Bardia Hajikarimloo, Salem M Tos, Mohammadamin Sabbagh Alvani, Mohammad Ali Rafiei, Diba Akbarzadeh, Mohammad ShahirEftekhar, Mohammadhosein Akhlaghpasand, Mohammad Amin Habibi
{"title":"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","authors":"Bardia Hajikarimloo, Salem M Tos, Mohammadamin Sabbagh Alvani, Mohammad Ali Rafiei, Diba Akbarzadeh, Mohammad ShahirEftekhar, Mohammadhosein Akhlaghpasand, Mohammad Amin Habibi","doi":"10.1016/j.wneu.2024.10.089","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>AI models have demonstrated promising performance for predicting the Ki-67 index in meningiomas and can optimize the treatment strategy.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Artificial Intelligence in Prediction of Ki-67 Index in Meningiomas: A Systematic Review and Meta-Analysis.\",\"authors\":\"Bardia Hajikarimloo, Salem M Tos, Mohammadamin Sabbagh Alvani, Mohammad Ali Rafiei, Diba Akbarzadeh, Mohammad ShahirEftekhar, Mohammadhosein Akhlaghpasand, Mohammad Amin Habibi\",\"doi\":\"10.1016/j.wneu.2024.10.089\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>AI models have demonstrated promising performance for predicting the Ki-67 index in meningiomas and can optimize the treatment strategy.</p>\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.wneu.2024.10.089\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.wneu.2024.10.089","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Application of Artificial Intelligence in Prediction of Ki-67 Index in Meningiomas: A Systematic Review and Meta-Analysis.
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.
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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