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Last Posted: Sep 30, 2022
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How to improve the diagnosis of prostate cancer
B Plackett, Nature, September 2022

There are two main schools of thought on how to replace PSA screening. The first is to look for better biomarkers in the blood or urine, and the second eschews testing samples altogether in favour of sophisticated imaging techniques. Whichever approach wins out, better screening with fewer false positives should mean fewer patients undergo needless biopsies.

Accumulation of copy number alterations and clinical progression across advanced prostate cancer
A Grist et al, Genome Medicine, September 5, 2022

The burden of copy number alterations positively associated with radiologically evident distant metastases at diagnosis (P=0.00006) and showed a non-linear relationship with clinical outcome on univariable and multivariable analysis, characterized by a sharp increase in the relative risk of progression (P=0.003) and death (P=0.045) for each unit increase.

Genomic testing in localized prostate cancer can identify subsets of African-Americans with aggressive disease.
Awasthi Shivanshu et al. Journal of the National Cancer Institute 2022 9

This is a prospective study of the Decipher genomic classifier for NCCN low- and intermediate–risk PCa. Study eligible non-African American men were matched to African American men. Diagnostic biopsy specimens were processed to estimate Decipher scores. We found that integration of genomic classifiers with clinically-based risk classification can help identify the subset of African American men with localized PCa who harbor high genomic risk of early metastatic disease.

Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer.
Lee Changhee et al. NPJ digital medicine 2022 8 (1) 110

Active Surveillance (AS) for prostate cancer is a management option that continually monitors early disease and considers intervention if progression occurs. A robust method to incorporate “live” updates of progression risk during follow-up has hitherto been lacking. To address this, we developed a deep learning-based individualised longitudinal survival model using Dynamic-DeepHit-Lite (DDHL) that learns data-driven distribution of time-to-event outcomes.

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.