S1243 Epidemiology of Hepatocellular Carcinoma (HCC) in a Canadian University Centre
Bibliographic record
Abstract
Introduction: Nearly 85% of HCC cases are reported in Asia and Saharan Africa. This explains why data on risk factors often concerns Asian populations. In fact, hepatitis B and C are among the major risk factors leading to HCC in current literature. We believe our predominant Caucasian population might not be the same given the different incidence of Non-Alcoholic Steatohepatitis (NASH) or alcoholic cirrhosis compared to these countries. In this study, we evaluated the prevalence of risk factors of HCC in our Canadian population in order to prevent and identify the best treatments for our population. Since the cause leading to HCC might modify management in the future, it becomes interesting to describe the epidemiology of our population. Methods: We retrospectively reviewed 196 files of patients 18 years of age and older diagnosed with HCC by radiological or pathological criteria between 2010 and 2020 from two of our university databases (Registre Local du Cancer and Ned-Écho). The prevalence of cirrhosis, hepatitis B, hepatitis C, alcoholic cirrhosis and NASH were presented using proportion with the Wilson method using 95% confidence interval. Z tests were used to compare the prevalence of our population’s HCC risk factors with the literature values. Finally, a Cox model was used to assess the risks factors contributing most to mortality. Results: 178 patients were included in our study. 94.9% of our population was Caucasian. 83.1% were male.19.6% did not have an underlying cirrhosis. Only 59.6% had a CHILD A cirrhosis limiting accessibility to treatment. Furthermore, the prevalence of hepatitis B was 4.7% compared to 33% in current literature (p< 0.001), hepatitis C was 25.1% compared to 21% (p =0.183), alcoholism was 45.0% compared to 30% (p< 0.001), NASH was 37.8% compared to less than 16% (p=0.002). There was no statistical difference in mortality by cancer risk factor. Conclusion: New evidence suggest that HCC related to NASH may be a favourable prognostic factor in patients treated with lenvatinib. Hence, the choice of a tyrosine kinase treatment might be better for the management of a Caucasian population. Promoting good lifestyle habits might also reduce the incidence of HCC in our Canadian population given the high prevalence of HCC related to NASH and alcoholic cirrhosis. Finally, approximatively half of our population had a CHILD A cirrhosis which emphasizes how crucial it is to adequately screen for HCC before cirrhosis progression as this will have an effect on management and prognostic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".