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Related Concept Videos

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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Quantifying Overdiagnosis for Multicancer Detection Tests: A Novel Method.

Stuart G Baker1

  • 1Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, USA.

Statistics in Medicine
|November 26, 2024
PubMed
Summary
This summary is machine-generated.

Quantifying overdiagnosis in multicancer detection (MCD) tests is crucial. A new method estimates the screen overdiagnosis fraction (SOF) using yearly MCD tests, addressing concerns about unnecessary treatments from early cancer detection.

Keywords:
cancer screeninglead timemulticancer early detection testoverdiagnosis

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Area of Science:

  • Oncology
  • Biostatistics
  • Medical Screening

Background:

  • Multicancer detection (MCD) tests identify preclinical cancers from blood samples.
  • Overdiagnosis, detecting cancers that won't become symptomatic, is a significant concern with screening, potentially leading to harmful treatments.
  • Quantifying overdiagnosis, particularly the screen overdiagnosis fraction (SOF), is essential but challenging, especially for rapidly evolving MCD technologies.

Purpose of the Study:

  • To introduce a novel method for estimating the average screen overdiagnosis fraction (SOF) for multicancer detection (MCD) screening programs.
  • To address the difficulty in estimating SOF due to the unobserved nature of overdiagnosis and the need for short-term data with MCD tests.
  • To develop a method applicable to cancers without conventional screening and robust to technological changes.

Main Methods:

  • A new method is proposed requiring at least two annual MCD tests in individuals across a range of ages.
  • The method assumes an exponential distribution for the sojourn time in the operational preclinical cancer (OPC) state.
  • An SOF plot is generated, graphing average SOF against mean sojourn time, using lung cancer screening and synthetic data.

Main Results:

  • The proposed SOF plot method demonstrated the ability to distinguish between small and moderate levels of SOF.
  • The method's reliance on only one term with the exponential distribution assumption makes its results robust to violations.
  • The study provides a new tool for estimating SOF, particularly valuable for MCD tests where short-term data is available.

Conclusions:

  • The novel SOF plot method offers a complementary approach to existing models for estimating overdiagnosis in MCD screening.
  • This method is particularly useful for assessing the risk of overdiagnosis in new screening technologies like MCD tests.
  • Further application of the SOF plot is recommended as more short-term observational data for MCD tests becomes available.