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Chemoembolization, Therapeutic

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Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging.

European radiology
BACKGROUND: We attempted to train and validate a model of deep learning for the preoperative prediction of the response of patients with intermediate-stage hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE).

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.

European radiology
OBJECTIVES: We aimed to establish and validate an artificial intelligence-based radiomics strategy for predicting personalized responses of hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) session by quantitatively analy...

Supervised learning based on tumor imaging and biopsy transcriptomics predicts response of hepatocellular carcinoma to transarterial chemoembolization.

Cell reports. Medicine
Although transarterial chemoembolization (TACE) is the most widely used treatment for intermediate-stage, unresectable hepatocellular carcinoma (HCC), it is only effective in a subset of patients. In this study, we combine clinical, radiological, and...

Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach.

Journal of vascular and interventional radiology : JVIR
PURPOSE: To show that a deep learning (DL)-based, automated model for Lipiodol (Guerbet Pharmaceuticals, Paris, France) segmentation on cone-beam computed tomography (CT) after conventional transarterial chemoembolization performs closer to the "grou...

Real-time automatic prediction of treatment response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma using deep learning based on digital subtraction angiography videos.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients with the same tumor stage. Accurate prediction of TACE response rem...

A 3D Tumor-Mimicking In Vitro Drug Release Model of Locoregional Chemoembolization Using Deep Learning-Based Quantitative Analyses.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Primary liver cancer, with the predominant form as hepatocellular carcinoma (HCC), remains a worldwide health problem due to its aggressive and lethal nature. Transarterial chemoembolization, the first-line treatment option of unresectable HCC that e...

Robot-Assisted Transarterial Chemoembolization of Hepatocellular Carcinoma Using a Coaxial Microcatheter Driving Controller-Responder Robot System: Clinical Pilot Study.

Journal of vascular and interventional radiology : JVIR
PURPOSE: To evaluate the feasibility and safety of robot-assisted transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC) using a new coaxial microcatheter driving controller-responder robot (CRR) system.

Compressed sensing with deep learning reconstruction: Improving capability of gadolinium-EOB-enhanced 3D T1WI.

Magnetic resonance imaging
PURPOSE: The purpose of this study was to determine the utility of compressed sensing (CS) with deep learning reconstruction (DLR) for improving spatial resolution, image quality and focal liver lesion detection on high-resolution contrast-enhanced T...

Non-invasive assessment of response to transcatheter arterial chemoembolization for hepatocellular carcinoma with the deep neural networks-based radiomics nomogram.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Transcatheter arterial chemoembolization (TACE) is a mainstay treatment for intermediate and advanced hepatocellular carcinoma (HCC), with the potential to enhance patient survival. Preoperative prediction of postoperative response to TAC...