Haoru Wang, Yi Ji, Xin Chen, Ling He, Xiangming Fang, Jinhua Cai
{"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}
引用次数: 0
Abstract
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.