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A nomogram based on radiological features and immunoscore for predicting meningioma recurrence
1Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China.
A new nomogram accurately predicts meningioma recurrence using clinical, radiological, and immunoscore data, improving personalized treatment strategies for patients with brain tumors.
Area of Science:
- Neuro-oncology
- Medical imaging
- Immunohistochemistry
Background:
- Meningiomas are the most common primary brain tumors.
- Predicting meningioma recurrence is crucial for effective patient management.
- Current prediction methods may not fully capture all risk factors.
Purpose of the Study:
- To develop a predictive nomogram for meningioma recurrence.
- To integrate clinical, radiological, and immunoscore data.
- To enhance precision medicine in meningioma treatment.
Main Methods:
- Retrospective analysis of 109 meningioma patients.
- Assessment of CD8+ T, CD4+ T, and PD-L1 expression.
- T1C histogram analysis for tumor heterogeneity.
- Multivariate COX regression and Kaplan-Meier analyses.
Main Results:
- Immunoscore, entropy, grade, and dural tail sign identified as independent predictors.
- A nomogram was constructed with high predictive accuracy (C-indices: 0.780-0.794).
- The nomogram effectively predicts recurrence at 12, 24, and 36 months.
Conclusions:
- The developed nomogram shows significant potential for predicting meningioma recurrence.
- This tool can aid in advancing precision medicine for meningioma management.
- Improved prediction may lead to enhanced patient care and outcomes.
