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Last Posted: Jan 31, 2023
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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.

Factors Likely to Affect the Uptake of Genomic Approaches to Cancer Screening in Primary Care: A Scoping Review
KV Davis et al, J Per Med, December 10, 2022

Six articles focused on patient perceptions about testing for a single cancer (colorectal), and 1 reported on patient views related to testing for multiple cancers. Factors favoring this type of testing included its non-invasiveness, and the perceived safety, convenience, and effectiveness of testing. There is a dearth of information in the literature on primary care provider perceptions about liquid biopsy and MCED testing. The limited information on patient perceptions suggests that they are receptive to such tests.

Development of a Molecular Blood-Based Immune Signature Classifier as Biomarker for Risks Assessment in Lung Cancer Screening.
Fortunato Orazio et al. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2022 9 (11) 2020-2029

Monocytic myeloid-derived suppressor cell (MDSC), polymorphonuclear MDSC, intermediate monocytes and CD8+PD-1+ T cells distinguished patients with lung cancer from controls with AUCs values of 0.94/0.72/0.88 in the training, validation, and lung cancer specificity set, respectively. AUCs raised up to 1.00/0.84/0.92 in subgroup analysis considering only MSC-negative subjects. A 14-immune genes expression signature distinguished patients from controls with AUC values of 0.76 in the validation set and 0.83 in MSC-negative subjects.

Cancer, screening, and polygenic scores
C Babb de Villiers, PHG Foundation blog, November 1, 2022

The use of comprehensive risk prediction models that include genetic, environmental and lifestyle risk factors, are being trialled for stratified screening programmes. The results from these trials are imminent in the next few years and when they arrive they will provide evidence on whether risk prediction using polygenic scores can improve risk prediction for cancer and contribute towards stratified screening.

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.