Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki‐67 in World Health Organization Grade I Meningiomas. Issue 5 (November 2021)

• Record Type: Journal Article
• Authors: Khanna, Omaditya; Kazerooni, Anahita Fathi; Farrell, Christopher J; Baldassari, Michael P; Alexander, Tyler D; Karsy, Michael; Greenberger, Benjamin A; Garcia, Jose A; Sako, Chiharu; Evans, James J; Judy, Kevin D; Andrews, David W; Flanders, Adam E; Sharan, Ashwini D; Dicker, Adam P; Shi, Wenyin; Davatzikos, Christos
• Is Part Of: Neurosurgery. Volume 89:Issue 5(2021)
• Publication Date: 2021-11
• Subjects: Artificial intelligence -- Machine learning -- Meningioma -- Radiomics; Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1093/neuros/nyab307
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract : BACKGROUND: : Although 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. OBJECTIVE: : In this study, we develop a machine learning (ML) algorithm using radiomic feature analysis to predict Ki‐67 in WHO grade I meningiomas. METHODS: : A 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). RESULTS: : An 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. CONCLUSION: : Our 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.