• Omaditya Khanna, Anahita Fathi Kazerooni, Christopher J Farrell, Michael P Baldassari, Tyler D Alexander, Michael Karsy, Benjamin A Greenberger, Jose A Garcia, Chiharu Sako, James J Evans, Kevin D Judy, David W Andrews, Adam E Flanders, Ashwini D Sharan, Adam P Dicker, Wenyin Shi, and Christos Davatzikos.
• Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA.
• Neurosurgery. 2021 Oct 13; 89 (5): 928-936.

BackgroundAlthough World Health Organization (WHO) grade I meningiomas are considered "benign" tumors, an elevated Ki-67 is one crucial factor that has been shown to influence tumor behavior and clinical outcomes. The ability to preoperatively discern Ki-67 would confer the ability to guide surgical strategy.ObjectiveIn this study, we develop a machine learning (ML) algorithm using radiomic feature analysis to predict Ki-67 in WHO grade I meningiomas.MethodsA retrospective analysis was performed for a cohort of 306 patients who underwent surgical resection of WHO grade I meningiomas. Preoperative magnetic resonance imaging was used to perform radiomic feature extraction followed by ML modeling using least absolute shrinkage and selection operator wrapped with support vector machine through nested cross-validation on a discovery cohort (n = 230), to stratify tumors based on Ki-67 <5% and ≥5%. The final model was independently tested on a replication cohort (n = 76).ResultsAn area under the receiver operating curve (AUC) of 0.84 (95% CI: 0.78-0.90) with a sensitivity of 84.1% and specificity of 73.3% was achieved in the discovery cohort. When this model was applied to the replication cohort, a similar high performance was achieved, with an AUC of 0.83 (95% CI: 0.73-0.94), sensitivity and specificity of 82.6% and 85.5%, respectively. The model demonstrated similar efficacy when applied to skull base and nonskull base tumors.ConclusionOur proposed radiomic feature analysis can be used to stratify WHO grade I meningiomas based on Ki-67 with excellent accuracy and can be applied to skull base and nonskull base tumors with similar performance achieved.© Congress of Neurological Surgeons 2021.

16 keywords...

hide…