Haoru Wang, Yi Ji, Xin Chen, Ling He, Xiangming Fang, Jinhua Cai
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This systematic review and meta-analysis aimed to quantitatively evaluate the diagnostic accuracy of radiomics-based machine learning models for determining <i>MYCN</i> amplification in neuroblastoma and to critically assess the methodological quality of the included studies.MethodsA systematic search of articles published between January 1, 2000, and June 30, 2024, was conducted across PubMed, Embase, Web of Science, and the Cochrane Library. The articles focused on using radiomics to determine <i>MYCN</i> amplification in neuroblastoma. Methodological quality was assessed using the Radiomics Quality Score (RQS), METhodological RadiomICs Score (METRICS), and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. A meta-analysis of validation performance was performed on studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement Type 2a or higher.ResultsNine studies with 851 patients were included, and seven studies with 217 patients in the validation set were eligible for meta-analysis. The RQS scores ranged from 10 to 16 (mean 12), and METRICS scores ranged from 28.8% to 78.4% (mean 59.7%). QUADAS-2 assessment indicated that most studies had a low or unclear risk of bias. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were 0.78, 0.92, 9.45, and 0.24, respectively. The area under the summary receiver operating characteristic curve was 0.94 (95% confidence interval: 0.91-0.95).ConclusionDespite variability in study design and bias risk, radiomics shows promise as a non-invasive method for detecting <i>MYCN</i> amplification in neuroblastoma. Further refinement and validation in multicenter studies with larger sample sizes are needed to enhance its clinical applicability.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251358324"},"PeriodicalIF":2.8000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12235222/pdf/","citationCount":"0","resultStr":"{\"title\":\"Radiomics-Based Machine Learning for Determining <i>MYCN</i> Amplification Status in Childhood Neuroblastoma: A Systematic Review and Meta-Analysis.\",\"authors\":\"Haoru Wang, Yi Ji, Xin Chen, Ling He, Xiangming Fang, Jinhua Cai\",\"doi\":\"10.1177/15330338251358324\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>IntroductionThe <i>MYCN</i> oncogene promotes tumor cell proliferation in neuroblastoma, and its amplification is a well-established marker of poor prognosis. Radiomics-based approaches have shown promise in noninvasively determining <i>MYCN</i> amplification status; however, their diagnostic performance has varied significantly across studies. This systematic review and meta-analysis aimed to quantitatively evaluate the diagnostic accuracy of radiomics-based machine learning models for determining <i>MYCN</i> amplification in neuroblastoma and to critically assess the methodological quality of the included studies.MethodsA systematic search of articles published between January 1, 2000, and June 30, 2024, was conducted across PubMed, Embase, Web of Science, and the Cochrane Library. The articles focused on using radiomics to determine <i>MYCN</i> amplification in neuroblastoma. Methodological quality was assessed using the Radiomics Quality Score (RQS), METhodological RadiomICs Score (METRICS), and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. A meta-analysis of validation performance was performed on studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement Type 2a or higher.ResultsNine studies with 851 patients were included, and seven studies with 217 patients in the validation set were eligible for meta-analysis. The RQS scores ranged from 10 to 16 (mean 12), and METRICS scores ranged from 28.8% to 78.4% (mean 59.7%). QUADAS-2 assessment indicated that most studies had a low or unclear risk of bias. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were 0.78, 0.92, 9.45, and 0.24, respectively. The area under the summary receiver operating characteristic curve was 0.94 (95% confidence interval: 0.91-0.95).ConclusionDespite variability in study design and bias risk, radiomics shows promise as a non-invasive method for detecting <i>MYCN</i> amplification in neuroblastoma. Further refinement and validation in multicenter studies with larger sample sizes are needed to enhance its clinical applicability.</p>\",\"PeriodicalId\":22203,\"journal\":{\"name\":\"Technology in Cancer Research & Treatment\",\"volume\":\"24 \",\"pages\":\"15330338251358324\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12235222/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Technology in Cancer Research & Treatment\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/15330338251358324\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/7/7 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technology in Cancer Research & Treatment","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15330338251358324","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/7/7 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
MYCN癌基因在神经母细胞瘤中促进肿瘤细胞增殖,其扩增是预后不良的标志。基于放射组学的方法在无创确定MYCN扩增状态方面显示出前景;然而,他们的诊断表现在不同的研究中差异很大。本系统综述和荟萃分析旨在定量评估基于放射组学的机器学习模型用于确定神经母细胞瘤中MYCN扩增的诊断准确性,并严格评估纳入研究的方法学质量。方法系统检索2000年1月1日至2024年6月30日在PubMed、Embase、Web of Science和Cochrane图书馆发表的文章。本文的重点是利用放射组学技术测定神经母细胞瘤中MYCN的扩增。使用放射组学质量评分(RQS)、放射组学评分(METRICS)和诊断准确性研究质量评估2 (QUADAS-2)工具评估方法学质量。采用透明报告的多变量预测模型对个体预后或诊断声明类型2a或更高的研究进行验证性能的荟萃分析。结果纳入9项研究共851例患者,验证集中有7项研究共217例患者符合meta分析条件。RQS评分为10 ~ 16分(平均12分),METRICS评分为28.8% ~ 78.4%(平均59.7%)。QUADAS-2评估表明,大多数研究的偏倚风险较低或不明确。合并敏感性、特异性、阳性似然比和阴性似然比分别为0.78、0.92、9.45和0.24。总体受试者工作特征曲线下面积为0.94(95%可信区间:0.91-0.95)。结论:尽管研究设计和偏倚风险存在差异,放射组学有望作为一种非侵入性方法检测成神经细胞瘤中MYCN扩增。需要在更大样本量的多中心研究中进一步完善和验证,以增强其临床适用性。
Radiomics-Based Machine Learning for Determining MYCN Amplification Status in Childhood Neuroblastoma: A Systematic Review and Meta-Analysis.
IntroductionThe MYCN oncogene promotes tumor cell proliferation in neuroblastoma, and its amplification is a well-established marker of poor prognosis. Radiomics-based approaches have shown promise in noninvasively determining MYCN amplification status; however, their diagnostic performance has varied significantly across studies. This systematic review and meta-analysis aimed to quantitatively evaluate the diagnostic accuracy of radiomics-based machine learning models for determining MYCN amplification in neuroblastoma and to critically assess the methodological quality of the included studies.MethodsA systematic search of articles published between January 1, 2000, and June 30, 2024, was conducted across PubMed, Embase, Web of Science, and the Cochrane Library. The articles focused on using radiomics to determine MYCN amplification in neuroblastoma. Methodological quality was assessed using the Radiomics Quality Score (RQS), METhodological RadiomICs Score (METRICS), and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. A meta-analysis of validation performance was performed on studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement Type 2a or higher.ResultsNine studies with 851 patients were included, and seven studies with 217 patients in the validation set were eligible for meta-analysis. The RQS scores ranged from 10 to 16 (mean 12), and METRICS scores ranged from 28.8% to 78.4% (mean 59.7%). QUADAS-2 assessment indicated that most studies had a low or unclear risk of bias. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were 0.78, 0.92, 9.45, and 0.24, respectively. The area under the summary receiver operating characteristic curve was 0.94 (95% confidence interval: 0.91-0.95).ConclusionDespite variability in study design and bias risk, radiomics shows promise as a non-invasive method for detecting MYCN amplification in neuroblastoma. Further refinement and validation in multicenter studies with larger sample sizes are needed to enhance its clinical applicability.
期刊介绍:
Technology in Cancer Research & Treatment (TCRT) is a JCR-ranked, broad-spectrum, open access, peer-reviewed publication whose aim is to provide researchers and clinicians with a platform to share and discuss developments in the prevention, diagnosis, treatment, and monitoring of cancer.