ArticleEuropean radiology experimental2017
Breast DCE-MRI: lesion classification using dynamic and morphological features by means of a multiple classifier system.
Article in European radiology experimental, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Early prediction of pathological response to neoadjuvant chemotherapy of breast tumors: a comparative study using amide proton transfer-weighted, diffusion weighted and dynamic contrast enhanced MRI.Frontiers in medicine · 2024Article
- Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance.Insights into imaging · 2023Article
- A Framework for Evaluating the Technical Performance of Multiparameter Quantitative Imaging Biomarkers (mp-QIBs).Academic radiology · 2023Review
- Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study.Frontiers in oncology · 2022Article
- Radiomics-Derived Data by Contrast Enhanced Magnetic Resonance in RAS Mutations Detection in Colorectal Liver Metastases.Cancers · 2021Article
- Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer.Frontiers in oncology · 2021Article
- Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images.PeerJ. Computer science · 2021Article
- Synthesizing the First Phase of Dynamic Sequences of Breast MRI for Enhanced Lesion Identification.Frontiers in oncology · 2021Article
- Article
- Textural radiomic features and time-intensity curve data analysis by dynamic contrast-enhanced MRI for early prediction of breast cancer therapy response: preliminary data.European radiology experimental · 2020Article
- Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps.La Radiologia medica · 2020Article
- Molecular Subtypes Recognition of Breast Cancer in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging Phenotypes from Radiomics Data.Computational and mathematical methods in medicine · 2019Article
- Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine.European radiology experimental · 2018Review
- An Automated System for Analysis and Classification of Kinetic Curve on Dynamic Contrast-enhanced Magnetic Resonance Imaging Breast Lesion.Journal of medical physicsArticle
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5 authors.
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Abstract
backgroundIn breast magnetic resonance imaging (MRI) analysis for lesion detection and classification, radiologists agree that both morphological and dynamic features are important to differentiate benign from malignant lesions. We propose a multiple classifier system (MCS) to classify breast lesions on dynamic contrast-enhanced MRI (DCE-MRI) combining morphological features and dynamic information.
methodsThe proposed MCS combines the results of two classifiers trained with dynamic and morphological features separately. Twenty-six malignant and 22 benign breast lesions, histologically proven, were analysed. The lesions were subdivided into two groups: training set (14 benign and 18 malignant) and testing set (8 benign and 8 malignant). Volumes of interest were extracted both manually and automatically. We initially considered a feature set including 54 morphological features and 98 dynamic features. These were reduced by means of a selection procedure to delete redundant parameters. The performance of each of the two classifiers and of the overall MCS was compared with pathological classification.
resultsWe obtained an accuracy of 91.7% on the testing set using automatic segmentation and combining the best classifier for morphological features (decision tree) and for dynamic information (Bayesian classifier). With implementation of the MCS, an increase in accuracy of 12.5% and of 31.3% was obtained compared with the accuracy of the Bayesian classifier tested with dynamic features and with that of the decision tree tested with morphological parameters, respectively.
conclusionsAn MCS can optimise the accuracy for breast lesion classification combining morphological features and dynamic information.
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