ArticleFrontiers in oncology2025
Construction of a Bayesian network-based risk prediction model for hepatocellular carcinoma in cirrhotic patients.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To investigate clinical data of hospitalised cirrhosis patients, identify risk factors for cirrhosis progression to hepatocellular carcinoma, establish a risk prediction model, and provide scientific basis for early identification of high-risk patients. Methods: Hospitalised cirrhosis patients treated at Xinjiang Uygur Autonomous Region People's Hospital between January 2019 and December 2023 were selected. Their inpatient examination records were retrieved, including medical record summaries alongside results for coagulation function, complete blood count, liver function tests, urinalysis, renal function tests, tumour markers, comprehensive hepatitis panel, comprehensive thyroid function panel, comprehensive biochemical panel, glucose series, and lipid series. Patients diagnosed with hepatic malignancy during subsequent hospitalisations (excluding the initial admission) formed the cancer progression group, while those without hepatic malignancy constituted the control group. Univariate and multivariate analyses identified risk factors for hepatocellular carcinoma (HCC) progression in cirrhosis patients. A predictive model for HCC development in cirrhosis patients was constructed using a combined Lasso regression model and Bayesian network model. Results: This study enrolled 1,204 individuals, including 1,128 cirrhosis patients, of whom 76 progressed to liver malignancy. Multivariate logistic regression analysis indicated that female gender was a protective factor against cirrhosis progression to liver malignancy( Conclusions: Gender, hepatitis B, TC, and AT3 constitute risk factors for hepatocellular carcinoma in cirrhotic patients; Gender, hepatitis type, DOI, FT4, AT3, SCC, CRP, MAO, and Ca are associated with the progression of liver cirrhosis to malignant liver tumours either directly or indirectly. The risk prediction model constructed by combining LASSO regression with Bayesian networks demonstrates good predictive value.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.