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Last Posted: May 31, 2023
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Utility of polygenic risk scores in UK cancer screening: a modelling analysis
C Huntley et al, Lancet Oncology, May 2023

It is proposed that, through restriction to individuals delineated as high risk, polygenic risk scores (PRSs) might enable more efficient targeting of existing cancer screening programmes and enable extension into new age ranges and disease types. To address this proposition, we present an overview of the performance of PRS tools (ie, models and sets of single nucleotide polymorphisms) alongside harms and benefits of PRS-stratified cancer screening for eight example cancers (breast, prostate, colorectal, pancreas, ovary, kidney, lung, and testicular cancer).

The "Scope" of Colorectal Cancer Screening in Lynch Syndrome: Is There an Optimal Interval?
Leah H Biller et al. J Natl Cancer Inst 2023 5

Across all genes related to Lynch syndrome, colonoscopy screening reduces the risk of CRC and improves overall survival among LS carriers, and remains the mainstay of current risk reduction recommendations. What remains less clear are both the optimal age at which to start screening and the best interval between colonoscopy screenings for LS carriers, such that many international professional society guidelines vary with respect to these recommendations.

Utilization of Circulating Tumor Cells in the Management of Solid Tumors
PC Kumiali et al, J Per Med, April 20., 2023

CTCs may play significant roles in cancer screening, diagnosis, treatment navigation, including prognostication and precision medicine, and surveillance. In cancer screening, capturing and evaluating CTCs from peripheral blood could be a strategy to detect cancer at its earliest stage. Cancer diagnosis using liquid biopsy could also have tremendous benefits. Full utilization of CTCs in the clinical management of malignancies may be feasible in the near future; however, several challenges still exist.

Leveraging Artificial Intelligence to Improve Accuracy of Lung Cancer Screening
NCI Blog, December 16, 2022 Brand

Artificial intelligence (AI) has been used to discriminate between normal and precancer/cancer in a number of settings. In the past decade, significant progress has been made in computer-aided detection and diagnosis, leading to several Food and Drug Administration (FDA)-approved types of software. Recent efforts are focused on deep learning, a subset of AI that uses artificial neural networks to learn from huge amounts of data. If the trained neural network out-performs human expert interpretation, it is poised to transform medical imaging and diagnostics.

Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.