Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24350
Appears in Collections:Faculty of Health Sciences and Sport Journal Articles
Peer Review Status: Refereed
Title: iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management
Authors: Collins, Ian
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, John
Antoniou, Antonis C
Phillips, Kelly-Anne
Contact Email: emma.steel@stir.ac.uk
Keywords: Breast cancer
Risk
Decision support
BRCA1
Chemoprevention
Issue Date: Feb-2016
Publisher: Springer
Citation: Collins I, Bickerstaffe A, Ranaweera T, Maddumarachchi S, Keogh L, Emery J, Mann GB, Butow P, Weideman P, Steel E, Trainer A, Bressel M, Hopper JL, Cuzick J, Antoniou AC & Phillips K (2016) iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management, Breast Cancer Research and Treatment, 156 (1), pp. 171-182.
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® 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® 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®, risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent®, IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent® (i.e., IBIS or BOADICEA) with the programmed iPrevent® model choice algorithm was assessed. Estimated breast cancer risks from iPrevent® were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent® were assessed for appropriateness. Risk estimation model choice was 100% consistent with the programmed iPrevent®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® were 100% appropriate. iPrevent® 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.
Type: Journal Article
URI: http://hdl.handle.net/1893/24350
DOI Link: http://dx.doi.org/10.1007/s10549-016-3726-y
Rights: © The Author(s) 2016 This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Affiliation: Peter MacCallum Cancer Centre
University of Melbourne
University of Melbourne
University of Melbourne
University of Melbourne
University of Melbourne
Royal Women's Hospital (Victoria Australia)
University of Sydney
Peter MacCallum Cancer Centre
University of Melbourne
Peter MacCallum Cancer Centre
Peter MacCallum Cancer Centre
University of Melbourne
Queen Mary, University of London
Peter MacCallum Cancer Centre
University of Melbourne

Files in This Item:
File Description SizeFormat 
Collins-etal-BCRT-2016.pdf1.68 MBAdobe PDFView/Open


This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.