ArticleFrontiers in oncology2022
Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Artificial intelligence and personalised medicine in liver cancer.Journal of hepatology · 2026Review
- Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Imaging-Based Artificial Intelligence in Vascular and Interventional Radiology: A Narrative Review.Journal of imaging informatics in medicine · 2026Review
- An External Validation Study on Two Pre-Trained Large Language Models for Multimodal Prognostication in Laryngeal and Hypopharyngeal Cancer: Integrating Clinical, Treatment, and Radiomic Data to Predict Survival Outcomes with Interpretable Reasoning.Bioengineering (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
- Prognostic and predictive imaging markers of hepatocellular carcinoma: a pictorial essay.Insights into imaging · 2025Article
- A novel radiomics model combining GTVp, GTVnd, and clinical data for chemoradiotherapy response prediction in patients with advanced NSCLC.Frontiers in medicine · 2025Article
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
- Preclinical Implementation of matRadiomics: A Case Study for Early Malformation Prediction in Zebrafish Model.Journal of imaging · 2024Article
- Deep learning to estimate response of concurrent chemoradiotherapy in non-small-cell lung carcinoma.Journal of translational medicine · 2024Article
- Future AI Will Most Likely Predict Antibody-Drug Conjugate Response in Oncology: A Review and Expert Opinion.Cancers · 2024Review
- Prognostication of Hepatocellular Carcinoma Using Artificial Intelligence.Korean journal of radiology · 2024Review
- Deep Learning Based on Computed Tomography Predicts Response to Chemoimmunotherapy in Lung Squamous Cell Carcinoma.Aging and disease · 2024Article
- Feature Extraction of Ultrasound Radiofrequency Data for the Classification of the Peripheral Zone of Human Prostate.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Pretreatment CT-Based Machine Learning Radiomics Model Predicts Response in Inoperable Stage III NSCLC Treated with Concurrent Radiochemotherapy Plus PD-1 Inhibitors.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to develop and validate a pyradiomics model for preoperative prediction of initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). To this end, computed tomography (CT) images were acquired from multi-centers. Numerous pyradiomics features were extracted and machine learning approach was used to build a model for predicting initial response of TACE treatment. The predictive accuracy, overall survival (OS), and progression-free survival (PFS) were analyzed. Gene Set Enrichment Analysis (GSEA) was further used to explore signaling pathways in The Cancer Genome Atlas (TCGA)-HCC cohort. Overall, 24 of the 1,209 pyradiomic features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. The pyradiomics signature showed high predictive accuracy across the discovery set (AUC: 0.917, 95% confidence interval [CI]: 86.93-96.39), validation set 1 (AUC: 0.902, 95% CI: 84.81-95.59), and validation set 2 (AUC: 0.911; 95% CI: 83.26-98.98). Based on the classification of pyradiomics model, we found that a group with high values base on pyramidomics score showed good PFS and OS (both
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