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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: May 21, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Rad-EfficientNet: Improving Breast MRI Diagnosis Through Integration of Radiomics and Deep Learning.

Konstantinos Georgas, Ioannis A Vezakis, Ioannis Kakkos

    IEEE Journal of Biomedical and Health Informatics
    |March 17, 2025
    PubMed
    Summary

    This study introduces Rad-EfficientNet, a deep learning model that combines imaging and radiomic features for improved breast cancer diagnosis. The model achieved 82% accuracy in differentiating benign from malignant tumors using MRI scans.

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    Area of Science:

    • Oncology
    • Radiology
    • Artificial Intelligence

    Background:

    • Breast cancer is the leading cancer in women globally, necessitating improved non-invasive diagnostic methods for early detection.
    • Current diagnostic techniques require enhancement for accurate and timely identification of malignant breast tumors.
    • Multiparametric 3 Tesla (3T) breast magnetic resonance imaging (MRI) offers potential for detailed tumor characterization.

    Purpose of the Study:

    • To develop and evaluate Rad-EfficientNet, a novel convolutional neural network (CNN) integrating radiomic features for enhanced breast tumor classification.
    • To improve the accuracy of differentiating benign from malignant breast lesions using deep learning and radiomics.
    • To assess the efficacy of incorporating radiomic features directly into the CNN training pipeline.

    Main Methods:

    • A dataset of 104 breast MRI cases (45 benign, 59 malignant) was utilized.
    • Radiomic features were extracted from 3D tumor bounding boxes and reduced to 25 using Pearson's correlation coefficient and Variance Inflation Factor.
    • Rad-EfficientNet, based on EfficientNet, was developed with a radiomics fusion layer, incorporating feature reduction, concatenation, and dropout.

    Main Results:

    • Rad-EfficientNet achieved an 82% accuracy score in classifying breast tumors.
    • The proposed model outperformed conventional classifiers trained solely on radiomic features.
    • Hybrid models combining learned and radiomic features post-training were also outperformed by Rad-EfficientNet.

    Conclusions:

    • Integrating radiomic features directly into the CNN training pipeline enables the learning of complementary information.
    • Rad-EfficientNet demonstrates a promising approach to enhance deep learning diagnostic techniques for breast lesions.
    • This method offers a pathway to improve the accuracy and effectiveness of non-invasive breast cancer diagnosis using MRI.