Breast cancer tumors have different morphological phenotypes and specific histopathological types with particular prognostic and clinical characteristics. The treatment of rare malignant lesions is frequently controversial due to the absence of trials to determine the optimal managements. This review describes the spectrum of rare breast tumors indicating the clinical, epidemiological and treatment characteristics.

The digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination with machine learning technologies, it should help doctors in therapeutic path and in minimally invasive intervention procedures. Confidentiality of medical records is a very delicate issue, therefore some anonymization process is mandatory in order to maintain patients privacy. Moreover, data availability is very limited in some health domains like lung cancer treatment. Hence, generation of synthetic data conformed to real data would solve this issue. In this paper, the use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients is introduced as a tool to solve this problem in the form of anonymized synthetic patients. Generated synthetic patients are validated using both statistical methods, as well as by oncologists using the indirect mortality rate obtained for patients in different stages.

The development of healthcare patient digital twins in combination with machine learning technologies helps doctors in therapeutic prescription and in minimally invasive intervention procedures. The confidentiality of medical records or limited data availability in many health domains are drawbacks that can be overcome with the generation of synthetic data conformed to real data. The use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients has been previously introduced as a tool to solve this problem in the form of anonymized synthetic patients. However, generated synthetic data are mainly validated from the machine learning domain (loss functions) or expert domain (oncologists). In this paper, we propose statistical decision making as a validation tool: Is the model good enough to be used? Does the model pass rigorous hypothesis testing criteria? We show for the case at hand how loss functions and hypothesis validation are not always well aligned.

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