A radiomics model enables prediction venous sinus invasion in meningioma.

Abstract

Preoperative prediction of meningioma venous sinus invasion would facilitate the selection of surgical approaches and predicting the prognosis. To predict venous sinus invasion in meningiomas, we used radiomic signatures to construct a model based on preoperative contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging. In total, 599 patients with pathologically confirmed meningioma were retrospectively enrolled. For each patient enrolled in this study, 1595 radiomic signatures were extracted from T1C and T2 image sequences. Pearson correlation analysis and recursive feature elimination were used to select the most relevant signatures extracted from different image sequences, and logistic regression algorithms were used to build a radiomic model for risk prediction of meningioma sinus invasion. Furthermore, a nomogram was built by incorporating clinical characteristics and radiomic signatures, and a decision curve analysis was used to evaluate the clinical utility of the nomogram. Twenty radiomic signatures that were significantly related to venous sinus invasion were screened from 3190 radiomic signatures. Venous sinus invasion was associated with tumor position, and the clinicoradiomic model that incorporated the above characteristics (20 radiomic signatures and tumor position) had the best discriminating ability. The areas under the curve for the training and validation cohorts were 0.857 (95% confidence interval [CI], 0.824-0.890) and 0.824 (95% CI, 0.752-0.8976), respectively. The clinicoradiomic model had good predictive performance for venous sinus invasion in meningioma, which can aid in devising surgical strategies and predicting prognosis.