Purpose: Estimates of familial colorectal cancer risks are useful in genetic counseling and as a guide to determining entry into screening programs and trials of chemoprevention. Furthermore, they provide an insight into the contribution of the known colorectal cancer genes to the familial risk of the disease. There is a paucity of data about the familial colorectal cancer risk associated with early-onset disease outside the recognized cancer predisposition syndromes.
Methods: This was a retrospective cohort study. The parents and siblings of 205 patients with colorectal cancer aged less than 55 years at diagnosis were studied for mortality and cancer incidence.
Results: The overall standardized mortality ratio of colorectal cancer compared with the Northern Irish population was 3.54 (95 percent confidence interval, 2.59-4.79). There was some evidence that a family history of colorectal cancer is associated with a greater risk of colon (4.16; 95 percent confidence interval, 2.83-5.91) rather than rectal cancer (2.62; 95 percent confidence interval, 1.43-4.40). Risks in parents (2.54; 95 percent confidence interval, 1.45-3.72) were lower than in siblings (6.15; 95 percent confidence interval, 3.90-9.23).
Conclusion: First-degree relatives of patients with early-onset disease are at a marked increase in risk. There is evidence that risks vary depending on the type of affected relative and by the site of colorectal cancer. This information should be considered in formulating screening strategies.
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http://dx.doi.org/10.1007/s10350-004-6267-0 | DOI Listing |
BMC Cancer
January 2025
Department of Gastrointestinal Surgery I Section, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
Background: Gastric cancer peritoneal metastasis lacks effective predictive indices. This article retrospectively explored predictive values of DNA ploidy, stroma, and nucleotyping in gastric cancer peritoneal metastasis.
Methods: A comprehensive analysis was conducted on specimens obtained from 80 gastric cancer patients who underwent gastric resection at the Department of Gastrointestinal Surgery of Wuhan University Renmin Hospital.
Ann Surg Oncol
January 2025
Division of Colorectal Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China.
Nat Med
January 2025
Vall d'Hebron Hospital Campus and Vall d'Hebron Institute of Oncology (VHIO), University of Vic - Central University of Catalonia, Barcelona, Spain.
Encorafenib + cetuximab (EC) is approved for previously treated BRAF V600E-mutant metastatic colorectal cancer (mCRC) based on the BEACON phase 3 study. Historically, first-line treatment of BRAF V600E-mutant mCRC with chemotherapy regimens has had limited efficacy. The phase 3 BREAKWATER study investigated EC+mFOLFOX6 versus standard of care (SOC) in patients with previously untreated BRAF V600E mCRC.
View Article and Find Full Text PDFInt J Colorectal Dis
January 2025
Hereditary Digestive Tract Tumors Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Via Giacomo Venezian 1, 20133, Milan, Italy.
Purpose: In this study, we investigated the progression of high-grade dysplasia (HGD)/CRC in patients with hereditary colorectal cancer syndromes (HCSS) and concomitant inflammatory bowel diseases (IBDs).
Methods: We described the natural history of a series of patients with confirmed diagnosis of hereditary colorectal cancer syndromes (HCCSs) and concomitant IBDs who were referred to the Hereditary Digestive Tumors Registry at the Fondazione IRCCS Istituto Nazionale dei Tumori of Milan.
Results: Between January 1989 and April 2024, among 450 patients with APC-associated polyposis and 1050 patients with Lynch syndrome (LS), we identified six patients with IBDs (five with UC, one with ileal penetrating CD) and concomitant HCCSs (five with LS, one with APC-associated polyposis).
Sci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!