ArticleFrontiers in oncology2021
Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 3 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Performance of Artificial Intelligence Models in Predicting Responsiveness of Hepatocellular Carcinoma to Transarterial Chemoembolization (TACE): A Systematic Review and Meta-Analysis.Journal of the American College of Radiology : JACR · 2026Pooled it
- The use of machine learning in transarterial chemoembolisation/transarterial embolisation for patients with intermediate-stage hepatocellular carcinoma: a systematic review.La Radiologia medica · 2025Pooled it
- Radiomics as a tool for prognostic prediction in transarterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis.La Radiologia medica · 2024Pooled it
- MRI radiomics andBMC cancer · 2026Article
- An exploratory study on whole-lung radiomics features from computed tomography for prognostic prediction in non-small cell lung cancer with concurrent chemoradiotherapy.Translational cancer research · 2026Article
- Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.World journal of gastrointestinal oncology · 2026Article
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- Dual-energy CT biomarkers for predicting the efficacy of TACE combined with lenvatinib and immune checkpoint inhibitors in unresectable HCC.European radiology experimental · 2026Article
- Imaging-Based Artificial Intelligence in Vascular and Interventional Radiology: A Narrative Review.Journal of imaging informatics in medicine · 2026Review
- AI models based on gadoxetic acid-enhanced MRI to predict treatment response and prognosis after TACE in hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models.World journal of gastroenterology · 2025Review
- Comment on "CT-based intratumoral and peritumoral radiomics to predict the treatment response to hepatic arterial infusion chemotherapy plus lenvatinib and PD-1 in high-risk hepatocellular carcinoma cases: a multi-center study".Hepatology international · 2025Article
- Habitat Model Based on Lung CT for Predicting Brain Metastasis in Patients with Non-Small Cell Lung Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Quantitative CT Perfusion and Radiomics Reveal Complementary Markers of Treatment Response in HCC Patients Undergoing TACE.Diagnostics (Basel, Switzerland) · 2025Article
- Computed tomography-based quantification of intra-tumoral heterogeneity for predicting treatment response to concurrent chemoradiotherapy in non-small cell lung cancer: a multicenter retrospective study.Translational cancer research · 2025Article
- Development of a predictive model for distant metastasis in HCC patients post-TACE using clinical data, radiomics, and deep learning.Journal of cancer research and clinical oncology · 2025Article
- Review
- 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
- Predicting Treatment Response to Transcatheter Arterial Chemoembolization in Hepatocellular Carcinoma Patients using a Deep Learning-based Approach.Current medical imaging · 2025Article
- Identification and validation of immune-related and inflammation-related genes in endometriosis.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe aimed to develop radiology-based models for the preoperative prediction of the initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) since the integration of radiomics and deep learning (DL) has not been reported for TACE.
methodsThree hundred and ten intermediate-stage HCC patients who underwent TACE were recruited from three independent medical centers. Based on computed tomography (CT) images, recursive feature elimination (RFE) was used to select the most useful radiomics features. Five radiomics conventional machine learning (cML) models and a DL model were used for training and validation. Mutual correlations between each model were analyzed. The accuracies of integrating clinical variables, cML, and DL models were then evaluated.
resultsGood predictive accuracies were showed across the two cohorts in the five cML models, especially the random forest algorithm (AUC = 0.967 and 0.964, respectively). DL showed high accuracies in the training and validation cohorts (AUC = 0.981 and 0.972, respectively). Significant mutual correlations were revealed between tumor size and the five cML models and DL model (each
conclusionThe radiomics cML models and DL model showed notable accuracy for predicting the initial response to TACE treatment. Moreover, the integrated model could serve as a novel and accurate method for prediction in intermediate-stage HCC.
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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.