ArticleLiver cancer2023
Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study.
Article in Liver cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 38 papers.
What it found
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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.
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Who cites it
38 citing papers in PubMed, 46 citations in OpenAlex.
- Response evaluation to systemic therapy in HCC: Current challenges and future perspectives.Hepatology (Baltimore, Md.) · 2026Review
- Deep learning-assisted tumor radiomic dynamics on MRI predict pathological complete response in HCC undergoing immune-based therapy followed by hepatectomy.Hepatology (Baltimore, Md.) · 2026Article
- Radiomics-Based Prediction of Treatment Response in Non-Small Cell Lung Cancer Using Pre-Treatment CT Imaging Features.Diagnostics (Basel, Switzerland) · 2026Article
- Chinese expert consensus on conversion and perioperative therapy of primary liver cancer (2024 edition).Hepatobiliary surgery and nutrition · 2026Review
- MRI-based clinical-radiologic model to predict the efficacy and prognosis of interventional with systemic therapy in advanced hepatocellular carcinoma: a multicenter study.Journal of translational medicine · 2026Article
- Automating classification of treatment responses to combined targeted therapy and immunotherapy in HCC.Journal of molecular cell biology · 2026Article
- Radiogenomics predicts immune microenvironment heterogeneity and response to combination immunotherapy in hepatocellular carcinoma.Journal of translational medicine · 2026Article
- Review
- Emerging Role of MRI-Based Artificial Intelligence in Individualized Treatment Strategies for Hepatocellular Carcinoma: A Narrative Review.Journal of magnetic resonance imaging : JMRI · 2026Review
- Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging: A Bibliometric and Visualized Analysis.Journal of hepatocellular carcinoma · 2026Article
- Advancements in intratumoral therapies for liver tumors.Frontiers in oncology · 2026Review
- Radiomics and deep learning for immune checkpoint inhibitor outcomes: imaging-derived immune phenotyping and clinical translation across solid tumors.Frontiers in immunology · 2026Review
- Interpretable model based on multisequence magnetic resonance imaging radiomics for predicting the pathological grades of hepatocellular carcinomas.World journal of radiology · 2025Article
- TUG1 targeting enhances anticancer immunity thereby facilitating lenvatinib efficacy in hepatocellular carcinoma.Genes and immunity · 2025Article
- Breaking through the transarterial chemoembolization resistance barrier: Reshaping the treatment path for advanced liver cancer with triple therapy.World journal of clinical oncology · 2025Article
- Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma.Abdominal radiology (New York) · 2025Review
- Review
- Decision-Making Biomarkers Guiding Therapeutic Strategies in Hepatocellular Carcinoma: From Prediction to Personalized Care.Cancers · 2025Review
- MRI-based radiomics signatures for predicting the efficacy of targeted therapy with lenvatinib in hepatocellular carcinoma: a retrospective cohort study.Journal of cancer research and clinical oncology · 2025Article
- Combination therapy reduces transarterial chemoembolization resistance in advanced hepatocellular carcinoma.World journal of clinical oncology · 2025Article
Corrections and comments
- Erratum issuedErratum.2025
Authors and funding
28 authors at 9 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients. Methods: Patients ( Results: The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% ( Conclusion: Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.
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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.