A Meta-Analysis of Radiomics-Based Models for Preoperative Assessment of Ki-67 Status in Hepatocellular Carcinoma

Summary

This meta-analysis of 17 studies (1,708 patients with hepatocellular carcinoma) found pooled sensitivity, specificity, and area under the curve values of 0.87, 0.79, and 0.90, respectively, for radiomic prediction of high Ki-67 expression; substantial heterogeneity indicates a need for standardized research.

Abstract

This study aimed to evaluate the diagnostic value of radiomic features in predicting Ki-67 expression levels in hepatocellular carcinoma (HCC) through a meta-analysis. Electronic databases, including PubMed, Web of Science, the Cochrane Library, and Embase, were systematically searched for relevant clinical studies published through 20 August 2025. Studies using radiomic features to predict Ki-67 expression levels in patients with HCC were included. Sensitivity, specificity, and summary receiver operating characteristic curves were evaluated, and the area under the curve (AUC) was calculated. A total of 17 studies involving 1,708 patients with HCC were included. The pooled sensitivity was 0.87 (95% confidence interval [CI], 0.81–0.91), the pooled specificity was 0.79 (95% CI, 0.71–0.85), and the overall AUC was 0.90 (95% CI, 0.87–0.93). The pooled AUC values for Ki-67 cutoff values of 10% and >10% were 0.89 (95% CI, 0.86–0.92) and 0.90 (95% CI, 0.87–0.92), respectively. The AUC values for magnetic resonance imaging- and ultrasound-derived radiomic features were 0.88 (95% CI, 0.85–0.91) and 0.92 (95% CI, 0.89–0.94), respectively. The AUC for prediction models based on logistic regression was 0.89 (95% CI, 0.86–0.92). Radiomic features showed promising pooled diagnostic performance for predicting high Ki-67 expression in HCC. However, substantial heterogeneity was present among the included studies. Further standardized research is needed to validate these findings.

Introduction

Over the past decade, the incidence and prevalence of liver cancer have progressively increased, making it a significant and growing disease burden worldwide. Specifically, hepatocellular carcinoma (HCC) accounts for 90% of all primary liver cancer diagnoses and represents the fourth most common cause of cancer-related deaths globally, with 840,000 new cases reported in 2018^1,2. Despite recent advances in surgical techniques, local interventions, and systemic therapies, the long-term prognosis of patients with HCC remains suboptimal, with 5-year overall survival rates ranging between 13% and 36%

Protocol

Search strategy and information source
In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement¹⁵, a systematic search was performed across PubMed, Web of Science, Cochrane Library, and Embase from database inception to 20 August 2025, without language restrictions (see Supplemental File 1). The search strategy was constructed by grouping keywords and subject headings (such as Medical Subject Headings [MeSH] in PubMed and Emtree in Embase) into three logical blocks using the Boolean operators ‘AND’ and ‘OR’. These blocks were....

Results

Characteristics of included studies
Through a systematic electronic database search, 3,923 studies were initially identified for screening. After excluding 2,032 duplicate records and 1,719 irrelevant studies, 172 articles underwent full-text review. Based on the predefined inclusion and exclusion criteria, 17 studies^{13,14,17,18,19,}

Discussion

This systematic meta-analysis evaluated the diagnostic performance of radiomic features for predicting Ki-67 expression in HCC. The pooled sensitivity was 0.87, the pooled specificity was 0.79, and the summary AUC was 0.90. Although estimates were broadly similar across the subgroups analyzed, substantial heterogeneity limits the generalizability of the pooled estimates. These findings suggest that radiomics may be a complement to tissue-based assessment, but its clinical utility requires further validation.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Review Manager	Nordic Cochrane Centre	Version 5.3	Risk-of-bias assessment and mapping
Stata	StataCorp	Version 16.0	Diagnostic meta-analysis using the midas module