Tina Yao, Nicole St Clair, Gabriel F Miller, Adam L Dorfman, Mark A Fogel, Sunil Ghelani, Rajesh Krishnamurthy, Christopher Z Lam, Michael Quail, Joshua D Robinson, David Schidlow, Timothy C Slesnick, Justin Weigand, Jennifer A Steeden, Rahul H Rathod, Vivek Muthurangu
Gianfranco Di Salle, Lorenzo Tumminello, Maria Elena Laino, Sherif Shalaby, Gayane Aghakhanyan, Salvatore Claudio Fanni, Maria Febi, Jorge Eduardo Shortrede, Mario Miccoli, Lorenzo Faggioni, Mirco Cosottini, Emanuele Neri
Purpose To perform a systematic review and meta-analysis assessing the predictive accuracy of radiomics in the noninvasive determination of isocitrate dehydrogenase (IDH) status in grade 4 and lower-grade diffuse gliomas. Materials and Methods A systematic search was performed in the PubMed, Scopus, Embase, Web of Science, and Cochrane Library databases for relevant articles published between January 1, 2010, and July 7, 2021. Pooled sensitivity and specificity across studies were estimated. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2, and methods were evaluated using the radiomics quality score (RQS). Additional subgroup analyses were performed according to tumor grade, RQS, and number of sequences used (PROSPERO ID: CRD42021268958). Results Twenty-six studies that included 3280 patients were included for analysis. The pooled sensitivity and specificity of radiomics for the detection of IDH mutation were 79% (95% CI: 76, 83) and 80% (95% CI: 76, 83), respectively. Low RQS scores were found overall for the included works. Subgroup analyses showed lower false-positive rates in very low RQS studies (RQS < 6) (meta-regression, z = -1.9; P = .02) compared with adequate RQS studies. No substantial differences were found in pooled sensitivity and specificity for the pure grade 4 gliomas group compared with the all-grade gliomas group (81% and 86% vs 79% and 79%, respectively) and for studies using single versus multiple sequences (80% and 77% vs 79% and 82%, respectively). Conclusion The pooled data showed that radiomics achieved good accuracy performance in distinguishing IDH mutation status in patients with grade 4 and lower-grade diffuse gliomas. The overall methodologic quality (RQS) was low and introduced potential bias. Keywords: Neuro-Oncology, Radiomics, Integration, Application Domain, Glioblastoma, IDH Mutation, Radiomics Quality Scoring Supplemental material is available for this article. Published under a CC BY 4.0 license.
目的 对放射组学在无创确定 4 级和低级别弥漫性胶质瘤中异柠檬酸脱氢酶(IDH)状态方面的预测准确性进行系统综述和荟萃分析。材料与方法 在 PubMed、Scopus、Embase、Web of Science 和 Cochrane Library 数据库中对 2010 年 1 月 1 日至 2021 年 7 月 7 日期间发表的相关文章进行了系统检索。对各项研究的汇总敏感性和特异性进行了估算。使用诊断准确性研究质量评估-2对偏倚风险进行评估,并使用放射组学质量评分(RQS)对方法进行评估。根据肿瘤分级、RQS和所用序列的数量进行了其他亚组分析(PROSPERO ID:CRD42021268958)。结果 共有26项研究纳入分析,共纳入3280名患者。放射组学检测IDH突变的总体敏感性和特异性分别为79%(95% CI:76,83)和80%(95% CI:76,83)。所纳入研究的总体 RQS 分数较低。亚组分析显示,与RQS足够高的研究相比,RQS非常低的研究(RQS < 6)假阳性率较低(元回归,z = -1.9; P = .02)。纯4级胶质瘤组与所有级别胶质瘤组(分别为81%和86% vs 79%和79%)以及使用单序列与多序列的研究(分别为80%和77% vs 79%和82%)的汇总灵敏度和特异性没有发现实质性差异。结论 汇总数据显示,放射组学在区分4级和低级别弥漫性胶质瘤患者的IDH突变状态方面具有良好的准确性。总体方法学质量(RQS)较低,存在潜在偏倚。关键词神经肿瘤学 放射组学 整合 应用领域 胶质母细胞瘤 IDH突变 放射组学质量评分 本文有补充材料。以 CC BY 4.0 许可发布。
{"title":"Accuracy of Radiomics in Predicting <i>IDH</i> Mutation Status in Diffuse Gliomas: A Bivariate Meta-Analysis.","authors":"Gianfranco Di Salle, Lorenzo Tumminello, Maria Elena Laino, Sherif Shalaby, Gayane Aghakhanyan, Salvatore Claudio Fanni, Maria Febi, Jorge Eduardo Shortrede, Mario Miccoli, Lorenzo Faggioni, Mirco Cosottini, Emanuele Neri","doi":"10.1148/ryai.220257","DOIUrl":"10.1148/ryai.220257","url":null,"abstract":"<p><p>Purpose To perform a systematic review and meta-analysis assessing the predictive accuracy of radiomics in the noninvasive determination of isocitrate dehydrogenase <i>(IDH</i>) status in grade 4 and lower-grade diffuse gliomas. Materials and Methods A systematic search was performed in the PubMed, Scopus, Embase, Web of Science, and Cochrane Library databases for relevant articles published between January 1, 2010, and July 7, 2021. Pooled sensitivity and specificity across studies were estimated. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2, and methods were evaluated using the radiomics quality score (RQS). Additional subgroup analyses were performed according to tumor grade, RQS, and number of sequences used (PROSPERO ID: CRD42021268958). Results Twenty-six studies that included 3280 patients were included for analysis. The pooled sensitivity and specificity of radiomics for the detection of <i>IDH</i> mutation were 79% (95% CI: 76, 83) and 80% (95% CI: 76, 83), respectively. Low RQS scores were found overall for the included works. Subgroup analyses showed lower false-positive rates in very low RQS studies (RQS < 6) (meta-regression, <i>z</i> = -1.9; <i>P</i> = .02) compared with adequate RQS studies. No substantial differences were found in pooled sensitivity and specificity for the pure grade 4 gliomas group compared with the all-grade gliomas group (81% and 86% vs 79% and 79%, respectively) and for studies using single versus multiple sequences (80% and 77% vs 79% and 82%, respectively). Conclusion The pooled data showed that radiomics achieved good accuracy performance in distinguishing <i>IDH</i> mutation status in patients with grade 4 and lower-grade diffuse gliomas. The overall methodologic quality (RQS) was low and introduced potential bias. <b>Keywords:</b> Neuro-Oncology, Radiomics, Integration, Application Domain, Glioblastoma, IDH Mutation, Radiomics Quality Scoring <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 1","pages":"e220257"},"PeriodicalIF":9.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10831518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139478904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The LLM Will See You Now: Performance of ChatGPT on the Brazilian Radiology and Diagnostic Imaging and Mammography Board Examinations.","authors":"Hari Trivedi, Judy Wawira Gichoya","doi":"10.1148/ryai.230568","DOIUrl":"10.1148/ryai.230568","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 1","pages":"e230568"},"PeriodicalIF":8.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10831515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Scottish Medical Imaging Archive: A Unique Resource for Imaging-related Research.","authors":"Gary J Whitman, David J Vining","doi":"10.1148/ryai.230466","DOIUrl":"10.1148/ryai.230466","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 1","pages":"e230466"},"PeriodicalIF":9.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10831505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139080957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Weak Supervision, Strong Results: Achieving High Performance in Intracranial Hemorrhage Detection with Fewer Annotation Labels.","authors":"Kareem A Wahid, David Fuentes","doi":"10.1148/ryai.230598","DOIUrl":"10.1148/ryai.230598","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 1","pages":"e230598"},"PeriodicalIF":8.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10831509/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sharing Data Is Essential for the Future of AI in Medical Imaging.","authors":"Laura C Bell, Efrat Shimron","doi":"10.1148/ryai.230337","DOIUrl":"10.1148/ryai.230337","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 1","pages":"e230337"},"PeriodicalIF":9.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10831510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139478907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mustafa Ahmed Mahmutoglu, Chandrakanth Jayachandran Preetha, Hagen Meredig, Joerg-Christian Tonn, Michael Weller, Wolfgang Wick, Martin Bendszus, Gianluca Brugnara, Philipp Vollmuth
Adrian P Brady, Bibb Allen, Jaron Chong, Elmar Kotter, Nina Kottler, John Mongan, Lauren Oakden-Rayner, Daniel Pinto Dos Santos, An Tang, Christoph Wald, John Slavotinek