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Diagnostic Performance of Radiomics in Prediction of Ki-67 Index Status in Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis Publisher Pubmed



Shahidi R1 ; Hassannejad E2 ; Baradaran M3 ; Klontzas ME4, 5 ; Shahireftekhar M3, 6 ; Shojaeshafiei F7 ; Hajiesmailpoor Z8 ; Chong W9 ; Broomand N10 ; Alizadeh M11 ; Mozafari N1 ; Sadeghsalehi H12 ; Teimoori S13 ; Farhadi A14 Show All Authors
Authors
  1. Shahidi R1
  2. Hassannejad E2
  3. Baradaran M3
  4. Klontzas ME4, 5
  5. Shahireftekhar M3, 6
  6. Shojaeshafiei F7
  7. Hajiesmailpoor Z8
  8. Chong W9
  9. Broomand N10
  10. Alizadeh M11
  11. Mozafari N1
  12. Sadeghsalehi H12
  13. Teimoori S13
  14. Farhadi A14
  15. Nouri H1
  16. Shobeiri P15
  17. Sotoudeh H16
Show Affiliations
Authors Affiliations
  1. 1. School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
  2. 2. Department of Radiology, School of Medicine, Birjand University of Medical Sciences, Birjand, Iran
  3. 3. Department of Radiology, Imam Ali Hospital, North Khorasan University of Medical Sciences, Bojnurd, Iran
  4. 4. Department of Medical Imaging, University Hospital of Heraklion, Crete, Heraklion, 71110, Greece
  5. 5. Department of Radiology, School of Medicine, University of Crete, Crete, Heraklion, 71003, Greece
  6. 6. Department of Surgery, School of Medicine, Qom University of Medical Sciences, Qom, Iran
  7. 7. Department of Radiology, Tehran University of Medical Sciences, Tehran, Iran
  8. 8. Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
  9. 9. Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, United States
  10. 10. Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran
  11. 11. Physiology Research Center, Iran University of Medical Sciences, Tehran, Iran
  12. 12. Department of Artificial Intelligence in Medical Sciences, Faculty of Advanced Technologies in Medicine, Iran University Of Medical Sciences, Tehran, Iran
  13. 13. Young Researchers and Elites Club, Faculty of Medicine, Islamic Azad University, Yazd Branch, Yazd, Iran
  14. 14. Persian Gulf Tropical Medicine Research Center, Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran
  15. 15. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, 10065, NY, United States
  16. 16. Neuroradiology Section, Department of Radiology and Neurology, The University of Alabama at Birmingham, AL, United States

Source: Journal of Medical Imaging and Radiation Sciences Published:2024


Abstract

Background: Lung cancer's high prevalence and invasiveness make it a major global health concern. The Ki-67 index, which indicates cellular proliferation, is crucial for assessing lung cancer aggressiveness. Radiomics, which extracts quantifiable features from medical images using algorithms, may provide insights into tumor behavior. This systematic review and meta-analysis evaluate the effectiveness of radiomics in predicting Ki-67 status in Non-Small Cell Lung Cancer (NSCLC) using CT scans. Methods and materials: A comprehensive search was conducted in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception until April 19, 2024. Original studies discussing the performance of CT-based radiomics for predicting Ki-67 status in NSCLC cohorts were included. The quality assessment involved quality assessment of diagnostic accuracy studies (QUADAS-2), radiomics quality score (RQS) and METhodological RadiomICs Score (METRICS). Quantitative meta-analysis, using R, assessed pooled diagnostic odds ratio, sensitivity, and specificity in NSCLC cohorts. Results: We identified 10 studies that met the inclusion criteria, involving 2279 participants, with 9 of these studies included in quantitative meta-analysis. The pooled sensitivity and specificity of radiomics-based models for predicting Ki-67 status in NSCLC were 0.783 (95 % CI: 0.732 - 0.827) and 0.796 (95 % CI: 0.707 - 0.864) in training cohorts, and 0.803 (95 % CI: 0.744 - 0.851) and 0.696 (95 % CI: 0.613 - 0.768) in validation cohorts. It was identified in subgroup analysis that utilizing ITK-SNAP as a segmentation software contributed to a significantly higher pooled sensitivity. Conclusion: This meta-analysis indicates promising diagnostic accuracy of radiomics in predicting Ki-67 in NSCLC. © 2024
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