Predictors of Development of Hepatocellular Carcinoma in Non-Cirrhotic Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease/Metabolic Dysfunction-Associated Steatohepatitis - A Retrospective Analysis of the National Inpatient Sample Database
Bibliographic record
Abstract
Background: One-quarter of the world population is thought to have metabolic dysfunction-associated steatotic liver disease (MASLD). The incidence of metabolic dysfunction-associated steatohepatitis (MASH) and MASLD is rapidly increasing due to the ongoing global epidemic of type 2 diabetes mellitus and obesity. Hepatitis B and C have declined in incidence due to advances in prevention and treatment, yet the overall burden of hepatocellular carcinoma (HCC) continues to rise, largely driven by the growing prevalence of MASLD/MASH. MASLD/MASH is now the fastest-growing etiology of HCC in the USA, France and the UK, with an estimated annual incidence of HCC ranging from 0.5% to 2.6% in patients with MASH cirrhosis. The incidence of HCC among patients with non-cirrhotic MASLD/MASH is lower, approximately 0.1% to 1.3%. There are no screening guidelines currently for HCC in non-cirrhotic MASLD/MASH patients. Our study highlights the dire need to develop HCC predictive strategies and algorithm in this non-cirrhotic population and to move away from a cirrhotic-centered approach but rather use risk-based models. We have identified predictors of development of HCC in this patient population that can be used to develop risk-based HCC screening guidelines and models in a non-cirrhotic population with MASLD/MASH. Methods: -test and were used to establish association between two variables. The significant variables were included in the logistic regression model to identify independent association between variables. Results: From the NIS database, 1,326,230 non-cirrhotic MASLD/MASH patients were identified. The mean age was 53.75 years; 52% were female. Older age (P < 0.0001), female gender (adjusted odds ratio (AOR) = 1.303, P < 0.001), and Asian race (AOR = 1.135, P = 0.01) were associated with increased HCC risk. Anemia, leukopenia, hyponatremia, and hypoalbuminemia were independent predictors (all P < 0.001). Benign liver lesions such as focal nodular hyperplasia (AOR = 1.269) and hemangiomas (1.475), as well as infections like cholangitis (3.093) and liver abscess (2.073), were linked to higher risk. Autoimmune diseases, including rheumatoid arthritis (0.679) and systemic lupus erythematosus (SLE, 0.456), were associated with decreased HCC risk (P < 0.001). Conclusions: This study provides compelling evidence that HCC can develop in non-cirrhotic MASLD/MASH patients. These findings highlight an urgent need to shift from a cirrhosis-centric approach to a more comprehensive, risk-based HCC screening model - especially given that MASLD/MASH is now the most common and fastest-growing etiology of HCC around the globe. This study can potentially help develop those screening guidelines to prevent the development or early detection of HCC in non-cirrhotic MASLD/MASH patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".