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An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment
- Source :
- Interface Focus
- Publication Year :
- 2019
-
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.
- Subjects :
- medicine.medical_specialty
Modalities
business.industry
0206 medical engineering
Biomedical Engineering
Biophysics
Bioengineering
02 engineering and technology
Articles
Computational biomechanics
medicine.disease
020601 biomedical engineering
Biochemistry
030218 nuclear medicine & medical imaging
Biomaterials
03 medical and health sciences
0302 clinical medicine
Workflow
Breast cancer
Medical imaging
medicine
Medical physics
business
Biotechnology
Subjects
Details
- ISSN :
- 20428898
- Volume :
- 9
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- Interface focus
- Accession number :
- edsair.doi.dedup.....2452a5efe8fb4fc1ee2506da40137e61