AIMC Topic: Dynamic Contrast Enhanced Magnetic Resonance Imaging

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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...
Contrast Media Radiomics Magnetic Resonance Imaging Middle Aged Prognosis Neoadjuvant Therapy Humans Dynamic Contrast Enhanced Magnetic Resonance Imaging Female Breast Neoplasms
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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 Radiomics Biomarkers, Tumor Aged Adult Receptors, CCR5 Dynamic Contrast Enhanced Magnetic Resonance Imaging Middle Aged Support Vector Machine Contrast Media Humans Machine Learning Algorithms Prognosis Female
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Breast tumour classification in DCE-MRI via cross-attention and discriminant correlation analysis enhanced feature fusion.

Clinical radiology Apr 24, 2025
AIM: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has proven to be highly sensitive in diagnosing breast tumours, due to the kinetic and volumetric features inherent in it. To utilise the kinetics-related and volume-related informat...
Humans Image Interpretation, Computer-Assisted Dynamic Contrast Enhanced Magnetic Resonance Imaging Middle Aged Adult Diagnosis, Differential Contrast Media Sensitivity and Specificity Female Discriminant Analysis Breast Neoplasms Magnetic Resonance Imaging Aged Breast
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DCE-MRI based deep learning analysis of intratumoral subregion for predicting Ki-67 expression level in breast cancer.

Magnetic resonance imaging Mar 13, 2025
OBJECTIVE: To evaluate whether deep learning (DL) analysis of intratumor subregion based on dynamic contrast-enhanced MRI (DCE-MRI) can help predict Ki-67 expression level in breast cancer.
Aged Adult Breast Neoplasms Middle Aged Image Interpretation, Computer-Assisted Algorithms Deep Learning Ki-67 Antigen Retrospective Studies Contrast Media Dynamic Contrast Enhanced Magnetic Resonance Imaging Female Magnetic Resonance Imaging Humans Breast
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RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.

Biomedical physics & engineering express Feb 25, 2025
Accurate identification of molecular subtypes in breast cancer is critical for personalized treatment. This study introduces a novel neural network model, RAE-Net, based on Multimodal Feature Fusion (MFF) and the Evidential Deep Learning Algorithm (E...
Dynamic Contrast Enhanced Magnetic Resonance Imaging Reproducibility of Results Aged Adult Algorithms Breast Neoplasms Humans Image Processing, Computer-Assisted Female Middle Aged Contrast Media Neural Networks, Computer Deep Learning Magnetic Resonance Imaging
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Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI.

IEEE transactions on medical imaging Jan 2, 2025
Automated breast tumor segmentation on the basis of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) has shown great promise in clinical practice, particularly for identifying the presence of breast disease. However, accurate segment...
Magnetic Resonance Imaging Breast Image Interpretation, Computer-Assisted Dynamic Contrast Enhanced Magnetic Resonance Imaging Algorithms Female Humans Breast Neoplasms Neural Networks, Computer
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Optimizing Deep Learning Models for Luminal and Nonluminal Breast Cancer Classification Using Multidimensional ROI in DCE-MRI-A Multicenter Study.

Cancer medicine May 1, 2025
OBJECTIVES: Previous deep learning studies have not explored the synergistic effects of ROI dimensions (2D/2.5D/3D), peritumoral expansion levels (0-8 mm), and segmentation scenarios (ROI only vs. ROI original). Our study aims to evaluate the perform...
Dynamic Contrast Enhanced Magnetic Resonance Imaging ROC Curve Contrast Media Magnetic Resonance Imaging Middle Aged Humans Deep Learning Retrospective Studies Female Aged Adult Breast Neoplasms
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