ArticleCancer imaging : the official publication of the International Cancer Imaging Society2021
Applying arterial enhancement fraction (AEF) texture features to predict the tumor response in hepatocellular carcinoma (HCC) treated with Transarterial chemoembolization (TACE).
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Artificial Intelligence and Machine Learning Predicting Transarterial Chemoembolization Outcomes: A Systematic Review.Digestive diseases and sciences · 2025Pooled it
- Can multi-phase contrast-enhanced CT be used to differentiate between intra-abdominal and retroperitoneal fat-poor liposarcoma and leiomyosarcoma?BMC medical imaging · 2026Article
- Radiomic Analysis Applied to Pretreatment Gd-EOB-DTPA-enhanced MR Imaging Predicts Response to Selective Transcatheter Arterial Chemoembolization for Hepatocellular Carcinoma.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Evaluation of induction chemotherapy response using multiphasic dual-energy CT in nasopharyngeal carcinoma.European radiology · 2025Article
- Dual energy CT-derived quantitative parameters and hematological characteristics predict pathological complete response in neoadjuvant chemoradiotherapy esophageal squamous cell carcinoma patients.BMC gastroenterology · 2025Article
- Beyond Conventional CT: Role of Dual-Energy CT in Monitoring Response to Therapy in Abdominal Malignancies.Radiology. Imaging cancer · 2025Review
- A Machine Learning Model for Predicting Prognosis in HCC Patients With Diabetes After TACE.Journal of hepatocellular carcinoma · 2025Article
- A novel method for predicting hepatocellular carcinoma response to chemoembolization using an intraprocedural CT hepatic arteriography-based enhancement mapping: a proof-of-concept analysis.European radiology experimental · 2023Article
- Radiomics features of computed tomography and magnetic resonance imaging for predicting response to transarterial chemoembolization in hepatocellular carcinoma: a meta-analysis.Frontiers in oncology · 2023Review
- Arterial enhancement fraction in evaluating the therapeutic effect and survival for hepatocellular carcinoma patients treated with DEB-TACE.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
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6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundTo evaluate the application of Arterial Enhancement Fraction (AEF) texture features in predicting the tumor response in Hepatocellular Carcinoma (HCC) treated with Transarterial Chemoembolization (TACE) by means of texture analysis.
methodsHCC patients treated with TACE in Shengjing Hospital of China Medical University from June 2018 to December 2019 were retrospectively enrolled in this study. Pre-TACE Contrast Enhanced Computed Tomography (CECT) and imaging follow-up within 6 months were both acquired. The tumor responses were categorized according to the modified RECIST (mRECIST) criteria. Based on the CECT images, Region of Interest (ROI) of HCC lesion was drawn, the AEF calculation and texture analysis upon AEF values in the ROI were performed using CT-Kinetics (C.K., GE Healthcare, China). A total of 32 AEF texture features were extracted and compared between different tumor response groups. Multi-variate logistic regression was performed using certain AEF features to build the differential models to predict the tumor response. The Receiver Operator Characteristic (ROC) analysis was implemented to assess the discriminative performance of these models.
resultsForty-five patients were finally enrolled in the study. Eight AEF texture features showed significant distinction between Improved and Un-improved patients (p < 0.05). In multi-variate logistic regression, 9 AEF texture features were applied into modeling to predict "Improved" outcome, and 4 AEF texture features were applied into modeling to predict "Un-worsened" outcome. The Area Under Curve (AUC), diagnostic accuracy, sensitivity, and specificity of the two models were 0.941, 0.911, 1.000, 0.826, and 0.824, 0.711, 0.581, 1.000, respectively.
conclusionsCertain AEF heterogeneous features of HCC could possibly be utilized to predict the tumor response to TACE treatment.
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