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临床试验/NCT03829423
NCT03829423Unknown不适用

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

Jamil Kanfoud0 个研究点目标入组 1,526 人开始时间: 2019年4月1日最近更新:
适应症

试验速览

阶段
不适用
发起方
入组人数
1,526
主要终点
Sensitivity/specificity of breast interpretation algorithm

研究概览

简要总结

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.

详细描述

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.

研究设计

研究类型
Interventional
分配方式
Na
干预模型
Single Group
主要目的
Diagnostic
盲法
Single (Care Provider)

盲法说明

Retrospective breast MRI datasets with all personal patient information removed

入排标准

年龄范围
20 Years 至 —(Adult, Older Adult)
性别
Female
接受健康志愿者
否

入选标准

  • •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)

排除标准

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

结局指标

主要结局

Sensitivity/specificity of breast interpretation algorithm

时间窗: 1 year

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

次要结局

  • Time required for diagnosis(1 year)
  • User-friendliness of IntelliScan system(1 year)

研究者

发起方
Jamil Kanfoud
申办方类型
Other
责任方
Sponsor Investigator
主要研究者

Jamil Kanfoud

Head of Brunel Innovation Centre

Teesside University

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