ArticleScientific reports2024
CT radiomics-based biomarkers can predict response to immunotherapy in hepatocellular carcinoma.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- AI-Driven Radiomics Assisted Prognostic Modeling for Hepatocellular Carcinoma with Portal Vein Invasion: A Retrospective Study.Biomedicines · 2026Article
- Current status and challenges of immune checkpoint inhibitors in liver cancer: from monotherapy to combination strategies.Medical oncology (Northwood, London, England) · 2026Review
- Large language model and Gd-EOB-DTPA-enhanced MRI-based risk stratification system for postoperative hepatocellular carcinoma: a multicenter study.European radiology · 2026Article
- From Theory to Practice: Which Biomarkers Are Ready for Predicting Response in Advanced HCC?Liver international : official journal of the International Association for the Study of the Liver · 2026Review
- Immune infiltration differences underlying prognostic radiomics subtypes from matched MRI and RNA sequencing in patients with lower-grade gliomas.Discover oncology · 2026Article
- Decoding Treatment Response in Hepatocellular Carcinoma: The Era of Radiomics and Radiogenomics.Journal of hepatocellular carcinoma · 2026Review
- Medical imaging in immunotherapy response evaluation: from RECIST to AI-driven longitudinal models, and the distinction between risk prediction and kinetic diagnosis.Frontiers in medicine · 2026Review
- Overcoming immune resistance in hepatocellular carcinoma: insights into mechanisms, predictive factors, and interventional strategies.Frontiers in immunology · 2026Review
- Evaluation and prediction of Ki-67 expression in hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- Noninvasive prediction of the clinical benefit of immunotherapy in hepatocellular carcinoma.Journal of gastroenterology · 2025Review
- Prediction of Immunotherapy Response in Hepatocellular Carcinoma Patients Using Pretreatment CT Images.Diagnostics (Basel, Switzerland) · 2025Article
- Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Integrating radiomics and machine learning for the diagnosis and prognosis of hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- Hepatocellular Carcinoma: A Comprehensive Review.Diseases (Basel, Switzerland) · 2025Review
- Article
- Integrating ultrasound and serum indicators for evaluating outcomes of targeted immunotherapy in advanced liver cancer.World journal of gastrointestinal oncology · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Leveraging artificial intelligence to validate traditional biomarkers and drug targets in liver cancer recovery: a mini review.Frontiers in pharmacology · 2025Review
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Authors and funding
7 authors.
Funding
Abstract
Hepatocellular Carcinoma (HCC) remains a leading cause of cancer deaths. Despite the rise of immunotherapies, many HCC patients don't benefit. There's a clear need for biomarkers to guide treatment decisions. This research aims to identify such biomarkers by combining radiological data and machine learning. We analyzed clinical and CT imaging data of 54 HCC patients undergoing immunotherapy. Radiologic features were examined to develop a model predicting short-term immunotherapy effects. We utilized 9 machine learning and 2 ensemble learning techniques using RapidMiner for model construction. We conducted the validation of the above feature combination using 29 HCC patients who received immunotherapy from another hospital, and tested and validated it using XGBoost combined with a genetic algorithm. In 54 HCC patients, radiomics features varied significantly between those with partial response (PR) and stable disease (SD). Key features in Gray Level Run Length Matrix (GLRLM) and in adjacent tissues' Intensity Direct, Neighborhood Gray Tone Difference Matrix (NGTDM), and Shape correlated with short-term immunotherapy efficacy. Selected feature combinations of 15, 19, and 8/15 features yielded better outcomes. Deep learning, random forest, and naive bayes outperformed other methods, with Bagging being more reliable than Adaboost. In the validation set of 29 HCC patients, the mentioned feature combination also demonstrated favorable performance. Furthermore, we achieved improved results when employing XGBoost in conjunction with a genetic algorithm for testing and validation. The machine learning model built with CT image features derived from GLCM, GLRLM, IntensityDirect, NGTDM, and Shape can accurately forecast the short-term efficacy of immunotherapy for HCC.
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