  Treatments for breast cancer have continued to evolve and improve in recent
years, resulting in a substantial increase in survival rates, with
approximately 80\% of patients having a 10-year survival period. Given the
serious impact that breast cancer treatments can have on a patient's body
image, consequently affecting her self-confidence and sexual and intimate
relationships, it is paramount to ensure that women receive the treatment that
optimizes both survival and aesthetic outcomes. Currently, there is no gold
standard for evaluating the aesthetic outcome of breast cancer treatment. In
addition, there is no standard way to show patients the potential outcome of
surgery. The presentation of similar cases from the past would be extremely
important to manage women's expectations of the possible outcome. In this work,
we propose a deep neural network to perform the aesthetic evaluation. As a
proof-of-concept, we focus on a binary aesthetic evaluation. Besides its use
for classification, this deep neural network can also be used to find the most
similar past cases by searching for nearest neighbours in the highly semantic
space before classification. We performed the experiments on a dataset
consisting of 143 photos of women after conservative treatment for breast
cancer. The results for accuracy and balanced accuracy showed the superior
performance of our proposed model compared to the state of the art in aesthetic
evaluation of breast cancer treatments. In addition, the model showed a good
ability to retrieve similar previous cases, with the retrieved cases having the
same or adjacent class (in the 4-class setting) and having similar types of
asymmetry. Finally, a qualitative interpretability assessment was also
performed to analyse the robustness and trustworthiness of the model.
