ArticleFrontiers in immunology2020
Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients.
Article in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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56 citing papers in PubMed, 80 citations in OpenAlex.
- A multimodal deep learning model predicting hyperprogressive disease for PD-1 blockade in advanced hepatocellular carcinoma.NPJ digital medicine · 2026Article
- Clustering of quantitative CT features identifies HCC subtypes with distinct prognosis and immune signatures.European radiology experimental · 2026Article
- Automating classification of treatment responses to combined targeted therapy and immunotherapy in HCC.Journal of molecular cell biology · 2026Article
- Review
- The capability of deep-radiomics to predict pathological response to neoadjuvant immunochemotherapy in non-small cell lung cancer: a retrospective multicenter study.Frontiers in immunology · 2026Article
- Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma.Abdominal radiology (New York) · 2025Review
- Hepatocellular Carcinoma: A Comprehensive Review.Diseases (Basel, Switzerland) · 2025Review
- The delaying effect of toripalimab on disease progression in patients with advanced hepatocellular carcinoma and changes in serum tumor markers.World journal of surgical oncology · 2025Article
- CT Imaging-based radiomics predicts the pain relief of Strontium-89 in treating tumor-induced bone metastases.Journal of applied clinical medical physics · 2025Article
- A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma.NPJ precision oncology · 2025Article
- Cohort-based nomogram for forensic prediction of SCD: a single-center pilot study.Forensic science, medicine, and pathology · 2025Article
- A novel hybrid model for predicting tertiary lymphoid structures and targeted immunotherapy outcomes in hepatocellular carcinoma: a multicenter retrospective study.European radiology · 2025Article
- Pretreatment radiomic imaging features combined with immunological indicators to predict targeted combination immunotherapy response in advanced hepatocellular carcinoma.World journal of clinical oncology · 2025Article
- The evolving landscape of biomarkers for systemic therapy in advanced hepatocellular carcinoma.Biomarker research · 2025Review
- Article
- Alpha-fetoprotein combined with initial tumor shape irregularity in predicting the survival of patients with advanced hepatocellular carcinoma treated with immune-checkpoint inhibitors: a retrospective multi-center cohort study.Journal of gastroenterology · 2025Article
- A nomogram based on quantitative MR signal intensity predicts early response to combined systemic treatment in patients with hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Radiomics-based biomarker for PD-1 status and prognosis analysis in patients with HCC.Frontiers in immunology · 2025Article
- Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma.Computational and structural biotechnology journal · 2024Article
- Radiomic analysis based on magnetic resonance imaging for the prediction of VEGF expression in hepatocellular carcinoma patients.Abdominal radiology (New York) · 2024Article
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13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There is no study accessible now assessing the prognostic aspect of radiomics for anti-PD-1 therapy for patients with HCC. Aim: The aim of this study was to develop and validate a radiomics nomogram by incorporating the pretreatment contrast-enhanced Computed tomography (CT) images and clinical risk factors to estimate the anti-PD-1 treatment efficacy in Hepatocellular Carcinoma (HCC) patients. Methods: A total of 58 patients with advanced HCC who were refractory to the standard first-line of therapy, and received PD-1 inhibitor treatment with Toripalimab, Camrelizumab, or Sintilimab from 1st January 2019 to 31 July 2020 were enrolled and divided into two sets randomly: training set (n = 40) and validation set (n = 18). Radiomics features were extracted from non-enhanced and contrast-enhanced CT scans and selected by using the least absolute shrinkage and selection operator (LASSO) method. Finally, a radiomics nomogram was developed based on by univariate and multivariate logistic regression analysis. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility. Results: Eight radiomics features from the whole tumor and peritumoral regions were selected and comprised of the Fusion Radiomics score. Together with two clinical factors (tumor embolus and ALBI grade), a radiomics nomogram was developed with an area under the curve (AUC) of 0.894 (95% CI, 0.797-0.991) and 0.883 (95% CI, 0.716-0.998) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) confirmed that nomogram had good consistency and clinical usefulness. Conclusions: This study has developed and validated a radiomics nomogram by incorporating the pretreatment CECT images and clinical factors to predict the anti-PD-1 treatment efficacy in patients with advanced HCC.
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