Product Development
Future treatments and products.
Future treatments and products.
HCC high-risk individuals
High-risk individuals for developing HCC (1, 2, 3)
Individuals eligible for screening programs (3, 4, 5)
EASL–EORTC screening program (4, 6)
1. WHO guidelines. Fact sheet – Updated July, 2017.
2. Expert Point of View Articles Collection. Global Burden Of Liver Disease: A True Burden on Health Sciences and Economies – ASSESSED ON AUGUST 18, 2017.
3. EASL–EORTC Clinical Practice Guidelines. Management of hepatocellular carcinoma; European Association for the Study of the Liver; European Organisation for Research and Treatment of Cancer ; Journal of Hepatology 2012 vol. 56 j 908–943.
4. Bisceglie A et al. Issues in screening and surveillance for hepatocellular carcinoma. Gastroenterology (2004) 127:S104-S107.
5. Bruix et al. Management of Hepatocellular Carcinoma. Hepatology (2005) 42:1208-1236.
6. El-Serag et al. Surveillance for hepatocellular carcinoma: in whom and how? Therap Adv Gastroenterol. (2011) 4:5-10.
Screening application
We believe the medical device we are developing may be used as a screening method to determine, through hemodynamic monitoring, which individuals are at high risk for developing HCC.
This Autem non-invasive screening method could be applied by health professionals with relatively low-levels of specialization, allowing for the implementation of widespread screening campaigns. Results would be available within minutes, contributing to easier patient management.
Autem Medical studies showed high accuracy in identifying the correct diagnosis among 120 patients with early and advanced stage HCC and healthy controls.
Prediction application
We believe the device we are developing may be used as a prognostic and predictive solution method, through hemodynamic monitoring, designed to estimate the expected survival after each exposure procedure in patients having a risk for hospital admissions.
The prognostic model based on artificial intelligence and machine learning showed a high level of accuracy, considering data from the first exposure from 50 patients with advanced HCC.
The prognostic model is designed to provide information about the risk of death in the first year of follow-up, counting from the first day of exposure. In a retrospective analysis, we identified two distinct groups of patients with advanced HCC having different survival rates (p=2,84e-05).
The prediction model based on artificial intelligence and machine learning showed a high level of accuracy using preliminary data, considering all exposures from selected groups of patients.
The prediction model is designed to provide information about the risk of death and risk of hospitalization after fixed-time thresholds counting from the day of exposure.
Using these thresholds, we obtained 98.8% accuracy in assessment of risk of hospitalization and 91.2% accuracy in assessment of risk of death in the population of patients with HCC, and 100.0% accuracy in assessment of risk of hospitalization and 100.0% accuracy in assessment of risk of death in healthy controls.
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