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Exploring a recurrence model for atypical meningioma based on multiparametric MRI radiomic and clinical
Dengpan Song1, Qingjie Wei2, Shengqi Zhao3
1Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China.
Objectives:
Atypical meningioma (AM) has a high recurrence rate. This study explored the factors associated with recurrence and built a predictive model for AM by combining radiomic and clinical features.
Methods:
This retrospective cohort study enrolled 451 adult AM patients who underwent surgical treatment at three institutions between May 2012 and April 2024. The patients in institution 1 were randomly assigned to the training dataset (n = 246) or internal validation dataset (n = 164) at a ratio of 6:4, and patients in institutions 2 and 3 composed the external validation dataset (n = 41). The clinical and pathological characteristics of the patients were collected, and radiomics technology was used to extract image features from preoperative multiparametric MR images. After feature screening, three types of AM recurrence prediction models were constructed: the radiomic model, the clinical model and the combined model.
Results:
The median follow-up time was 27 months, and 22.8% (n = 103) of the patients relapsed after surgery. A total of 23 radiomic features were included in the model. Compared with the radiomic model and clinical model, the combined model performed better in predicting recurrence, with C-index values of 0.8453, 0.7867 and 0.8125 in the training, internal validation and external validation datasets, respectively, and the AUC value remained above 0.85 within 5 years. The radiomics score plays the most important role in predicting the recurrence of AM, with features such as t1c_original_shape_MajorAxisLength and t1c_original_shape_Flatness being of high importance. Among the clinical features, secondary tumors, subtotal resection and high Ki-67 levels contribute to AM recurrence.
Conclusions:
Radiomics has additional value for predicting AM tumor recurrence and has favorable predictive performance when combined with clinical features.
Insights
A new combined model using radiomics and clinical data improves prediction of atypical meningioma (AM) recurrence after surgery. This approach offers valuable insights for managing AM, a tumor with a high recurrence rate.
Area of Science:
- Neurosurgery
- Radiology
- Oncology
Background:
- Atypical meningioma (AM) is associated with a significant risk of recurrence following surgical treatment.
- Accurate prediction of AM recurrence is crucial for effective patient management and treatment planning.
Purpose of the Study:
- To investigate factors influencing AM recurrence.
- To develop and validate a predictive model for AM recurrence by integrating radiomic and clinical features.
Main Methods:
- A retrospective cohort study included 451 adult AM patients from three institutions.
- Radiomics features were extracted from preoperative multiparametric MRI, alongside clinical and pathological data.
- Three models (radiomic, clinical, combined) were constructed and validated internally and externally.
Main Results:
- The combined model demonstrated superior predictive performance for AM recurrence compared to radiomic or clinical models alone (C-index up to 0.8453).
- Radiomics score, particularly features like MajorAxisLength and Flatness, was a key predictor.
- Clinical factors such as secondary tumors, subtotal resection, and high Ki-67 levels were associated with increased recurrence risk.
Conclusions:
- Radiomics provides valuable supplementary information for predicting AM recurrence.
- Combining radiomic and clinical features yields a robust and favorable predictive model for AM recurrence.
