ArticleAmerican journal of cancer research2022
Artificial intelligence based on serum biomarkers predicts the efficacy of lenvatinib for unresectable hepatocellular carcinoma.
Article in American journal of cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- Artificial intelligence-enabled liquid biopsy in cancer: a systematic review and meta- analysis of diagnostic performance and biological implications.Frontiers in oncology · 2026Pooled it
- Pooled it
- Predictive Value of On-Treatment Alpha-Fetoprotein Change for Lenvatinib Response in Patients with Hepatocellular Carcinoma: A Real-World Retrospective Study in Vietnam.Medicina (Kaunas, Lithuania) · 2026Article
- Artificial Intelligence Applications in the Diagnosis, Treatment, and Prognosis of Hepatocellular Carcinoma.Gut and liver · 2026Review
- Prognostic value of salt-inducible kinase 2 expression in advanced hepatocellular carcinoma treated with sorafenib or lenvatinib: a propensity score-matched cohort study.American journal of cancer research · 2026Article
- Pathological mechanisms and research progress of multilevel intervention therapy for coronary heart disease.Frontiers in immunology · 2026Review
- Review
- Fibroblast growth factor 19-fibroblast growth factor receptor 4 axis: From oncogenesis to targeted-immunotherapy in advanced hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Review
- Circulating Fibroblast Growth Factor 21 (FGF21) as a Prognostic and Diagnostic Biomarker in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- The role of artificial intelligence in the management of liver diseases.The Kaohsiung journal of medical sciences · 2024Review
- Review
- scRNA-Seq Analysis Revealed CAFs Regulating HCC Cells via PTN Signaling.Journal of hepatocellular carcinoma · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
26 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lenvatinib has been effective not only as a first-line but also as a later-line systemic therapy for unresectable hepatocellular carcinoma (uHCC) in real-world clinical practice. How to predict the efficacy of lenvatinib and guide appropriate therapy selection in patients with uHCC have become important issues. This study aimed to investigate the impact of serum biomarkers on the treatment outcomes of patients with uHCC treated with lenvatinib in a real-world setting using an artificial intelligence algorithm. We measured serum biomarkers, including alpha-fetoprotein (AFP), albumin-bilirubin (ALBI) grade, and circulating angiogenic factors (CAFs [i.e., vascular endothelial growth factor, angiopoietin-2, fibroblast growth factor-19 [FGF19], and FGF21]) and analyzed treatment outcomes, including objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) in patients with uHCC treated with lenvatinib. The results of this study demonstrated that an AFP reduction ≥ 40% from baseline within 8 weeks after lenvatinib induction was associated with a higher ORR. With baseline biomarkers using a decision tree-based model, we identified patients with high, intermediate, and low ORRs (84.6%, 21.7% and 0%, respectively; odds ratio, 53.04, P < 0.001, high versus intermediate/low groups). Based on the decision tree-based survival predictive model, baseline AFP was the most important factor for OS, followed by ALBI grade and FGF21.
Indexed as
Identifiers
36628276PMC9827079W4315619257What 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.