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Parathyroid carcinoma (PC) is an extremely rare disease. Although it may occasionally occur in genetic syndromes, it is more often sporadic. It is usually associated with a consistent secretion of PTH, causing severe hypercalcemia and potentially all clinical conditions due to primary hyperparathyroidism. Management of PC can be challenging: some clinical, biochemical, and radiological features may be useful, but the final diagnosis of malignancy strictly relies on histological criteria. To date, radical surgery is the first-choice treatment and is the only effective therapy to control hypercalcemia and other clinical manifestations. On the other hand, chemo- or radiotherapy, local treatments, or novel drugs should be reserved for selected cases. We report an exceptionally unusual case of life-threatening PC, associated with several systemic manifestations: moderate pancreatitis, portal thrombosis, kidney stones, brown tumors, osteoporosis, hungry bone syndrome (HBS), chondrocalcinosis, neuropathy, and depression. The clinical case also represents an opportunity to provide a review of the recent literature, associated with a complete evaluation of the main diagnostic and therapeutic approaches.

Aim: The first prototype of the “Multidisciplinary Tumor Board Smart Virtual Assistant” is presented, aimed to (i) Automated classification of clinical stage starting from different free-text diagnostic reports; (ii) Resolution of inconsistencies by identifying controversial cases drawing the clinician’s attention to particular cases worthy for multi-disciplinary discussion; (iii) Support environment for education and knowledge transfer to junior staff; (iv) Integrated data-driven decision making and standardized language and interpretation. Patients and Method: Data from patients affected by Locally Advanced Cervical Cancer (LACC), FIGO stage IB2-IVa, treated between 2015 and 2018 were extracted. Magnetic Resonance (MR), Gynecologic examination under general anesthesia (EAU), and Positron Emission Tomography–Computed Tomography (PET-CT) performed at the time of diagnosis were the items from the Electronic Health Records (eHRs) considered for analysis. An automated extraction of eHR that capture the patient’s data before the diagnosis and then, through Natural Language Processing (NLP), analysis and categorization of all data to transform source information into structured data has been performed. Results: In the first round, the system has been used to retrieve all the eHR for the 96 patients with LACC. The system has been able to classify all patients belonging to the training set and - through the NLP procedures - the clinical features were analyzed and classified for each patient. A second important result was the setup of a predictive model to evaluate the patient’s staging (accuracy of 94%). Lastly, we created a user-oriented operational tool targeting the MTB who are confronted with the challenge of large volumes of patients to be diagnosed in the most accurate way. Conclusion: This is the first proof of concept concerning the possibility of creating a smart virtual assistant for the MTB. A significant benefit could come from the integration of these automate