Assessment of the Breast Cosmesis Using Deep Neural Networks: an Exploratory Study (ABCD) (ABCD)

April 7, 2025 updated by: Dr. Tabassum Wadasadawala, Tata Memorial Centre
Surgery and radiotherapy in breast cancer patients can cause treatment changes and may affect the final breast appearance. In this study, we are trying to evaluate the post treatment breast photographs of the patients and subject these to Artificial Intelligence based program so as to classify into appropriate categories based upon changes from baseline. This automated solution will help in decreasing the time required to achieve this task by physicians in the clinic.

Study Overview

Status

Recruiting

Conditions

Detailed Description

A new algorithm was introduced which is based on deep neural network (DNN) which receives an image as input and returns the coordinates of the breast key points as output. These key points are then given to a shortest-path algorithm that models images as graphs to refine breast key point localization. The algorithm learns, directly from the image, to compute features and to use those features in the analysis of the aesthetic result. This comprises of two main modules: regression and refinement of heatmaps, and regression of key points. To perform the heatmap regression, the U-Net model is used.

The goal of the first module is to generate an intermediate representation consisting on a fuzzy localization for the key points that are to be detected.

The second module receives and refines this fuzzy localization, and through complex calculations, outputting the x and y coordinates of the keypoints, and the data generated from which can be used for disease / image classification.

Study Type

Observational

Enrollment (Estimated)

720

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 Locations

  • India
    • Maharashtra
      • Mumbai, Maharashtra, India, 400012
        • Recruiting
        • Tata Memorial Centre
        • Contact:
        • Contact:
          • Tabassum Wadasadawala, MD
        • Contact:
          • Sahil Sood, MD
        • Contact:
          • Amit Sethi, PhD
        • Contact:
          • Rajiv Sarin, MD
        • Contact:
          • Rima Pathak, MD
        • Contact:
          • Revathy Krishnamurthy, MD
        • Contact:
          • Vani Parmar, MD
        • Contact:
          • Pallavi Rane, M.Sc

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

19 years to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

This is a retrospective analysis of patient photographs that have been acquired after written informed consent as per ethical requirements. The patients accrued in the ongoing prospective study (CTRI/2020/01/022871) have been re-consented for the current study in order to subject their breast photographs for neural network analysis. No photographs are taken separately for the current study. Hence this is essentially a retrospective study of the breast photographs to predict cosmesis.

Description

Inclusion Criteria:

  • Confirmed diagnosis of primary breast cancer (invasive or in situ)
  • Patient undergone breast conservation / Whole breast reconstruction
  • Patient received breast RT
  • Already provided written informed consent on earlier projects
  • Patient provided photographs of both breasts
  • Non-metastatic disease or oligometastatic
  • Age > 18 years
  • Reconsent given

Exclusion Criteria:

  • Mastectomy without whole breast reconstruction
  • Bilateral breast cancer
  • Partial breast irradiation
  • Male patient
  • Limited life expectancy due to co-morbidity
  • Patients undergoing brachy boost

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
Proportion of patients with excellent/good cosmesis
Time Frame: 3 years
The patient photographs will be processed for artificial intelligence based analysis of prediction of breast cosmesis
3 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Kappa statistic between different deep neural networks
Time Frame: 3 years
Concordance of various deep neural networks in prediction of breast cosmesis
3 years

Collaborators and Investigators

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

Sponsor

Tata Memorial Centre

Investigators

  • Principal Investigator: Tabassum Wadasadwala, MD, Tata Memorial Centre

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 (Actual)

October 4, 2021

Primary Completion (Estimated)

December 1, 2025

Study Completion (Estimated)

September 1, 2026

Study Registration Dates

First Submitted

June 28, 2022

First Submitted That Met QC Criteria

July 4, 2022

First Posted (Actual)

July 8, 2022

Study Record Updates

Last Update Posted (Actual)

April 10, 2025

Last Update Submitted That Met QC Criteria

April 7, 2025

Last Verified

April 1, 2025

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 3734

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

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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