Conditional Generative Adversarial Network for Predicting the Aesthetic Outcomes of Breast Cancer Treatment

The alterations to the visual appearance of patients' breasts that occur due to breast cancer locoregional treatment can impact the self-esteem and satisfaction of the patients, affecting quality-of-life after treatment. As such, it is imperative that the patients are adequately informed of the potential aesthetic outcomes of treatment, to facilitate the choice of treatment and promote realistic expectations. As breast asymmetries are among the most notable effects of treatment, we propose a conditional generative adversarial network for manipulating the breast shape in torso images, applying it to simulate how the breasts' shape may change through surgical interventions. Experiments on a private breast dataset suggest that the proposed model outperforms the state-of-the-art in the realistic reconstruction of the torso of the patient while effectively manipulating the breasts.Clinical relevance - The proposed model enables visualizing alterations to the shape of a patient's breasts caused by locoregional breast cancer treatment, aiding the patient in the choice of treatment plan when multiple options are available.