AIMC Topic: Contrast Media

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Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers.

BMC medical imaging Jul 1, 2025
OBJECTIVE: This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) imaging features and molecular subtypes of mass-type breast cancer.
Humans Machine Learning Mammography Radiographic Image Interpretation, Computer-Assisted Breast Neoplasms Middle Aged Triple Negative Breast Neoplasms Contrast Media Retrospective Studies Female Aged Adult
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Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks.

Scientific reports Jul 1, 2025
Oral cancer is a hazardous disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop the deep convolutional neural networks (CNN)-based multiclass classification and object detection models for distingui...
Sarcoma Humans Neural Networks, Computer Convolutional Neural Networks Contrast Media Mouth Neoplasms Tomography, X-Ray Computed Deep Learning
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Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients.

Scientific reports Jul 1, 2025
This study aimed to develop a machine learning model based on Magnetic Resonance Imaging (MRI) radiomics for predicting early recurrence after curative surgery in patients with hepatocellular carcinoma (HCC).A retrospective analysis was conducted on ...
Female Aged Adult Radiomics Humans Machine Learning Retrospective Studies Carcinoma, Hepatocellular Contrast Media Male Magnetic Resonance Imaging Middle Aged Neoplasm Recurrence, Local ROC Curve Hepatectomy Liver Neoplasms
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Deep learning reconstruction for T2-weighted and contrast-enhanced T1-weighted magnetic resonance enterography imaging in patients with Crohn's disease: Assessment of image quality and clinical utility.

Clinical imaging May 30, 2025
PURPOSE: To investigate the image quality of deep learning-reconstructed T2-weighted half-Fourier single-shot turbo spin echo (DL T2 HASTE) and contrast-enhanced T1-weighted volumetric interpolated breath-hold examination (DL T1 VIBE) of magnetic res...
Young Adult Crohn Disease Adult Deep Learning Image Interpretation, Computer-Assisted Male Adolescent Middle Aged Contrast Media Retrospective Studies Magnetic Resonance Imaging Humans Female Reproducibility of Results Aged
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Non-invasive prediction of DCE-MRI radiomics model on CCR5 in breast cancer based on a machine learning algorithm.

Cancer biomarkers : section A of Disease markers May 21, 2025
BackgroundNon-invasive methods with universal prognostic guidance for detecting breast cancer (BC) survival biomarkers need to be further explored.ObjectiveThis study aimed to investigate C-C motif chemokine receptor type 5 (CCR5) prognosis value in ...
Breast Neoplasms Prognosis Radiomics Receptors, CCR5 Biomarkers, Tumor Humans Support Vector Machine Middle Aged Female Dynamic Contrast Enhanced Magnetic Resonance Imaging Contrast Media Machine Learning Aged Algorithms Adult
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Improving lower-extremity artery depiction and diagnostic confidence using dual-energy technique and popliteal artery monitoring in dual-low dose CT angiography.

Scientific reports May 9, 2025
To assess the utility of dual-energy CT scanning (DECTs) with popliteal artery (PA) monitoring in dual low-dose (radiation and contrast) lower-extremity CT angiography (LE-CTA). 135 patients undergoing LE-CTA were prospectively included and divided i...
Contrast Media Aged, 80 and over Humans Lower Extremity Prospective Studies Adult Signal-To-Noise Ratio Peripheral Arterial Disease Female Male Computed Tomography Angiography Radiation Dosage Aged Middle Aged Popliteal Artery
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Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.

European journal of radiology Apr 29, 2025
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 R...
Adult Sensitivity and Specificity Radiomics Male Female Reproducibility of Results Aged Aged, 80 and over Kidney Neoplasms Carcinoma, Renal Cell Boosting Machine Learning Algorithms Middle Aged Contrast Media Neoplasm Grading Radiographic Image Interpretation, Computer-Assisted Humans Machine Learning Tomography, X-Ray Computed
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Gadoxetic acid-enhanced MRI for identifying cholangiocyte phenotype hepatocellular carcinoma by interpretable machine learning: individual application of SHAP.

BMC cancer Apr 28, 2025
PURPOSE: Cholangiocyte phenotype hepatocellular carcinoma (HCC) is highly invasive. This study aims to develop and validate an optimal machine learning model to predict cholangiocyte phenotype HCC based on T1 mapping gadoxetic acid-enhanced MRI and t...
Female Humans Aged Magnetic Resonance Imaging Machine Learning Middle Aged Adult Gadolinium DTPA Kaplan-Meier Estimate Phenotype Male Liver Neoplasms Prognosis Carcinoma, Hepatocellular Retrospective Studies Contrast Media
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Tissue Clutter Filtering Methods in Ultrasound Localization Microscopy Based on Complex-Valued Networks and Knowledge Distillation.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Apr 22, 2025
Ultrasound localization microscopy (ULM) is a blood flow imaging technique that utilizes micrometer-sized microbubbles (MBs) as contrast agents to achieve high-resolution microvessel reconstruction through precise localization and tracking of MBs. Th...
Microvessels Contrast Media Microscopy Microbubbles Phantoms, Imaging Animals Neural Networks, Computer Deep Learning Algorithms Ultrasonography Image Processing, Computer-Assisted
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Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Apr 22, 2025
Resolving arterial flows is essential for understanding cardiovascular pathologies, improving diagnosis, and monitoring patient condition. Ultrasound contrast imaging uses microbubbles to enhance the scattering of the blood pool, allowing for real-ti...
Animals Deep Learning Ultrasonography Signal Processing, Computer-Assisted Signal-To-Noise Ratio Microbubbles Image Processing, Computer-Assisted Contrast Media Phantoms, Imaging
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