Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas. Issue 5 (30th August 2021)

• Record Type: Journal Article
• Authors: Khanna, Omaditya; Fathi Kazerooni, Anahita; 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)
• Page Start: 928
• Page End: 936
• Publication Date: 2021-08-30
• 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.