The evidence concerning the possible association between physical activity and the risk of prostate cancer is inconsistent and additional data are needed. We examined the association between risk of prostate cancer and physical activity at work and in leisure time in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. In our study, including 127,923 men aged 20-97 years from 8 European countries, 2,458 cases of prostate cancer were identified during 8.5 years of followup. Using the Cox proportional hazards model, we investigated the associations between prostate cancer incidence rate and occupational activity and leisure time activity in terms of participation in sports, cycling, walking and gardening; a metabolic equivalent (MET) score based on weekly time spent on the 4 activities; and a physical activity index. MET hours per week of leisure time activity, higher score in the physical activity index, participation in any of the 4 leisure time activities, and the number of leisure time activities in which the participants were active were not associated with prostate cancer incidence. However, higher level of occupational physical activity was associated with lower risk of advanced stage prostate cancer (p(trend) = 0.024). In conclusion, our data support the hypothesis of an inverse association between advanced prostate cancer risk and occupational physical activity, but we found no support for an association between prostate cancer risk and leisure time physical activity.
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http://dx.doi.org/10.1002/ijc.24326 | DOI Listing |
Ann Nucl Med
January 2025
Department of Biomedical Sciences, Humanitas University, Milan, Italy.
The purpose of this systematic review was to evaluate the role of PSMA PET/CT in intermediate-risk prostate cancer (PCa) patients, to determine whether it could help improve treatment strategy and prognostic stratification. A systematic literature search up to May 2024 was conducted in the PubMed, Embase and Scopus databases. Articles with mixed risk patient populations, review articles, editorials, letters, comments, or case reports were excluded.
View Article and Find Full Text PDFJ Neurooncol
January 2025
Department of Neurosurgery, Allegheny Health Network, Neuroscience Institute, Pittsburgh, PA, United States.
Langenbecks Arch Surg
January 2025
Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Purpose: Assessing surgical skills is vital for training surgeons, but creating objective, automated evaluation systems is challenging, especially in robotic surgery. Surgical procedures generally involve dissection and exposure (D/E), and their duration and proportion can be used for skill assessment. This study aimed to develop an AI model to acquire D/E parameters in robot-assisted radical prostatectomy (RARP) and verify if these parameters could distinguish between novice and expert surgeons.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.
Mov Disord Clin Pract
January 2025
The Edmond J. Safra Program in Parkinson's Disease, University Health Network and the University of Toronto, Toronto, Ontario, Canada.
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