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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Breast Cancer

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 19 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

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

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Cosmesis, photographic assessment, neural network