Research graph
References from Development and Validation of an Interpretable Machine Learning Model Based on Gd-EOB-DTPA-enhanced MRI for Evaluating Small HCC (≤2 cm): A Multicenter Cohort Study. Local targets link to admitted publications; unresolved targets remain external evidence.
Hepatocellular carcinoma
2021 · External reference
Global, regional and national burden of primary liver cancer by subtype
10.1016/j.ejca.2021.11.023 · 2022 · External reference
Liver resection for hepatocellular carcinoma with vascular invasion: current insights and future perspectives
10.1097/ot9.0000000000000085 · 2025 · External reference
Diagnostic performance of CT/MRI LI-RADS version 2018 major feature combinations: individual participant data meta-analysis
10.1148/radiol.243450 · 2025 · External reference
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 · External reference
Radiomics in liver diseases: current progress and future opportunities
2020 · External reference
Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies
10.3389/fonc.2024.1362737 · 2024 · External reference
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 · External reference
Radiomics-based distinction of small (</=2 cm) hepatocellular carcinoma and precancerous lesions based on unenhanced MRI
10.1016/j.crad.2024.01.019 · 2024 · External reference
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 · External reference
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 · External reference
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 · External reference
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 · External reference
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 · External reference
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 · External reference
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 · External reference
MRI-based radiomics nomogram for preoperatively differentiating intrahepatic mass-forming cholangiocarcinoma from resectable colorectal liver metastases
10.1016/j.acra.2023.04.030 · 2023 · External reference
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 · External reference
User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability
10.1016/j.neuroimage.2006.01.015 · 2006 · External reference
Computational radiomics system to decode the radiographic phenotype
10.1158/0008-5472.can-17-0339 · 2017 · External reference
The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping
10.1148/radiol.2020191145 · 2020 · External reference
Unresolved reference
1994 · External reference
A guide to machine learning for biologists
10.1038/s41580-021-00407-0 · 2022 · External reference
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 · External reference
Major and ancillary magnetic resonance features of LI-RADS to assess HCC: an overview and update
10.1186/s13027-017-0132-y · 2017 · External reference
LI-RADS major features: CT, MRI with extracellular agents, and MRI with hepatobiliary agents
10.1007/s00261-017-1291-4 · 2018 · External reference
Diagnosis of hepatocellular carcinoma using Gd-EOB-DTPA MR imaging
10.2463/mrms.rev.2021-0031 · 2022 · External reference
Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules
10.1007/s00330-019-06347-w · 2020 · External reference
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 · External reference
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 · External reference
Current status and analysis of machine learning in hepatocellular carcinoma
2023 · External reference
Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma
10.1111/apt.16563 · 2021 · External reference
Research progress of MRI-based radiomics in hepatocellular carcinoma
2025 · External reference
A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging
10.1016/j.ejrad.2023.111159 · 2023 · External reference
