A Pre-Operative Clinical Model To Predict Microvascular Invasion and Long-Term Outcome After Resection of Hepatocellular Cancer: The Australian Experience
Background Hepatocellular cancer (HCC) is a leading cause of mortality worldwide. Liver resection or transplantation offer the best chance of long-term survival. The aim of this study was to perform a survival and prognostic factor analysis on patients who underwent resection of HCC at two major ter...
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Veröffentlicht in: | European journal of surgical oncology 2016-10, Vol.42 (10), p.1576-1583 |
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Sprache: | eng |
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Zusammenfassung: | Background Hepatocellular cancer (HCC) is a leading cause of mortality worldwide. Liver resection or transplantation offer the best chance of long-term survival. The aim of this study was to perform a survival and prognostic factor analysis on patients who underwent resection of HCC at two major tertiary referral hospitals, and to investigate a pre-operative prediction model for microvascular invasion (MVI). Methods Clinico-pathological and survival data were collected from all patients who underwent liver resection for HCC at two tertiary referral centres (Royal North Shore/North Shore Private Hospitals and Westmead Hospital) from 1998 to 2012. An overall and disease-free survival analysis was performed and a predictive model for MVI identified. Results The total number of patients in this series was 125 and the 5-year overall and disease-free survival rates were 56% and 37%, respectively. MVI was the only factor to be independently associated with a poor prognosis on both overall and disease-free survival. Age ≥ 64 years, a serum alpha-fetoprotein (AFP) ≥ 400 ng/ml (x40 above normal) and tumor size > 50 mm were independently associated with MVI. An MVI prediction model using these three pre-operative factors provides a good assessment of the risk of MVI. Conclusion MVI in the resected specimen of patients with HCC is associated with a poor prognosis. A preoperative MVI prediction model offers a useful way to identify patients at risk of relapse. However, more precise predictive models using molecular and genetic variables are needed to improve selection of patients most suitable for radical surgical treatment. |
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ISSN: | 0748-7983 1532-2157 |
DOI: | 10.1016/j.ejso.2016.05.032 |