AIMC Topic: Contrast Media
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MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach.
European radiology experimental
Aug 12, 2025
BACKGROUND: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and ...
Liver MRI proton density fat fraction inference from contrast enhanced CT images using deep learning: A proof-of-concept study.
PloS one
Aug 8, 2025
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease worldwide, affecting over 30% of the global general population. Its progressive nature and association with other chronic diseases make...
The development of a multimodal prediction model based on CT and MRI for the prognosis of pancreatic cancer.
BMC gastroenterology
Aug 6, 2025
PURPOSE: To develop and validate a hybrid radiomics model to predict the overall survival in pancreatic cancer patients and identify risk factors that affect patient prognosis.
Refined prognostication of pathological complete response in breast cancer using radiomic features and optimized InceptionV3 with DCE-MRI.
Scientific reports
Jul 30, 2025
BACKGROUND: Neoadjuvant therapy plays a pivotal role in breast cancer treatment, particularly for patients aiming to conserve their breast by reducing tumor size pre-surgery. The ultimate goal of this treatment is achieving a pathologic complete resp...
Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-Net and Swin UNETR.
Scientific reports
Jul 16, 2025
Accurate segmentation of the liver parenchyma, portal veins, hepatic veins, and lesions from MRI is important for hepatic disease monitoring and treatment. Multi-phase contrast enhanced imaging is superior in distinguishing hepatic structures compare...
Non-invasive liver fibrosis screening on CT images using radiomics.
BMC medical imaging
Jul 15, 2025
PURPOSE: To develop a radiomics machine learning model for detecting liver fibrosis on CT images of the liver.
Preoperative prediction value of 2.5D deep learning model based on contrast-enhanced CT for lymphovascular invasion of gastric cancer.
Scientific reports
Jul 15, 2025
To develop and validate artificial intelligence models based on contrast-enhanced CT(CECT) images of venous phase using deep learning (DL) and Radiomics approaches to predict lymphovascular invasion in gastric cancer prior to surgery. We retrospectiv...
Recurrence prediction of invasive ductal carcinoma from preoperative contrast-enhanced computed tomography using deep convolutional neural network.
Biomedical physics & engineering express
Jul 10, 2025
Predicting the risk of breast cancer recurrence is crucial for guiding therapeutic strategies, including enhanced surveillance and the consideration of additional treatment after surgery. In this study, we developed a deep convolutional neural networ...
Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study.
Cancer imaging : the official publication of the International Cancer Imaging Society
Jul 7, 2025
OBJECTIVES: The purpose of this study is to mainly develop a predictive model based on clinicoradiological and radiomics features from preoperative gadobenate-enhanced (Gd-BOPTA) magnetic resonance imaging (MRI) using multilayer perceptron (MLP) deep...
Deep learning-based classification of parotid gland tumors: integrating dynamic contrast-enhanced MRI for enhanced diagnostic accuracy.
BMC medical imaging
Jul 4, 2025
BACKGROUND: To evaluate the performance of deep learning models in classifying parotid gland tumors using T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted MR images, along with DCE data derived from time-intensity curves.
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