ArticleJournal for immunotherapy of cancer2024
Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.
Article in Journal for immunotherapy of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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45 citing papers in PubMed.
- Transformer-Based Multitask Framework Integrating Habitat and Deep Learning for Predicting Early Disease Control and Survival in Immunotherapy-Treated Hepatocellular Carcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multimodal clinical-radiomic-deep learning model for preoperative classification of pediatric pineal region tumors.Neuroradiology · 2026Article
- Integrating B-mode ultrasound radiomics and clinical variables to predict 26-week progression-free survival in advanced hepatocellular carcinoma under targeted immunotherapy.Abdominal radiology (New York) · 2026Article
- Development and validation of an interpretable MRI-based multimodal fusion model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter study.Abdominal radiology (New York) · 2026Article
- Current status and challenges of immune checkpoint inhibitors in liver cancer: from monotherapy to combination strategies.Medical oncology (Northwood, London, England) · 2026Review
- Preoperative MRI Intratumoral Heterogeneity Radiomics for Microvascular Invasion and Recurrence-Free Survival in HCC.Liver international : official journal of the International Association for the Study of the Liver · 2026Observational
- A perioperative multi-modal fusion and deep learning-based prognostic system for upper tract urothelial carcinoma: a multi-institutional study.Insights into imaging · 2026Article
- Clustering of quantitative CT features identifies HCC subtypes with distinct prognosis and immune signatures.European radiology experimental · 2026Article
- 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
- Body composition radiomics combined with machine learning for early recurrence prediction in intrahepatic cholangiocarcinoma following curative surgery: A Multi-Center study.European journal of nuclear medicine and molecular imaging · 2026Article
- Dual-energy CT biomarkers for predicting the efficacy of TACE combined with lenvatinib and immune checkpoint inhibitors in unresectable HCC.European radiology experimental · 2026Article
- Noninvasive evaluation and clinical value prediction of tumor-infiltrating neutrophil-to-T-cell ratio in pancreatic ductal adenocarcinoma.NPJ digital medicine · 2026Article
- Prediction of the efficacy after the first transarterial chemoembolization in hepatocellular carcinoma using CT radiomics combined with inflammatory composite indicators.Frontiers in medicine · 2026Article
- Neoadjuvant therapy for hepatocellular carcinoma: emerging combinations and biomarker challenges.Frontiers in immunology · 2026Review
- A Multimodal Diagnostic Model for Hepatocellular Carcinoma Integrating Biomarkers Related to Programmed Cell Death and Radiomics Features.Current medicinal chemistry · 2026Article
- From anatomical to biological resectability: navigating adaptive resistance in hepatocellular carcinoma conversion.Frontiers in oncology · 2026Review
- Development of a PNI-Based Prognostic Model for Patients with Unresectable Hepatocellular Carcinoma Treated with Lenvatinib-Based Triple Therapy.Journal of hepatocellular carcinoma · 2026Article
- Association of week-2 Contrast-Enhanced Ultrasound Perfusion Changes with Response to Lenvatinib Plus PD-1 Inhibitors in Unresectable Hepatocellular Carcinoma: A Retrospective Exploratory Study.Journal of hepatocellular carcinoma · 2026Article
- Radiomics and deep learning for immune checkpoint inhibitor outcomes: imaging-derived immune phenotyping and clinical translation across solid tumors.Frontiers in immunology · 2026Review
- CT-based radiomics-clinical model for risk assessment of parenteral nutrition-associated hepatic steatosis in chronic intestinal failure and its metabolomic interpretation.Frontiers in nutrition · 2026Article
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15 authors.
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Abstract
backgroundLenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promising treatment effects in unresectable hepatocellular carcinoma (HCC). However, biomarkers for predicting the response to LPI therapy remain to be further explored. We aimed to develop a radiomics model to noninvasively predict the efficacy of LPI therapy.
methodsClinical data of patients with HCC receiving LPI therapy were collected in our institution. The clinical model was built with clinical information. Nine machine learning classifiers were tested and the multilayer perceptron classifier with optimal performance was used as the radiomics model. The clinical-radiomics model was constructed by integrating clinical and radiomics scores through logistic regression analysis.
results151 patients were enrolled in this study (2:1 randomization, 101 and 50 in the training and validation cohorts), of which three achieved complete response, 69 showed partial response, 46 showed stable disease, and 33 showed progressive disease. The objective response rate, disease control rate, and conversion resection rates were 47.7, 78.1 and 23.2%. 14 features were selected from the initially extracted 1223 for radiomics model construction. The area under the curves of the radiomics model (0.900 for training and 0.893 for validation) were comparable to that of the clinical-radiomics model (0.912 for training and 0.892 for validation), and both were superior to the clinical model (0.669 for training and 0.585 for validation). Meanwhile, the radiomics model can categorize participants into high-risk and low-risk groups for progression-free survival (PFS) and overall survival (OS) in the training (HR 1.913, 95% CI 1.121 to 3.265, p=0.016 for PFS; HR 4.252, 95% CI 2.051 to 8.816, p=0.001 for OS) and validation sets (HR 2.347, 95% CI 1.095 to 5.031, p=0.012 for PFS; HR 2.592, 95% CI 1.050 to 6.394, p=0.019 for OS).
conclusionThe promising machine learning radiomics model was developed and validated to predict the efficacy of LPI therapy for patients with HCC and perform risk stratification, with comparable performance to clinical-radiomics model.
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