Application of Artificial Intelligence in Prediction of Ki-67 Index in Meningiomas: A Systematic Review and Meta-Analysis

Abstract

Background

The Ki-67 index is a histopathological marker that has been reported to be a crucial factor in the biological behavior and prognosis of meningiomas. Several studies have developed artificial intelligence (AI) models to predict the Ki-67 based on radiomics. In this study, we aimed to perform a systematic review and meta-analysis of AI models that predicted the Ki-67 index in meningioma.

Methods

Literature records were retrieved on April 27, 2024, using the relevant key terms without filters in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from included studies were extracted. The quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software.

Results

Our study included 6 studies. The mean Ki-67 ranged from 2.7 ± 2.97 to 4.8 ± 40.3. Of 6 studies, 5 utilized a machine learning method. The most used AI method was the least absolute shrinkage and selection operator. The area under the curve and accuracy ranged from 0.83 to 0.99 and 0.81 to 0.95, respectively. AI models demonstrated a pooled sensitivity of 87.5% (95% confidence interval [CI]: 75.2%, 94.2%), a specificity of 86.9% (95% CI: 75.8%, 93.4%), and a diagnostic odds ratio of 40.02 (95% CI: 13.5, 156.4). The summary receiver operating characteristic curve indicated an area under the curve of 0.931 for the prediction of Ki-67 index status in intracranial meningiomas.

Conclusions

AI models have demonstrated promising performance for predicting the Ki-67 index in meningiomas and can optimize the treatment strategy.