An Enhanced Artificial Intelligence Breast MRI Interpretation System (IntelliScan)

February 5, 2019 updated by: Jamil Kanfoud

A Comparative Single-centre Study to Evaluate an Enhanced Artificial Intelligence Breast MRI Interpretation System in Women Over 20 With Breast Lesions

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.

Study Overview

Status

Unknown

Conditions

Breast Cancer

Intervention / Treatment

Diagnostic test: Breast MRI interpretation

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.

Study Type

Interventional

Enrollment (Anticipated)

1526

Phase

Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

20 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

Genders Eligible for Study

Female

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Primary Purpose: Diagnostic
Allocation: N/A
Interventional Model: Single Group Assignment
Masking: Single

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity/specificity of breast interpretation algorithm Time Frame: 1 year	Sensitivity and specificity of the information provided by the breast interpretation algorithm to be above 90% and 70%, respectively	1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Time required for diagnosis Time Frame: 1 year	The time required to arrive at a diagnosis using IntelliScan should be less than using manual procedures	1 year
User-friendliness of IntelliScan system Time Frame: 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])	1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Jamil Kanfoud

Collaborators

Brunel University London

First Option Software Ltd.

Investigators

Study Director: Steve Dennis, B.Sc., First Option Software

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

April 1, 2019

Primary Completion (Anticipated)

January 1, 2020

Study Completion (Anticipated)

July 1, 2020

Study Registration Dates

First Submitted

January 30, 2019

First Submitted That Met QC Criteria

February 1, 2019

First Posted (Actual)

February 4, 2019

Study Record Updates

Last Update Posted (Actual)

February 6, 2019

Last Update Submitted That Met QC Criteria

February 5, 2019

Last Verified