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Background: Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously identifying multiple types of cancer within a single test using minimally invasive blood samples.
View Article and Find Full Text PDFEstimating the incremental population health impact of a multi-cancer early detection (MCED) test to complement existing screening among elevated risk populations with multiple cancer risk factors: a mathematical modeling study.
BMC Health Serv Res
December 2024
EpidStrategies, a Division of ToxStrategies, LLC, Katy, TX, USA.
Background: The added benefits of a multi-cancer early detection (MCED) test among individuals with multiple risk factors will help policy decision-makers allocate limited healthcare resources. This study sought to estimate the population health implications of adding an MCED test to standard-of-care (SOC) cancer screening tests among individuals aged 50-79 years with additional cancer risk factors (i.e.
View Article and Find Full Text PDFQuantifying Overdiagnosis for Multicancer Detection Tests: A Novel Method.
Stat Med
December 2024
Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, USA.
Multicancer detection (MCD) tests use blood specimens to detect preclinical cancers. A major concern is overdiagnosis, the detection of preclinical cancer on screening that would not have developed into symptomatic cancer in the absence of screening. Because overdiagnosis can lead to unnecessary and harmful treatments, its quantification is important.
View Article and Find Full Text PDFGWAS and 3D chromatin mapping identifies multicancer risk genes associated with hormone-dependent cancers.
PLoS Genet
November 2024
Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Hormone-dependent cancers (HDCs) share several risk factors, suggesting a common aetiology. Using data from genome-wide association studies, we showed spatial clustering of risk variants across four HDCs (breast, endometrial, ovarian and prostate cancers), contrasting with genetically uncorrelated traits. We identified 44 multi-HDC risk regions across the genome, defined as overlapping risk regions for at least two HDCs: two regions contained risk variants for all four HDCs, 13 for three HDCs and 28 for two HDCs.
View Article and Find Full Text PDFEarly multi-cancer detection through deep learning: An anomaly detection approach using Variational Autoencoder.
J Biomed Inform
December 2024
Laboratory of Information System and Signal Processing, National Advanced School of Engineering Yaounde, Department of Computer Engineering, University of Yaounde I, Yaounde, Cameroon. Electronic address:
Cancer is a disease that causes many deaths worldwide. The treatment of cancer is first and foremost a matter of detection, a treatment that is most effective when the disease is detected at an early stage. With the evolution of technology, several computer-aided diagnosis tools have been developed around cancer; several image-based cancer detection methods have been developed too.
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