Diagnostic performance of radiomics in prediction of Ki-67 index status in non-small cell lung cancer: A systematic review and meta-analysis

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.