NEW YORK (Reuters Health), Jul 28 - Family history contributes differently to the risks of colon cancer and rectal cancer, suggesting these malignancies may have different causative factors, according to an analysis of data from the Icelandic Cancer Registry and a genealogy database.
Dr. Tryggvi Stefansson, from Landspitali University Hospital in Reykjavik and colleagues determined the risk of colon and rectal cancer in 23,272 first-degree relatives of 2,770 colorectal cancer patients. The findings are reported in the International Journal of Cancer for July 15.
The researchers found that first-degree relatives had a 40% overall increased risk of both colon cancer and rectal cancer. However, further analysis revealed some differences between the two malignancies.
Siblings of colon cancer patients were at elevated risk for the malignancy themselves, whereas parents and offspring of patients were not. For rectal cancer, only brothers of patients were at increased risk.
"Our results confirm that family history of colorectal cancer is a risk factor for the disease," but also reveal differences between colon and rectal cancer. These differences suggest the different etiological factors are involved in the two malignancies, the authors conclude, and the findings may also "have implications for colon and rectal cancer screening programs."
Last Updated: 2006-07-27 11:48:13 -0400 (Reuters Health)
Int J Cancer 2006;119:304-308.
Related Reading
A single negative colonoscopy may suffice for average-risk patients, July 25, 2006
Copyright © 2006 Reuters Limited. All rights reserved. Republication or redistribution of Reuters content, including by framing or similar means, is expressly prohibited without the prior written consent of Reuters. Reuters shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon. Reuters and the Reuters sphere logo are registered trademarks and trademarks of the Reuters group of companies around the world.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
