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Last Posted: Aug 18, 2022
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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.

An appraisal of genetic testing for prostate cancer susceptibility
A Finch et al, NPJ Precision Oncology, June 22, 2022

We review and summarize the literature describing germline pathogenic variants in genes associated with increased prostate cancer risk and aggressivity. Important questions include: what is our ability to screen for and prevent prostate cancer in a man with a germline pathogenic variant and how does knowledge of a germline pathogenic variant influence treatment of men with nonmetastatic disease, with hormone-resistant disease and with metastatic disease? The frequency of germline pathogenic variants in prostate cancer is well described, according to personal and family history of cancer and by stage and grade of disease. The role of these genes in aggressive prostate cancer is also discussed. It is timely to consider whether or not genetic testing should be offered to all men with prostate cancer. The goals of testing are to facilitate screening for early cancers in unaffected high-risk men and to prevent advanced disease in men with cancer.

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials
A Esteva et al, NPJ Digital Medicine, June 8, 2022

Here we demonstrate prostate cancer therapy personalization by predicting long-term, clinically relevant outcomes using a multimodal deep learning architecture and train models using clinical data and digital histopathology from prostate biopsies. We train and validate models using five phase III randomized trials conducted across hundreds of clinical centers. Histopathological data was available for 5654 of 7764 randomized patients (71%) with a median follow-up of 11.4?years.

Comparative Effectiveness of Immune Checkpoint Inhibitors vs Chemotherapy by Tumor Mutational Burden in Metastatic Castration-Resistant Prostate Cancer
RP Graf et al, JAMA Network Open, March 31, 2022

What is the comparative effectiveness of single-agent immune checkpoint inhibitors (ICIs) vs taxane chemotherapy in populations of patients with metastatic castration-resistant prostate cancer (mCRPC) defined by levels of tumor mutational burden (TMB)? In this comparative effectiveness study of 741 patients with mCRPC, patients with TMB of 10 mutations per megabase (mt/Mb) or greater had significantly longer time to next treatment and overall survival with ICIs vs taxanes.

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.