4.3 Article

Diagnostic accuracy of imaging approaches for early tumor detection in children with Li-Fraumeni syndrome

期刊

PEDIATRIC RADIOLOGY
卷 52, 期 7, 页码 1283-1295

出版社

SPRINGER
DOI: 10.1007/s00247-022-05296-9

关键词

Cancer detection; Cancer predisposition disorder; Children; Li-Fraumeni syndrome; Screening; Whole-body magnetic resonance imaging

资金

  1. Terry Fox New Frontiers Program Project Grant [1081]
  2. Faculty of Medicine at Khon Kaen University
  3. Faculty of Medicine at Ramathibodi Hospital
  4. Mahidol University, Thailand

向作者/读者索取更多资源

This study assessed the diagnostic accuracy of the imaging used in the Toronto protocol for cancer surveillance in children. The results showed that optimizing imaging protocol, defining criteria for abnormalities, developing a structured reporting system, and practicing consensus double-reading can enhance the diagnostic accuracy for tumor surveillance.
Background The Toronto protocol for cancer surveillance in children with Li-Fraumeni syndrome has been adopted worldwide. Objective To assess the diagnostic accuracy of the imaging used in this protocol. Materials and methods We conducted a blinded retrospective review of imaging modalities in 31 pediatric patients. We compared imaging findings with the reference standards, which consisted of (1) histopathological diagnosis, (2) corresponding dedicated imaging or subsequent surveillance imaging or (3) clinical outcomes. We individually analyzed each modality's diagnostic performance for cancer detection and assessed it on a per-study basis for chest and abdominal regional whole-body MRI (n=115 each), brain MRI (n=101) and abdominal/pelvic US (n=292), and on a per-lesion basis for skeleton/soft tissues on whole-body MRI (n=140). Results Of 763 studies/lesions, approximately 80% had reference standards that identified 4 (0.7%) true-positive, 523 (85.3%) true-negative, 5 (0.8%) false-positive, 3 (0.5%) false-negative and 78 (12.7%) indeterminate results. There were 3 true-positives on whole-body MRI and 1 true-positive on brain MRI as well as 3 false-negatives on whole-body MRI. Sensitivities and specificities of tumor diagnosis using a worst-case scenario analysis were, respectively, 40.0% (95% confidence interval [CI]: 7.3%, 83.0%) and 38.2% (95% CI: 29.2%, 48.0%) for skeleton/soft tissues on whole-body MRI; sensitivity non-available and 97.8% (95% CI: 91.4%, 99.6%) for chest regional whole-body MRI; 100.0% (95% CI: 5.5%, 100.0%) and 96.8% (95% CI: 90.2%, 99.2%) for abdominal regional whole-body MRI; sensitivity non-available and 98.3% (95% CI: 95.3, 99.4) for abdominal/pelvic US; and 50.0% (95% CI: 2.7%, 97.3%) and 93.8% (95% CI: 85.6%, 97.7%) for brain MRI. Conclusion Considerations for optimizing imaging protocol, defining criteria for abnormalities, developing a structured reporting system, and practicing consensus double-reading may enhance the diagnostic accuracy for tumor surveillance.

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