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

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2024-10-29 DOI:10.1016/j.wneu.2024.10.089

Bardia Hajikarimloo, Salem M Tos, Mohammadamin Sabbagh Alvani, Mohammad Ali Rafiei, Diba Akbarzadeh, Mohammad ShahirEftekhar, Mohammadhosein Akhlaghpasand, Mohammad Amin Habibi

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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 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.

Results: 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.

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

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人工智能在脑膜瘤 Ki-67 指数预测中的应用：系统回顾与元分析

背景：Ki-67指数是一种组织病理学标志物，据报道是脑膜瘤生物学行为和预后的关键因素。一些研究开发了基于放射组学的人工智能（AI）模型来预测 Ki-67。本研究旨在对预测脑膜瘤Ki-67指数的人工智能模型进行系统回顾和荟萃分析：我们于 2024 年 4 月 27 日在 PubMed、Embase、Scopus 和 Web of Science 中使用相关关键词检索了文献记录。根据资格标准对记录进行筛选，并提取纳入研究的数据。采用 QUADAS-2 工具进行质量评估。使用 R 软件进行荟萃分析、敏感性分析和荟萃回归：我们的研究包括六项研究。平均 Ki-67 在 2.7 ± 2.97 到 4.8 ± 40.3 之间。六项研究中，五项采用了 ML 方法。使用最多的人工智能方法是最小绝对收缩和选择算子（LASSO）。AUC 和 ACC 分别为 0.83 至 0.99 和 0.81 至 0.95。AI 模型的集合灵敏度为 87.5%（95% CI：75.2%，94.2%），特异度为 86.9%（95% CI：75.8%，93.4%），诊断几率比（DOR）为 40.02（95% CI：13.5，156.4）。接受者操作特征 SROC 曲线显示，预测颅内脑膜瘤 Ki-67 指数状态的 AUC 为 0.931：结论：人工智能模型在预测脑膜瘤的 Ki-67 指数方面表现良好，可以优化治疗策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.

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