Abstract
No public abstract is available from the verified source records.
Longshan Yang, Zhelin Lv, Pengbo Wu, Haitao Wen, Gengyun Lin, Lina Huang, Shihan Chen, Jinyu Lin, Hongbo Liang, Weichao Hao, Haisu Tao
Abstract
Authors
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Hepatocellular carcinoma
2021
Global, regional and national burden of primary liver cancer by subtype
10.1016/j.ejca.2021.11.023 · 2022
Liver resection for hepatocellular carcinoma with vascular invasion: current insights and future perspectives
10.1097/ot9.0000000000000085 · 2025
Diagnostic performance of CT/MRI LI-RADS version 2018 major feature combinations: individual participant data meta-analysis
10.1148/radiol.243450 · 2025
Diagnosis of hepatic nodules 20 mm or smaller in cirrhosis: prospective validation of the noninvasive diagnostic criteria for hepatocellular carcinoma
10.1002/hep.21966 · 2008
Radiomics in liver diseases: current progress and future opportunities
2020
Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies
10.3389/fonc.2024.1362737 · 2024
An interpretable MRI-based radiomics model predicting the prognosis of high-intensity focused ultrasound ablation of uterine fibroids
10.1186/s13244-023-01445-2 · 2023
Radiomics-based distinction of small (</=2 cm) hepatocellular carcinoma and precancerous lesions based on unenhanced MRI
10.1016/j.crad.2024.01.019 · 2024
Institutions
No ROR-resolved institution is linked to this work yet.
Provenance
crossref
Confidence 100%
unpaywall
Confidence 95%
datacite
Confidence 0%
Multi-phase contrast-enhanced magnetic resonance image-based radiomics-combined machine learning reveals microscopic ultra-early hepatocellular carcinoma lesions
10.1007/s00259-022-05742-8 · 2022
Diagnostic accuracy of Gd-EOB-DTPA for detection hepatocellular carcinoma (HCC): a comparative study with dynamic contrast enhanced magnetic resonance imaging (MRI) and dynamic contrast enhanced computed tomography (CT)
10.12659/pjr.899239 · 2017
The diagnostic performance of gadoxetic acid disodium-enhanced magnetic resonance imaging and contrast-enhanced multi-detector computed tomography in detecting hepatocellular carcinoma: a meta-analysis of eight prospective studies
10.1007/s00330-019-06294-6 · 2019
Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study
10.1186/s12880-025-01646-9 · 2025
Fractal analysis based on Gd-EOB-DTPA-enhanced MRI for prediction of vessels that encapsulate tumor clusters in patients with hepatocellular carcinoma
10.1097/js9.0000000000002547 · 2025
Radiomic analysis based on Gd-EOB-DTPA enhanced MRI for the preoperative prediction of Ki-67 expression in hepatocellular carcinoma
10.1016/j.acra.2023.07.019 · 2024
Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection
10.3389/fonc.2024.1446386 · 2024
MRI-based radiomics nomogram for preoperatively differentiating intrahepatic mass-forming cholangiocarcinoma from resectable colorectal liver metastases
10.1016/j.acra.2023.04.030 · 2023
A machine-learning model based on dynamic contrast-enhanced MRI for preoperative differentiation between hepatocellular carcinoma and combined hepatocellular-cholangiocarcinoma
10.1016/j.crad.2024.02.001 · 2024
User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability
10.1016/j.neuroimage.2006.01.015 · 2006
Computational radiomics system to decode the radiographic phenotype
10.1158/0008-5472.can-17-0339 · 2017
The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping
10.1148/radiol.2020191145 · 2020
Unresolved referenced work
1994
A guide to machine learning for biologists
10.1038/s41580-021-00407-0 · 2022
Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study
10.1097/js9.0000000000000881 · 2024
Major and ancillary magnetic resonance features of LI-RADS to assess HCC: an overview and update
10.1186/s13027-017-0132-y · 2017
LI-RADS major features: CT, MRI with extracellular agents, and MRI with hepatobiliary agents
10.1007/s00261-017-1291-4 · 2018
Diagnosis of hepatocellular carcinoma using Gd-EOB-DTPA MR imaging
10.2463/mrms.rev.2021-0031 · 2022
Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules
10.1007/s00330-019-06347-w · 2020
Differentiation of small (</= 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm
10.1186/s12876-021-01710-y · 2021
Radiomics based on dynamic contrast-enhanced magnetic resonance imaging in preoperative differentiation of combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma: a multi-center study
10.2147/jhc.s406648 · 2023
Current status and analysis of machine learning in hepatocellular carcinoma
2023
Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma
10.1111/apt.16563 · 2021
Research progress of MRI-based radiomics in hepatocellular carcinoma
2025
A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging
10.1016/j.ejrad.2023.111159 · 2023
No additional external references are available.