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 is a global health concern, in part due to its high prevalence and invasiveness. The Ki-67 index, indicating cellular proliferation, is pivotal for assessing lung cancer aggressiveness. Radiomics is the inference of quantifiable data features from medical images through algorithms and may offer insights into tumor behavior. Here, we perform a systematic review and meta-analysis to assess the performance of radiomics for predicting Ki-67 status in Non-small Cell Lung Cancer (NSCLC) on CT scan. Methods and materials A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to November 16, 2023. 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) and radiomics quality score (RQS). Quantitative meta-analysis, using R, assessed pooled 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 overall quality of the included studies was moderate to high based on QUADAS-2 and RQS assessment. The pooled sensitivity and specificity of radiomics-based models for predicting the Ki-67 status of NSCLC training cohorts were 0.78 (95% CI [0.73; 0.83]) and 0.76 (95% CI [0.70; 0.82]), respectively. The pooled sensitivity and specificity of radiomics-based models for predicting the Ki-67 status of NSCLC validation cohorts were 0.79 (95% CI [0.73; 0.84]) and 0.69 (95% CI [0.61; 0.76]), respectively. Substantial heterogeneity was noted in the pooled sensitivity and specificity of training cohorts and the pooled specificity of validation cohorts (I 2 > 40%). It was identified 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. The study underscores radiomics’ potential in personalized lung cancer management, advocating for prospective studies with standardized methodologies and larger samples.