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Mri-Derived Radiomics Models for Prediction of Ki-67 Index Status in Meningioma: A Systematic Review and Meta-Analysis Publisher



Broomand Lomer N1 ; Khalaj F2 ; Ghorani H2 ; Mohammadi M3 ; Ghadimi DJ4 ; Zakavi S5 ; Afsharzadeh M6 ; Sotoudeh H7
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Authors Affiliations
  1. 1. Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, United States
  2. 2. Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Science, Tehran, Iran
  3. 3. School of Medicine, Iran University of Medical Sciences, Tehran, Iran
  4. 4. School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  5. 5. Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
  6. 6. Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  7. 7. Department of Radiology, UT Southwestern, Dallas, TX, United States

Source: Clinical Imaging Published:2025


Abstract

Purpose: The Ki-67 marker reflects tumor proliferation and correlates with meningioma prognosis. Here we aim to evaluate the performance of MRI-derived radiomics for Ki-67 index prediction in meningiomas. Methods: After a comprehensive search in Web of Science, PubMed, Embase, and Scopus, data extraction and risk of bias assessment was performed. Pooled sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnostic odds ratio (DOR) were computed. The summary receiver operating characteristic (sROC) curve was generated and area under the curve (AUC) was calculated. Separate meta-analyses were conducted for radiomics models and combined models. Heterogeneity was evaluated using the I2 statistic, and subgroup analysis was performed to identify potential sources of heterogeneity. Sensitivity analysis was carried out to detect possible outliers. Results: Seven studies were included, with six studies analyzed for radiomics model and four for combined model. For radiomics model, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 67 %, 82 %, 8.61, 3.54, 0.43, and 0.79, respectively. For combined model, pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 78 %, 78 %, 12.19, 3.47, 0.30, and 0.79, respectively. Sensitivity analysis identified no outliers. In radiomics model, potential sources of heterogeneity included mean age and the application of N4ITK bias correction. For combined model, heterogeneity was influenced by mean age, application of N4ITK bias correction, and the use of external validation. Conclusion: Radiomics shows promising ability to predict the Ki-67 index status in meningioma patients, potentially enhancing clinical decision-making and management strategies. © 2025 Elsevier Inc.
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