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

Status

Indicates the current recruitment status or the expanded access status.
Not yet recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

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

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Anticipated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
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

Study Contact

The name and contact information for the person who can answer enrollment questions for a clinical study. Each location where the study is being conducted may also have a specific contact, who may be better able to answer those questions.

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

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

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

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
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

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
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

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Hospital Italiano de Buenos Aires

Investigators

Investigators

A researcher involved in a clinical study. Related terms include site principal investigator, site sub-investigator, study chair, study director, and study principal investigator.
  • 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)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
September 1, 2021

Primary Completion (Anticipated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
April 1, 2022

Study Completion (Anticipated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
July 1, 2022

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
August 19, 2021

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
August 19, 2021

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
August 25, 2021

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
August 25, 2021

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
August 19, 2021

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
July 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant 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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