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

NCT05450016 | Recruiting

• 1 other identifier: 3734
• Study Type: observational
• Target: 720
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Breast Cancer

Keywords

Cosmesis, photographic assessment, neural network

Outcome Measures

Primary Outcomes (1)

• Proportion of patients with excellent/good cosmesis

  The patient photographs will be processed for artificial intelligence based analysis of prediction of breast cosmesis

  3 years

Secondary Outcomes (1)

• Kappa statistic between different deep neural networks

  3 years

Eligibility Criteria

• Age: 19 Years - 80 Years
• Gender Eligibility Details: Only female breast cancer patients will be studied
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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

You may not qualify if:

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

Sponsors & Collaborators

• Tata Memorial Centre: lead

Study Sites (1)

Tata Memorial Centre

Mumbai, Maharashtra, 400012, India

RECRUITING

Related Publications (21)

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Related Links

• YOLOv3: Real-Time Object Detection Algorithm (What's New?).
• R-CNN, Fast R-CNN, Faster R-CNN, YOLO - Object Detection Algorithms.

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Study Officials

• Tabassum Wadasadwala, MD

  Tata Memorial Centre

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Tabassum Wadasadawala, MD

CONTACT

9324445303; twadasadwala@actrec.gov.in

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor Tabassum Wadasadawala

Study Record Dates

• First Submitted: June 28, 2022
• First Posted: July 8, 2022
• Study Start: October 4, 2021
• Primary Completion: December 1, 2025
• Study Completion (Estimated): September 1, 2026
• Last Updated: April 10, 2025

Record last verified: 2025-04

Locations

IN (1)