Determining the familial risk distribution of colorectal cancer: a data mining approach

Rowena Chau, Mark A. Jenkins, Daniel D. Buchanan, Driss Ait Ouakrim, Graham G. Giles, Graham Casey, Steven Gallinger, Robert W. Haile, Loic Le Marchand, Polly A. Newcomb, Noralane M. Lindor, John L. Hopper, Aung Ko Win

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


This study was aimed to characterize the distribution of colorectal cancer risk using family history of cancers by data mining. Family histories for 10,066 colorectal cancer cases recruited to population cancer registries of the Colon Cancer Family Registry were analyzed using a data mining framework. A novel index was developed to quantify familial cancer aggregation. Artificial neural network was used to identify distinct categories of familial risk. Standardized incidence ratios (SIRs) and corresponding 95 % confidence intervals (CIs) of colorectal cancer were calculated for each category. We identified five major, and 66 minor categories of familial risk for developing colorectal cancer. The distribution the major risk categories were: (1) 7 % of families (SIR = 7.11; 95 % CI 6.65–7.59) had a strong family history of colorectal cancer; (2) 13 % of families (SIR = 2.94; 95 % CI 2.78–3.10) had a moderate family history of colorectal cancer; (3) 11 % of families (SIR = 1.23; 95 % CI 1.12–1.36) had a strong family history of breast cancer and a weak family history of colorectal cancer; (4) 9 % of families (SIR = 1.06; 95 % CI 0.96–1.18) had strong family history of prostate cancer and weak family history of colorectal cancer; and (5) 60 % of families (SIR = 0.61; 95 % CI 0.57–0.65) had a weak family history of all cancers. There is a wide variation of colorectal cancer risk that can be categorized by family history of cancer, with a strong gradient of colorectal cancer risk between the highest and lowest risk categories. The risk of colorectal cancer for people with the highest risk category of family history (7 % of the population) was 12-times that for people in the lowest risk category (60 %) of the population. Data mining was proven an effective approach for gaining insight into the underlying cancer aggregation patterns and for categorizing familial risk of colorectal cancer.

Original languageEnglish (US)
Pages (from-to)241-251
Number of pages11
JournalFamilial Cancer
Issue number2
StatePublished - Apr 1 2016


  • Colorectal cancer
  • Data mining
  • Familial aggregation
  • Familial risk

ASJC Scopus subject areas

  • Genetics
  • Oncology
  • Genetics(clinical)
  • Cancer Research


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