Abstract
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. Literature records were retrieved on April 27th, 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 QUADAS-2 tool. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software. Our study included six studies. The mean Ki-67 ranged from 2.7 ± 2.97 to 4.8 ± 40.3. Of six studies, five utilized an ML method. The most used AI method was the least absolute shrinkage and selection operator (LASSO). The AUC and ACC ranged from 0.83 to 0.99 and 0.81 to 0.95, respectively. AI models demonstrated a pooled sensitivity of 87.5% (95% CI: 75.2%, 94.2%), a specificity of 86.9% (95% CI: 75.8%, 93.4%), and a diagnostic odds ratio (DOR) of 40.02 (95% CI: 13.5, 156.4). The summary receiver operating characteristic SROC curve indicated an AUC of 0.931 for the prediction of Ki-67 index status in intracranial meningiomas. AI models have demonstrated promising performance for predicting the Ki-67 index in meningiomas and can optimize the treatment strategy.
Overview
- The study aims to perform a systematic review and meta-analysis of AI models that predict the Ki-67 index in meningiomas. The literature records were retrieved using relevant key terms without filters in PubMed, Embase, Scopus, and Web of Science. The data from included studies were extracted, and the quality assessment was performed using the QUADAS-2 tool. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software. The study included six studies, and the mean Ki-67 ranged from 2.7 ± 2.97 to 4.8 ± 40.3. Of six studies, five utilized an ML method. The most used AI method was the least absolute shrinkage and selection operator (LASSO). The AUC and ACC ranged from 0.83 to 0.99 and 0.81 to 0.95, respectively. AI models demonstrated a pooled sensitivity of 87.5% (95% CI: 75.2%, 94.2%), a specificity of 86.9% (95% CI: 75.8%, 93.4%), and a diagnostic odds ratio (DOR) of 40.02 (95% CI: 13.5, 156.4). The summary receiver operating characteristic SROC curve indicated an AUC of 0.931 for the prediction of Ki-67 index status in intracranial meningiomas. The study aims to optimize the treatment strategy for meningiomas by predicting the Ki-67 index using AI models.
Comparative Analysis & Findings
- The study compared the outcomes observed under different experimental conditions or interventions detailed in the study. The results showed that AI models demonstrated a pooled sensitivity of 87.5% (95% CI: 75.2%, 94.2%), a specificity of 86.9% (95% CI: 75.8%, 93.4%), and a diagnostic odds ratio (DOR) of 40.02 (95% CI: 13.5, 156.4). The summary receiver operating characteristic SROC curve indicated an AUC of 0.931 for the prediction of Ki-67 index status in intracranial meningiomas. The study found that AI models demonstrated promising performance for predicting the Ki-67 index in meningiomas and can optimize the treatment strategy.
Implications and Future Directions
- The study's findings suggest that AI models can accurately predict the Ki-67 index in meningiomas, which can optimize the treatment strategy. The study's limitations include the small sample size and the lack of standardization in the data collection process. Future research should focus on increasing the sample size and standardizing the data collection process to improve the accuracy of the AI models. Additionally, future research should explore the use of AI models in predicting other prognostic factors in meningiomas, such as age and gender, to further optimize the treatment strategy.