Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization

August 19, 2021 updated by: Hospital Italiano de Buenos Aires
The correct categorization of breast density is essential to adapt the diagnostic examination to the needs of each patient. Assessment of breast density is performed visually by radiologists. Some authors have detected that this method involves considerable intra and interobserver variability. On the other hand, automated systems for measuring breast density are becoming more and more frequent. Machine learning is a domain of Artificial Intelligence, which comprises the process of developing systems with the ability to learn and make predictions using data. These systems are designed to aid healthcare professional decision making. In the present work, the multicenter study of external validation of a tool based on deep learning for the categorization of mammographic breast density is proposed.

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

The correct categorization of breast density is essential to adapt the diagnostic examination to the needs of each patient. Assessment of breast density is performed visually by radiologists. Some authors have detected that this method involves considerable intra and interobserver variability. On the other hand, automated systems for measuring breast density are becoming more and more frequent. Consequently, in clinical practice, breast density is reported from the assessment carried out by specialists with the support of these systems. But there are few studies about the use, concordance and perception of usefulness of professionals on these tools. A study carried out at the Hospital Italiano de Buenos Aires reported a moderate to almost perfect inter- and intra-observer agreement among radiologists and a moderate concordance between the categorization carried out by experts and that carried out by commercial software of a digital mammography machine. Machine learning is a domain of Artificial Intelligence, which comprises the process of developing systems with the ability to learn and make predictions using data. Once a system designed to aid healthcare professional decision making is developed, it must be validated. In 2019, an internal validation of a tool based on deep learning techniques was carried out for the automatic categorization of mammographic breast density. The tool reached a very good interobserver agreement, kappa = 0.64 (95% CI 0.58-0.69), when compared with the performance of the professionals. It reached a sensitivity of 83.2 (CI: 76.9-88.3) and a specificity of 88.4 (83.9-92.0.) In the present work, the multicenter study of external validation of a tool based on deep learning for the categorization of mammographic breast density is proposed. The evaluation of this tool will be carried out in two external institutions: Hospital Alemán and Fundación Científica del Sur.

Study Type

Observational

Enrollment (Anticipated)

277

Contacts and Locations

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

Study Contact

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

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

The unit of analysis will be the bilateral mammographic images with mediolateral oblique and craniocaudal views. The images selected for this study will be screening mammograms performed at Saint John's Cancer Institute. The institution will select the images according to the inclusion and exclusion criteria. The images will be extracted from the institutional database retrospectively and will be anonymized without any personal data, except for the age of the patient. These images will be stored in DICOM format, in a safe place with restricted access limited only to the investigation team.

Description

Inclusion Criteria:

  • Mammograms included in the study should meet the following criteria:

    • Female patients of 40 years of age or more.
    • To have at least one screening mammography exam performed at Saint John's
    • Cancer Institute during the study period. These exams will be included regardless of the brand of the mammography equipment.
    • Mammograms should be performed with digital equipment.

Exclusion Criteria:

  • Mammograms with the following criteria will be excluded from the study:

    • Patients with gigantomastia, defined by the need for more than one image of each mammographic view (mediolateral oblique and craniocaudal) to evaluate the entire breast volume.
    • Patients with breast implants.
    • Patients with a history of breast surgery.

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Agreement between the majority report and Artemisia´s categorization of dense breasts/non-dense breasts
Time Frame: 2 months
The agreement between the CNN and the total of the professionals' categorizations will be calculated with the linear weighted kappa. To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by Artemisia for the same set of images.
2 months
Agreement between the majority report and Artemisia in each one of the four breast density categories
Time Frame: 2 months
For each one of the professionals involved in the study, the agreement with the CNN will be calculated with the linear weighted kappa coefficient. To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by Artemisia for the same set of images.
2 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Agreement between each observer and Artemisia´s categorization of dense breasts/non-dense breasts
Time Frame: 2 months
To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by Artemisia for the same set of images.
2 months
Agreement between each observer and Artemisia in each one of the four breast density categories
Time Frame: 2 months
For each one of the professionals involved in the study, the agreement with the CNN will be calculated with the linear weighted kappa coefficient. To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by Artemisia for the same set of images.
2 months
Agreement between each observer and the majority report in the categorization of dense breasts/non-dense breasts
Time Frame: 2 months
For each one of the professionals involved in the study, the agreement with the majority report will be calculated with the linear weighted kappa coefficient. To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by the majority report for the same set of images.
2 months
Agreement between each observer and the majority report in each one of the four breast density categories
Time Frame: 2 months
For each one of the professionals involved in the study, the agreement with the majority report will be calculated with the linear weighted kappa coefficient. To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by the majority report for the same set of images.
2 months

Collaborators and Investigators

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

Sponsor

Hospital Italiano de Buenos Aires

Investigators

  • Principal Investigator: Daniel R Luna, MD, Hospital Italiano de Buenos Aires

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

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)

September 1, 2021

Primary Completion (Anticipated)

April 1, 2022

Study Completion (Anticipated)

July 1, 2022

Study Registration Dates

First Submitted

August 19, 2021

First Submitted That Met QC Criteria

August 19, 2021

First Posted (Actual)

August 25, 2021

Study Record Updates

Last Update Posted (Actual)

August 25, 2021

Last Update Submitted That Met QC Criteria

August 19, 2021

Last Verified

July 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 6077
  • 4927 (PRIISA)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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