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

NCT ID: NCT05450016

Last Updated: 2025-04-10

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 720 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-10-04
• Study Completion Date: 2026-09-30

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.

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.

Conditions

Breast Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Eligibility Criteria

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

• Minimum Eligible Age: 19 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

Tata Memorial Centre

OTHER

Sponsor Role: lead

Responsible Party

Dr. Tabassum Wadasadawala

Professor Tabassum Wadasadawala

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Tabassum Wadasadwala, MD

Role: PRINCIPAL_INVESTIGATOR

Tata Memorial Centre

Locations

Tata Memorial Centre

Mumbai, Maharashtra, India

Site Status: RECRUITING

Countries

India

Central Contacts

Tabassum Wadasadawala, MD

Role: CONTACT

twadasadwala@actrec.gov.in

9324445303

Facility Contacts

Tabassum Wadasadawala, MD

Role: primary

twadasadawala@actrec.gov.in

9324445303

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Related Links

https://viso.ai/deep-learning/yolov3-overview/.

YOLOv3: Real-Time Object Detection Algorithm (What's New?).

https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e

R-CNN, Fast R-CNN, Faster R-CNN, YOLO - Object Detection Algorithms.

Other Identifiers

3734

Identifier Type: -

Identifier Source: org_study_id