Introduction And Objective: With respect to the increasing numbers of deaths due to colorectal cancer in Poland, the aim of the study was to investigate socio-demographic characteristics which influence colorectal cancer screening acceptance and to assess spatial variation of colorectal cancer mortality.
Material And Methods: An age-period-cohort model was estimated to assess mortality trends in colorectal cancer in Poland. A geographical analysis was performed by spatial regression. Factors influencing participation in colorectal cancer screening were identified using structural equation modelling.
Results: In 2014 in Poland, 6.4 thousand men and 5.0 thousand women died due to colorectal cancer. In total, by 2030 this number is expected to rise to nearly 14.4 thousand. Observed spatial clustering of age-adjusted colorectal cancer mortality is associated with spatial variation in tobacco use, employment in industry, and consumption of red meat. Patient-physician communication, advanced age, and healthy diet are the most important predictors of colorectal cancer screening acceptance. Tobacco and alcohol users are not more likely to participate in colorectal cancer screening, adjusting for other variables.
Conclusions: Self-selection of patients who follow healthy diet means that individuals at higher risk of colorectal cancer are less likely to participate in colorectal cancer screening. Therefore, screening should be more targeted. According to the structural equation modelling results, the phenomenon of 'no-show' for screening can be mitigated by patient-physician communication. The inhabitants of the Greater Poland region are at the highest risk of dying due to colorectal cancer, which may have public health policy implications.
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http://dx.doi.org/10.26444/aaem/99233 | DOI Listing |
J Exp Clin Cancer Res
January 2025
Department of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
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Colorectal Surgery Department, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/ Hunan Cancer Hospital, No. 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, China.
Objective: The clinical benefits of neoadjuvant bevacizumab plus chemotherapy in locally advanced gastric cancer patients are controversial. This study intended to evaluate the efficacy and safety of neoadjuvant bevacizumab plus chemotherapy in these patients.
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J Transl Med
January 2025
Department of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
Background: Tumor microenvironment (TME), particularly immune cell infiltration, programmed cell death (PCD) and stress, has increasingly become a focal point in colorectal cancer (CRC) treatment. Uncovering the intricate crosstalk between these factors can enhance our understanding of CRC, guide therapeutic strategies, and improve patient prognosis.
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BMC Cancer
January 2025
Department of Laboratory Medicine, Affiliated Gaozhou People's Hospital, Guangdong Medical University, Maoming, 525200, P.R. China.
Background: DNA hypomethylation and uracil misincorporation into DNA, both of which have a very important correlation with colorectal carcinogenesis. Folate plays a crucial role in DNA synthesis, acting as a coenzyme in one-carbon metabolism, which involves the synthesis of purines, pyrimidines, and methyl groups. MTHFR, a key enzyme in folate metabolism, has been widely studied in relation to neural tube defects and hypertension, but its role in colorectal cancer remains underexplored.
View Article and Find Full Text PDFBMC Med Res Methodol
January 2025
Leeds Institute of Clinical Trials Research, University of Leeds, Clarendon Way, Leeds, LS2 9NL, UK.
Background: Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that fully exploit the longitudinal data stored within electronic health records (EHRs). This review aims to summarise methods currently utilised for prediction of cancer from longitudinal data and provides recommendations on how such models should be developed.
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