NCT03829423

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.

Trial Health

35
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
1,526

participants targeted

Target at P75+ for not_applicable breast-cancer

Timeline
Completed

Started Apr 2019

Shorter than P25 for not_applicable breast-cancer

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

January 30, 2019

Completed
5 days until next milestone

First Posted

Study publicly available on registry

February 4, 2019

Completed
2 months until next milestone

Study Start

First participant enrolled

April 1, 2019

Completed
9 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 1, 2020

Completed
6 months until next milestone

Study Completion

Last participant's last visit for all outcomes

July 1, 2020

Completed
Last Updated

February 6, 2019

Status Verified

February 1, 2019

Enrollment Period

9 months

First QC Date

January 30, 2019

Last Update Submit

February 5, 2019

Conditions

Breast Cancer

Keywords

artificial intelligencemagnetic resonance imagingbreast cancer

Outcome Measures

Primary Outcomes (1)

  • Sensitivity/specificity of breast interpretation algorithm

    Sensitivity and specificity of the information provided by the breast interpretation algorithm to be above 90% and 70%, respectively

    1 year

Secondary Outcomes (2)

  • Time required for diagnosis

    1 year

  • User-friendliness of IntelliScan system

    1 year

Interventions

Breast MRI interpretationDIAGNOSTIC_TEST

Analysis and interpretation of breast MRI sequences by a specially developed breast MRI interpretation algorithm

Eligibility Criteria

Age20 Years+
Sexfemale
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • 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)

You may not qualify if:

  • Incomplete breast MRI datasets
  • Breast MRI without lesions
  • Breast lesion on MRI not biopsied

Contact the study team to confirm eligibility.

Sponsors & Collaborators

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by SiteNeoplasmsBreast DiseasesSkin DiseasesSkin and Connective Tissue Diseases

Study Officials

  • Steve Dennis, B.Sc.

    First Option Software

    STUDY DIRECTOR

Central Study Contacts

Jamil Kanfoud, M.Eng.

CONTACT

+44(0)01223940jamil.kanfoud@brunel.ac.uk

Susann Wolfram, PhD

CONTACT

+44(0)1223940s.wolfram@tees.ac.uk

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
SINGLE
Who Masked
CARE PROVIDER
Masking Details
Retrospective breast MRI datasets with all personal patient information removed
Purpose
DIAGNOSTIC
Intervention Model
SINGLE GROUP
Sponsor Type
OTHER
Responsible Party
SPONSOR INVESTIGATOR
PI Title
Head of Brunel Innovation Centre

Study Record Dates

First Submitted

January 30, 2019

First Posted

February 4, 2019

Study Start

April 1, 2019

Primary Completion

January 1, 2020

Study Completion

July 1, 2020

Last Updated

February 6, 2019

Record last verified: 2019-02