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ONCOassist adds PREDICT algorithm to help doctors make critical treatment decisions following breast cancer surgery

CAMBRIDGE, UK, TK September, 2016—The PREDICT breast treatment tool, designed to help doctors determine the ideal course of treatment following a patients’ breast cancer surgery, is now available to oncology healthcare professionals globally via ONCOassist, an interactive, clinical decision support app.  ONCOassist provides tools that oncology professionals rely on daily.
PREDICT is the first breast cancer model of this type to include tumour HER2 and KI67 status in its decision making algorithm.

Making the PREDICT tool available through ONCOassist expands the tool’s reach to a global audience. It is available both on desktop and mobile devices (iOS and Android) in an easy-to-use and interactive format.

ONCOassist also includes adjuvant decision support calculators for other cancers, useful formulas, prognostic algorithms, American Joint Cancer Committee (AJCC) staging and Common Toxicity Criteria for Adverse Events (CTCAE) tools.
ONCOassist is used widely, around the world, and is offered as a member benefit by the European Society of Medical Oncology. It is one of the few medical apps on the market to have CE approval, meaning it is fully compliant with EU medical device standards.
ONCOassist Chief Medical Officer Dr Richard Bambury said: “The addition of the PREDICT breast algorithm will enhance our platform with new functionality to help clinicians make informed decisions about adjuvant treatment following breast cancer surgery. This will be of huge benefit to our global userbase.”
Professor Paul Pharoah, of the University of Cambridge, who was part of the development team, said: “PREDICT is used by cancer doctors from around the world.  We hope that including the model in ONCOassist will encourage more doctors to use the model and help improve outcomes for breast cancer patients.”

The technology was licensed to ONCOassist by Cambridge Enterprise, the commercialisation arm of the University of Cambridge.

About ONCOassist

ONCOassist is a clinical decision support app for oncology professionals. It contains all the clinical tools oncology professionals need in an easy to use and interactive format. It is offered as a member benefit by the European Society of Medical Oncology and has rapidly growing global userbase. ONCOassist was found in 2012 by Kevin Bambury, Eoin O’Carroll and Dr Richard Bambury.

About PREDICT

PREDICT is a mathematical model designed for patients and doctors to help them decide on the ideal course of treatment following breast cancer surgery. It is the first model of its type to include tumour HER2 and KI67 status.

PREDICT was developed by a partnership among The Breast Unit at Cambridge University Hospitals NHS Trust (CUH), the University of Cambridge Department of Oncology and the NHS Eastern Cancer Registry and Information Centre (ECRIC).

About Cambridge Enterprise

Cambridge Enterprise Limited is a wholly owned subsidiary of the University of Cambridge, responsible for the commercialisation of University intellectual property. Activities include management and licensing of intellectual property and patents, proof of concept funding and support for University staff and research groups wishing to provide expert advice or facilities to public and private sector organisations. Cambridge Enterprise provides access to angel and early stage capital through the Cambridge Enterprise Seed Funds, University of Cambridge Enterprise Funds, Cambridge Innovation Capital and Cambridge Enterprise Venture Partners, and offers business planning, mentoring, and other related programmes.

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Media Contacts

Cambridge Enterprise

Catherine Aman

+44 (0)1223 760335

catherine.aman@enterprise.cam.ac.uk
ONCOassist
Kevin Bambury
+ 353 21 731 9541

kevin@portablemedicaltechnology.com

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1 Comment

  1. Clinical Decision Support Tools says:
    January 30, 2018 at 7:24 am

    I really like your tool and appreciate your work. Such tools also help patients and caregivers in optimal decision making, as they estimate the absolute benefits and risks of treatment.

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