ArticleFrontiers in pharmacology2025
Performance of AI-based machine learning models for overall survival prediction in advanced hepatocellular carcinoma patients receiving immunoradiotherapy.
Article in Frontiers in pharmacology, 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide. Although immunotherapy and targeted therapy have improved survival in advanced HCC, outcomes remain heterogeneous. Radiotherapy (RT) may enhance systemic treatment efficacy through local control and immunomodulation. Artificial intelligence (AI) offers opportunities to integrate multimodal data for individualized prognostic assessment. Methods: A total of 175 HCC patients were included in this study: 115 in the RT group (RT + immunotherapy + targeted therapy) and 60 in the non-RT group (immunotherapy + targeted therapy). Baseline characteristics were analyzed with chi-square and Mann-Whitney U tests. Overall survival (OS) was compared using the Kaplan-Meier method and log-rank test. Patients were randomly divided into a training cohort and a validation cohort (6:4 ratio). Prognostic factors were identified in the training cohort and incorporated into 101 machine learning (ML) algorithms. Model performance was assessed using the concordance index (C-index), receiver operating characteristic (ROC) curves, and risk score stratification. Results: The RT group achieved significantly longer OS than the non-RT group (median OS: 15.4 vs. 8.5 months, Conclusion: RT significantly improved prognosis in advanced HCC patients treated with immunotherapy and targeted therapy. Among multiple algorithms, the StepCox (forward) + Ridge model achieved superior predictive performance, supporting its potential value in individualized prognostic assessment.
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.