Radiomic signatures of meningiomas using the Ki-67 proliferation index as a prognostic marker of clinical outcomes
Author:
Khanna Omaditya1, Fathi Kazerooni Anahita23, Arif Sherjeel23, Mahtabfar Aria1, Momin Arbaz A.1, Andrews Carrie E.1, Hafazalla Karim1, Baldassari Michael P.1, Velagapudi Lohit1, Garcia Jose A.23, Sako Chiharu23, Farrell Christopher J.1, Evans James J.1, Judy Kevin D.1, Andrews David W.1, Flanders Adam E.4, Shi Wenyin5, Davatzikos Christos23
Affiliation:
1. Departments of Neurological Surgery and 2. Center for Biomedical Image Computing and Analytics and 3. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; and 4. Radiology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania; 5. Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania
Abstract
OBJECTIVE
The clinical behavior of meningiomas is not entirely captured by its designated WHO grade, therefore other factors must be elucidated that portend increased tumor aggressiveness and associated risk of recurrence. In this study, the authors identify multiparametric MRI radiomic signatures of meningiomas using Ki-67 as a prognostic marker of clinical outcomes independent of WHO grade.
METHODS
A retrospective analysis was conducted of all resected meningiomas between 2012 and 2018. Preoperative MR images were used for high-throughput radiomic feature extraction and subsequently used to develop a machine learning algorithm to stratify meningiomas based on Ki-67 indices < 5% and ≥ 5%, independent of WHO grade. Progression-free survival (PFS) was assessed based on machine learning prediction of Ki-67 strata and compared with outcomes based on histopathological Ki-67.
RESULTS
Three hundred forty-three meningiomas were included: 291 with WHO grade I, 43 with grade II, and 9 with grade III. The overall rate of recurrence was 19.8% (15.1% in grade I, 44.2% in grade II, and 77.8% in grade III) over a median follow-up of 28.5 months. Grade II and III tumors had higher Ki-67 indices than grade I tumors, albeit tumor and peritumoral edema volumes had considerable variation independent of meningioma WHO grade. Forty-six high-performing radiomic features (1 morphological, 7 intensity-based, and 38 textural) were identified and used to build a support vector machine model to stratify tumors based on a Ki-67 cutoff of 5%, with resultant areas under the curve of 0.83 (95% CI 0.78–0.89) and 0.84 (95% CI 0.75–0.94) achieved for the discovery (n = 257) and validation (n = 86) data sets, respectively. Comparison of histopathological Ki-67 versus machine learning–predicted Ki-67 showed excellent performance (overall accuracy > 80%), with classification of grade I meningiomas exhibiting the greatest accuracy. Prediction of Ki-67 by machine learning classifier revealed shorter PFS for meningiomas with Ki-67 indices ≥ 5% compared with tumors with Ki-67 < 5% (p < 0.0001, log-rank test), which corroborates divergent patient outcomes observed using histopathological Ki-67.
CONCLUSIONS
The Ki-67 proliferation index may serve as a surrogate marker of increased meningioma aggressiveness independent of WHO grade. Machine learning using radiomic feature analysis may be used for the preoperative prediction of meningioma Ki-67, which provides enhanced analytical insights to help improve diagnostic classification and guide patient-specific treatment strategies.
Publisher
Journal of Neurosurgery Publishing Group (JNSPG)
Subject
Neurology (clinical),General Medicine,Surgery
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