Assessment of the Breast Cosmesis Using Deep Neural Networks: an Exploratory Study (ABCD)
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Breast Cancer. Basic parameters: 19 years — 80 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- India
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Primary outcome measures
- Proportion of patients with excellent/good cosmesis [Time frame: 3 years]
Secondary outcome measures (1)
- Kappa statistic between different deep neural networks [Time frame: 3 years]
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
India · 1 center
- Tata Memorial Centre — Mumbai
Publications
- Hill-Kayser CE, Vachani C, Hampshire MK, Di Lullo GA, Metz JM. Cosmetic outcomes and complications reported by patients having undergone breast-conserving treatment. Int J Radiat Oncol Biol Phys. 2012 Jul 1;83(3):839-44. doi: 10.1016/j.ijrobp.2011.08.013. Epub 2011 Dec 2. PMID 22137022
- Cardoso JS, Silva W, Cardoso MJ. Evolution, current challenges, and future possibilities in the objective assessment of aesthetic outcome of breast cancer locoregional treatment. Breast. 2020 Feb;49:123-130. doi: 10.1016/j.breast.2019.11.006. Epub 2019 Nov 21. PMID 31790958
- Vrieling C, Collette L, Bartelink E, Borger JH, Brenninkmeyer SJ, Horiot JC, Pierart M, Poortmans PM, Struikmans H, Van der Schueren E, Van Dongen JA, Van Limbergen E, Bartelink H. Validation of the methods of cosmetic assessment after breast-conserving therapy in the EORTC "boost versus no boost" trial. EORTC Radiotherapy and Breast Cancer Cooperative Groups. European Organization for Research an PMID 10524421
- Kim MS, Reece GP, Beahm EK, Miller MJ, Atkinson EN, Markey MK. Objective assessment of aesthetic outcomes of breast cancer treatment: measuring ptosis from clinical photographs. Comput Biol Med. 2007 Jan;37(1):49-59. doi: 10.1016/j.compbiomed.2005.10.007. Epub 2006 Jan 24. PMID 16438948
- Pezner RD, Patterson MP, Hill LR, Vora N, Desai KR, Archambeau JO, Lipsett JA. Breast retraction assessment: an objective evaluation of cosmetic results of patients treated conservatively for breast cancer. Int J Radiat Oncol Biol Phys. 1985 Mar;11(3):575-8. doi: 10.1016/0360-3016(85)90190-7. PMID 3972667
- Pezner RD, Lipsett JA, Vora NL, Desai KR. Limited usefulness of observer-based cosmesis scales employed to evaluate patients treated conservatively for breast cancer. Int J Radiat Oncol Biol Phys. 1985 Jun;11(6):1117-9. doi: 10.1016/0360-3016(85)90058-6. PMID 3997593
- Lowery JC, Wilkins EG, Kuzon WM, Davis JA. Evaluations of aesthetic results in breast reconstruction: an analysis of reliability. Ann Plast Surg. 1996 Jun;36(6):601-6; discussion 607. doi: 10.1097/00000637-199606000-00007. PMID 8792969
- Cohen M, Evanoff B, George LT, Brandt KE. A subjective rating scale for evaluating the appearance outcome of autologous breast reconstruction. Plast Reconstr Surg. 2005 Aug;116(2):440-9. doi: 10.1097/01.prs.0000173214.05854.e4. PMID 16079671
Identifiers
NCT: NCT05450016 · 3734