ArticleSurgery open science2025
Comparison of machine learning and Cox regression models for prognostic analysis in hepatocellular carcinoma patients with distant metastasis.
Article in Surgery open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Machine Learning-Derived Risk Groups and Clinical Implementation of Survival Prediction in Lung Cancer: Evidence from a Kazakh National Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Explainable machine-learning prediction of overall and cancer-specific survival in adult triple-negative breast cancer using SEER: a comparative study of nomograms and random survival forests.Translational cancer research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: With the development of conversion therapy, there has been a significant improvement in advanced stage hepatocellular carcinoma (HCC) patients' survival outcomes. Accurate prognostic assessment of patients with distant metastasis (DM) is therefore pivotal in improving quality of life, guiding treatment, and optimizing patient management. Methods: This study extracted patients with distant metastatic HCC from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox regression were used to identify prognostic factors. Then, Cox regression, DeepSurv, Decision Tree, and Random Survival Forests models were used to predict overall survival. Model performance was evaluated by area under the curve (AUC), decision curve analysis, calibration curve, and Brier score. The visualization of Cox regression and machine learning algorithms utilized nomogram and Shapley additive explanations, respectively. Results: The study included 3051 HCC patients with DM. Factors such as tumor size, lung metastasis, N stage, ace, chemotherapy, radiotherapy, AFP, fibrosis, treatment interval, and number of metastases were independently associated with patient prognosis. Among all models, Cox regression and Random Survival Forest models showed stable performance, achieving AUCs of 0.746/0.760, 0.745/0.749, and 0.729/0.718 at 3, 6, and 12 months, respectively. Meanwhile, Cox regression showed the lowest Brier score (0.180 and 0.125) at 6 and 12 months. Conclusions: Cox regression and Random Survival Forest models demonstrated robust prognostic performance for HCC, with Cox regression exhibiting superior temporal stability. The Cox-based nomogram provides an intuitive tool for rapid 3-, 6-, and 12-month survival stratification in metastatic HCC patients.
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.