p < 0.001), and tumor-brain interface ( p < 0.001). Five radiomics features were selected for model development. The prediction model for radiomics features demonstrated an average validation accuracy of 0.74, while the model for clinical imaging features showed an average validation accuracy of 0.69. When applied to external test data (Hospital 2), the radiomics model achieved an area under the receiver operating characteristics curve (AUC) of 0.72 and accuracy of 0.69, while the clinical imaging model achieved an AUC of 0.82 and accuracy of 0.81. An improved performance was obtained from the model constructed by combining radiomics and clinical imaging features. In the combined model, the AUC and accuracy for meningioma grading were 0.86 and 0.73, respectively. In conclusion, this study demonstrates the potential value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas. The combination of both radiomics and clinical imaging features achieved the highest AUC among the models. Therefore, the combined model of radiomics and clinical imaging features may offer a more effective tool for predicting clinical outcomes in meningioma patients.
Keyphrases
- high resolution
- contrast enhanced
- low grade
- machine learning
- high grade
- lymph node metastasis
- end stage renal disease
- chronic kidney disease
- healthcare
- newly diagnosed
- emergency department
- ejection fraction
- multiple sclerosis
- squamous cell carcinoma
- brain injury
- prognostic factors
- artificial intelligence
- fluorescence imaging
- peritoneal dialysis
- photodynamic therapy
- optical coherence tomography
- resting state
- white matter
- electronic health record
- adverse drug