Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0019204 (hepatocellular carcinoma)
 71,386 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The authors describe a case of synchronously occurring (double) tumours, i.e. primary hepatocellular carcinoma and aortic body chemodectoma in a 14-year-old mixed-breed male dog. The tumours were identified during necropsy, following euthanasia. In the last months of its life, the dog showed signs of weakness, anorexia, apathy, inactivity, and abdominal palpation elicited a painful reaction. The primary liver cancer emerged in the left lateral lobe without evidence of any distant metastases. Histopathological and immunohistochemical investigations revealed a well-differentiated, trabecular, claudin-7-, claudin-5- and pancytokeratin-negative hepatocellular carcinoma. The Ki-67 proliferation index was 33%. During necropsy, a synchronously occurring benign, grade I type aortic body chemodectoma was also detected in the dog. This neuroendocrine tumour showed chromogranin-, synaptophysin-, neuron-specific enolase- and S100 protein-positivity, and the Ki-67 proliferation index was 2%. The authors believe that this is the first description of synchronously occurring hepatocellular carcinoma and aortic body chemodectoma in a dog.
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PMID:A case of synchronous hepatocellular carcinoma and aortic body chemodectoma in a dog - pathological case report. 2135 46

We investigate the problem faced by a healthcare system wishing to allocate its constrained screening resources across a population at risk for developing a disease. A patient's risk of developing the disease depends on his/her biomedical dynamics. However, knowledge of these dynamics must be learned by the system over time. Three classes of reinforcement learning policies are designed to address this problem of simultaneously gathering and utilizing information across multiple patients. We investigate a case study based upon the screening for Hepatocellular Carcinoma (HCC), and optimize each of the three classes of policies using the indifference zone method. A simulation is built to gauge the performance of these policies, and their performance is compared to current practice. We then demonstrate how the benefits of learning-based screening policies differ across various levels of resource scarcity and provide metrics of policy performance.
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PMID:Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity. 2530 68