ArticleInternational journal of general medicine2026
Prognostic Models for Small Hepatocellular Carcinoma Using Inflammatory Indices and Machine Learning: A Propensity Score-Matched Study.
Article in International journal of general medicine, 2026. 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The prognosis for patients with small hepatocellular carcinoma (HCC) after curative resection is variable. Early recurrence (within 2 years) remains a significant clinical challenge, closely associated with poor long-term outcomes. Although inflammatory biomarkers have shown prognostic value, integrated models for predicting both early recurrence and long-term survival are lacking. This study developed and validated prognostic models for overall survival (OS) and recurrence-free survival (RFS) in HCC patients post-resection, employing propensity score matching (PSM) to control for confounders between small (≤3 cm) and non-small HCC, with an emphasis on assessing model performance within the small HCC subgroup. Methods: A retrospective analysis of HCC patients who underwent hepatectomy was performed. PSM (1:1 nearest-neighbor with replacement) was applied to balance baseline characteristics between small and non-small HCC groups. After matching, a balanced cohort was used for survival analysis. Independent prognostic factors were identified through Cox regression. A traditional nomogram and risk score models were developed and compared against three machine learning models (LASSO-Cox, Random Forest, XGBoost) using time-dependent AUC. Results: Following PSM, 165 patients (90 with small HCC and 75 with non-small HCC) were included. Small HCC patients demonstrated significantly better OS and RFS (both p < 0.01). Multivariate analysis identified tumor size, SII, and AAR as independent predictors for OS, and tumor size, PAR, NLR, and GPR for RFS. The LASSO-Cox model exhibited the best overall performance, achieving the highest accuracy for early recurrence (2-year RFS AUC = 0.727) and competitive accuracy for long-term survival (5-year OS AUC = 0.698). The nomogram retained good interpretability (5-year OS AUC = 0.691). Conclusion: This study establishes a comprehensive prognostic framework for small HCC, integrating tumor size with systemic inflammation (SII, NLR), liver function (AAR, GPR), and nutritional-coagulation status (PAR). The LASSO-Cox model is recommended for predicting both early recurrence and long-term survival, offering refined risk stratification to guide postoperative management.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.