Please use this identifier to cite or link to this item: https://repository.southwesthealthcare.com.au/swhealthcarejspui/handle/1/3359
Journal Title: iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management
Authors: Collins, Ian M.
Bickerstaffe, Adrian
Ranaweera, Thilina
Maddumarachchi, Sanjaya
Keogh, Louise
Emery, Jon
Mann, G. Bruce
Butow, Phyllis
Weideman, Prue
Steel, Emma
Trainer, Alison
Bressel, Mathias
Hopper, John L.
Cuzick, Jack
Antoniou, Antonis C.
Phillips, Kelly-Anne
SWH Author: Collins, Ian M.
Keywords: Algorithms
Australia
Breast Neoplasms Prevention & Control
Evidence-Based Medicine
Female
Human
Internet
Models
Precision Medicine
Risk Assessment
Risk Factor
User-Computer Interface
Breast Cancer
Chemoprevention
Decision Support
Risk
Issue Date: 2016
Date Accessioned: 2023-03-17T04:56:43Z
Date Available: 2023-03-17T04:56:43Z
Accession Number: 26909793
Url: https://www.ncbi.nlm.nih.gov/pubmed/26909793
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4788692/pdf/10549_2016_Article_3726.pdf
Description Affiliation: Division of Cancer Medicine, Peter MacCallum Cancer Centre, Locked Bag 1, A'Beckett St., Melbourne, VIC, Australia.
The Greater Green Triangle Clinical School, Deakin University School of Medicine, Warrnambool, Australia.
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
Department of General Practice, The University of Melbourne, Melbourne, Australia.
The Breast Service, Royal Melbourne and Royal Women's Hospital, Melbourne, Australia.
Department of Surgery, The University of Melbourne, Melbourne, Australia.
Centre for Medical Psychology and Evidence-based Decision-Making (CeMPED) and The Psycho-Oncology Cooperative Research Group (PoCoG), The University of Sydney, Sydney, Australia.
Department of Medicine, The University of Melbourne, Melbourne, Australia.
Department of Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, Australia.
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK.
Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Division of Cancer Medicine, Peter MacCallum Cancer Centre, Locked Bag 1, A'Beckett St., Melbourne, VIC, Australia. kelly.phillips@petermac.org.
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia. kelly.phillips@petermac.org.
Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia. kelly.phillips@petermac.org.
Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia. kelly.phillips@petermac.org.
Format Startpage: 171-82
Source Volume: 156
Issue Number: 1
Database: Medline
Notes: eng
Cancer Research UK/United Kingdom
Research Support, Non-U.S. Gov't
Netherlands
2016/02/26
Breast Cancer Res Treat. 2016 Feb;156(1):171-82. doi: 10.1007/s10549-016-3726-y. Epub 2016 Feb 24.
DOI: 10.1007/s10549-016-3726-y
Date: Feb
Abstract: We aimed to develop a user-centered, web-based, decision support tool for breast cancer risk assessment and personalized risk management. Using a novel model choice algorithm, iPrevent((R)) selects one of two validated breast cancer risk estimation models (IBIS or BOADICEA), based on risk factor data entered by the user. Resulting risk estimates are presented in simple language and graphic formats for easy comprehension. iPrevent((R)) then presents risk-adapted, evidence-based, guideline-endorsed management options. Development was an iterative process with regular feedback from multidisciplinary experts and consumers. To verify iPrevent((R)), risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent((R)), IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent((R)) (i.e., IBIS or BOADICEA) with the programmed iPrevent((R)) model choice algorithm was assessed. Estimated breast cancer risks from iPrevent((R)) were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent((R)) were assessed for appropriateness. Risk estimation model choice was 100 % consistent with the programmed iPrevent((R)) logic. Discrepant 10-year and residual lifetime risk estimates of >1 % were found for 1 and 4 cases, respectively, none was clinically significant (maximal variation 1.4 %). Risk management interventions suggested by iPrevent((R)) were 100 % appropriate. iPrevent((R)) successfully integrates the IBIS and BOADICEA risk assessment models into a decision support tool that provides evidence-based, risk-adapted risk management advice. This may help to facilitate precision breast cancer prevention discussions between women and their healthcare providers.
URI: https://repository.southwesthealthcare.com.au/swhealthcarejspui/handle/1/3359
Journal Title: Breast Cancer Research and Treatment
Type: Journal Article
Appears in Collections:SWH Staff Publications



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