Post-heart transplantation lymphoproliferative diseases (PTLDs) and the diagnostic role of [18f] FDG-PET/CT.

BACKGROUND: The incidence of cancer is higher in transplant patients than in the normal population, mostly due to the assumption of immunosuppressants able to reduce the possibility of rejection. In addition, immunocompromised patients have a greater susceptibility to EBV, HPV and HIV, infectious agents that by themselves may favor the onset of malignancies. Post-transplant lymphoproliferative diseases (PLDs) are among the most frequent neoplasms in transplant patients which like other aggressive neoplasms may be identified by the [18f] fluoro-D-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT). METHODS: We evaluated the clinical use of FDG-PET/CT in detecting PTLDs and other neoplasms performed at the lowest clinical or laboratory suspicion of malignancy in 127 consecutive subjects who underwent heart transplantation. RESULTS: A SUV>4 more confirmed the suspect of malignancy and induced us to further investigations. Of the 127 transplant subjects who underwent FDG-PET/CT, 64 showed a SUV value >4. Of these 64, 8 had PTLDs, 49 other neoplasms (urinary tract tumors, thyroid cancer, HPV cancer related, Kaposi' sarcoma and EBV related head and neck neoplasms) and 7 patients with chronic non-neoplastic inflammatory diseases. CONCLUSIONS: In the present study, FDG-PET/CT examination was of great use for an early identification and for an early treatment of PTLDs and other neoplasms.

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies.

The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information. Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach. In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies.