Breast Cancer
Conditions
Keywords
artificial intelligence, magnetic resonance imaging, breast cancer
Brief summary
Interpretation of breast MR images is a very time-consuming process and places a great burden on breast radiologists. This project aims to develop a technical solution that addresses this healthcare challenge by developing a system that is able to automatically interpret breast MR images in order to aid the radiologist in their diagnosis.
Detailed description
Breast cancer is the most common type of cancer in women worldwide, with nearly 1.7 million new cases diagnosed in 2015. In the UK, one in five cases of breast cancer results in a fatality. The IntelliScan project aims to develop a technological solution that addresses a significant healthcare challenge. IntelliScan will develop a software system that will be able to interpret breast MR images automatically in order to identify potential breast cancers. Regular MRI screening of the breast is offered to women from the age of 20, who are at higher risk of developing breast cancer. MR image sequences provide a large amount of information to the radiologist and the interpretation of images is a manual process, which is very time consuming. The high number of women eligible for MRI screening combined with the amount of data provided by MRI scans places a great burden on healthcare systems. Therefore, automatisation of this process would greatly relieve this burden and also has the potential to provide more accurate diagnoses. In this first study, the system's user interface as well as the algorithm will be developed using existing MRI scans. Existing MRI scans with known breast anomalies will be used to develop the decision-making basis for the algorithm. The system will then be tested using existing MRI scans without information about possible anomalies and results will be compared to results from the software system currently in use. In addition, the user-friendliness of the system's user interface will also be evaluated.
Interventions
DIAGNOSTIC_TESTBreast MRI interpretation
Analysis and interpretation of breast MRI sequences by a specially developed breast MRI interpretation algorithm
Sponsors
Brunel University London
First Option Software Ltd.
Jamil Kanfoud
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Caregiver)
Masking description
Retrospective breast MRI datasets with all personal patient information removed
Eligibility
Sex/Gender
FEMALE
Age
20 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Breast MRI scans * MRI examinations undertaken at partner NHS Trust in the UK * MRI examinations undertaken on the MRI system currently installed at partner NHS Trust site (since 2008)
Exclusion criteria
* Incomplete breast MRI datasets * Breast MRI without lesions * Breast lesion on MRI not biopsied
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity/specificity of breast interpretation algorithm | 1 year | Sensitivity and specificity of the information provided by the breast interpretation algorithm to be above 90% and 70%, respectively |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Time required for diagnosis | 1 year | The time required to arrive at a diagnosis using IntelliScan should be less than using manual procedures |
| User-friendliness of IntelliScan system | 1 year | Obviousness score for categorisation of beast lesions (0 \[not obvious\] to 100 \[extremely obvious\]); ease-of-use score for IntelliScan system (0 \[not easy to use\] to 10 \[extremely easy to use\]) |
Contacts
Primary ContactJamil Kanfoud, M.Eng.
Backup ContactSusann Wolfram, PhD
Outcome results
None listed