Evidence map›Paper›PMID 26681340›Full record

ArticleFamilial cancer2016

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 and 3 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Familial cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 7 institutions in 3 countries.

Rowena ChauCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie Street, Parkville, VIC, 3010, Australia.
Mark A JenkinsCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie Street, Parkville, VIC, 3010, Australia.
Daniel D BuchananCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie Street, Parkville, VIC, 3010, Australia.
Driss Ait OuakrimCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie Street, Parkville, VIC, 3010, Australia.
Graham G GilesCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie Street, Parkville, VIC, 3010, Australia.
Graham CaseyDepartment of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Steven GallingerSamuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada.
Robert W HaileDivision of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA.
Loic Le MarchandUniversity of Hawaii Cancer Center, Honolulu, HI, USA.
Polly A NewcombCancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Noralane M LindorDepartment of Health Science Research, Mayo Clinic Arizona, Scottsdale, AZ, USA.
John L HopperCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie Street, Parkville, VIC, 3010, Australia.
Aung Ko WinCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie Street, Parkville, VIC, 3010, Australia. awin@unimelb.edu.au.ORCID http://orcid.org/0000-0002-2794-5261
The University of Melbourne · AUFred Hutch Cancer Center · USMayo Clinic in Arizona · USMount Sinai Hospital · CAStanford University · USUniversity of Hawaiʻi at Mānoa · USUniversity of Southern California · US

Funding

CALIFORNIA COORDINATED CANCER PREVENTION & CONTROL PROGRAMU58DP003862 · DP · CALIFORNIA STATE DEPT OF PUBLIC HEALTH · PI SNIPES, KURT P · 2012 to 2014
$31.5M
USC Consortium:Colorectal Cancer Family RegistryU01CA074799 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HAILE, ROBERT WILLIAM · 1997 to 2008
$20.4M
SEATTLE FAMILIAL COLORECTAL CANCER REGISTRYU01CA074794 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI NEWCOMB, POLLY A · 1997 to 2007
$14.1M
ONTARIO REGISTRY FOR STUDIES OF FAMILIAL COLON CANCERU01CA074783 · NCI · CANCER CARE ONTARIO · PI GALLINGER, STEVEN · 1997 to 2008
$12.5M
Colon Cancer Family Registry CohortUM1CA167551 · NCI · STANFORD UNIVERSITY · PI HAILE, ROBERT WILLIAM, JENKINS, MARK A · 2013 to 2017
$11.3M
FAMILIAL COLORECTAL NEOPLASIA COLLABORATIVE GROUPU01CA074800 · NCI · MAYO CLINIC ROCHESTER · PI LINDOR, NORALANE MOREY · 1997 to 2007
$9.1M
The Colon Cancer Family Registry: USC ConsortiumU24CA074799 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HAILE, ROBERT WILLIAM · 2008 to 2011
$7.6M
The Colon Cancer Family Registry: SeattleU24CA074794 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI NEWCOMB, POLLY A · 2008 to 2011
$7.0M
AUSTRALASIAN COLORECTAL CANCER FAMILY REGISTRYU01CA097735 · NCI · UNIVERSITY OF MELBOURNE · PI HOPPER, JOHN L · 2002 to 2007
$6.9M
The Colon Cancer Family Registry: AustralasiaU24CA097735 · NCI · UNIVERSITY OF MELBOURNE · PI HOPPER, JOHN L · 2008 to 2011
$6.2M
HAWAII FAMILY REGISTRY OF COLON CANCERU01CA074806 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI LE MARCHAND, LOIC · 1997 to 2008
$6.1M
The Familial Colorectal Neoplasia Collaborative GroupU24CA074800 · NCI · MAYO CLINIC ROCHESTER · PI LINDOR, NORALANE MOREY, THIBODEAU, STEPHEN NORMAN · 2008 to 2011
$5.3M
CCR NIH HHS HHSN261201300011CNCCDPHP CDC HHS U58 DP003862NCCDPHP CDC HHS U58DP003862-01NCI NIH HHS HHSN261201000034CNCI NIH HHS HHSN261201000035CNCI NIH HHS HHSN261201000035INCI NIH HHS HHSN261201000121CNCI NIH HHS HHSN261201300012INCI NIH HHS HHSN261201300021CNCI NIH HHS K05 CA152715NCI NIH HHS N01 CN067009NCI NIH HHS N01 PC035142NCI NIH HHS N01-PC-35142NCI NIH HHS U01 CA074783NCI NIH HHS U01 CA074794NCI NIH HHS U01 CA074799NCI NIH HHS U01 CA074800NCI NIH HHS U01 CA074806NCI NIH HHS U01 CA097735NCI NIH HHS U01/U24 CA074783NCI NIH HHS U01/U24 CA074794NCI NIH HHS U01/U24 CA074799NCI NIH HHS U01/U24 CA074800NCI NIH HHS U01/U24 CA097735NCI NIH HHS U24 CA074783NCI NIH HHS U24 CA074794NCI NIH HHS U24 CA074799NCI NIH HHS U24 CA074800NCI NIH HHS U24 CA074806NCI NIH HHS U24 CA097735NCI NIH HHS UM1 CA167551
6 · The paper itself

Abstract

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.

Indexed as

AgedBreast NeoplasmsColorectal NeoplasmsData MiningDNA-Binding ProteinsFemaleHumansMaleMiddle AgedMismatch Repair Endonuclease PMS2MutL Protein Homolog 1MutS Homolog 2 ProteinOntarioPedigreeProstatic NeoplasmsRegistriesDNA-Binding ProteinsG-T mismatch-binding proteinMismatch Repair Endonuclease PMS2MLH1 protein, humanMSH2 protein, humanMutL Protein Homolog 1MutS Homolog 2 ProteinPMS2 protein, humanColorectal cancerData miningFamilial aggregationFamilial risk

Identifiers

PMID26681340
PMCPMC4803603
OpenAlexW2200265085

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.