Obesity and weight gain linked to prostate cancer mortality

2007 ◽  
Keyword(s):  
Prostate Cancer ◽  
Weight Gain ◽  
Cancer Mortality ◽  
Prostate Cancer Mortality

535: Population-Based Case-Control Study of PSA and DRE Screening on Prostate Cancer Mortality

2005 ◽  
Vol 173 (4S) ◽  
pp. 146-146
Author(s):  
Eric J. Bergstralh ◽  
Rosebud O. Roberts ◽  
Michael M. Lieber ◽  
Sara A. Farmer ◽  
Jeffrey M. Slezak ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Mortality ◽  
Case Control Study ◽  
Population Based ◽  
Case Control ◽  
Prostate Cancer Mortality ◽  
Control Study

435: When is Decreased Prostate Cancer Mortality to be Expected Provided that Efficacious Prostate Cancer Screening is Introduced?

2004 ◽  
Vol 171 (4S) ◽  
pp. 115-115
Author(s):  
Magnus Törnblom ◽  
Henry Eriksson ◽  
Stefan Franzen ◽  
Ove Gustafsson ◽  
Hans Lilja ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Screening ◽  
Cancer Mortality ◽  
Prostate Cancer Screening ◽  
Prostate Cancer Mortality

scholarly journals Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3064
Author(s):  
Jean-Emmanuel Bibault ◽  
Steven Hancock ◽  
Mark K. Buyyounouski ◽  
Hilary Bagshaw ◽  
John T. Leppert ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Treatment ◽  
Cancer Mortality ◽  
Treatment Strategies ◽  
Gradient Boosting ◽  
Prostate Cancer Treatment ◽  
Prospective Data ◽  
Prostate Cancer Mortality ◽  
Development And Validation

Prostate cancer treatment strategies are guided by risk-stratification. This stratification can be difficult in some patients with known comorbidities. New models are needed to guide strategies and determine which patients are at risk of prostate cancer mortality. This article presents a gradient-boosting model to predict the risk of prostate cancer mortality within 10 years after a cancer diagnosis, and to provide an interpretable prediction. This work uses prospective data from the PLCO Cancer Screening and selected patients who were diagnosed with prostate cancer. During follow-up, 8776 patients were diagnosed with prostate cancer. The dataset was randomly split into a training (n = 7021) and testing (n = 1755) dataset. Accuracy was 0.98 (±0.01), and the area under the receiver operating characteristic was 0.80 (±0.04). This model can be used to support informed decision-making in prostate cancer treatment. AI interpretability provides a novel understanding of the predictions to the users.

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