Abstract:
:Clinicians face many challenges when diagnosing and treating breast cancer. These challenges include interpreting and co-locating information between different medical imaging modalities that are used to identify tumours and predicting where these tumours move to during different treatment procedures. We have developed a novel automated breast image analysis workflow that integrates state-of-the-art image processing and machine learning techniques, personalized three-dimensional biomechanical modelling and population-based statistical analysis to assist clinicians during breast cancer detection and treatment procedures. This paper summarizes our recent research to address the various technical and implementation challenges associated with creating a fully automated system. The workflow is applied to predict the repositioning of tumours from the prone position, where diagnostic magnetic resonance imaging is performed, to the supine position where treatment procedures are performed. We discuss our recent advances towards addressing challenges in identifying the mechanical properties of the breast and evaluating the accuracy of the biomechanical models. We also describe our progress in implementing a prototype of this workflow in clinical practice. Clinical adoption of these state-of-the-art modelling techniques has significant potential for reducing the number of misdiagnosed breast cancers, while also helping to improve the treatment of patients.
journal_name
Interface Focusjournal_title
Interface focusauthors
Babarenda Gamage TP,Malcolm DTK,Maso Talou G,Mîra A,Doyle A,Nielsen PMF,Nash MPdoi
10.1098/rsfs.2019.0034subject
Has Abstractpub_date
2019-08-06 00:00:00pages
20190034issue
4eissn
2042-8898issn
2042-8901pii
rsfs20190034journal_volume
9pub_type
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