Objective: Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies.
Materials And Methods: T1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using Z score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions.
Results: Intra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms.
Discussion: A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.
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http://dx.doi.org/10.1007/s10334-020-00892-y | DOI Listing |
J Mater Chem B
January 2025
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bezmialem Vakif University, 34093, Istanbul, Turkey.
Theranostic agents hold great promise for personalized medicine by combining diagnostic and therapeutic functions. Herein, two novel multifunctional theranostic glyconanoprobes targeting breast cancer were engineered for synergistic dual chemo-gene therapy and triple chemo-gene-photothermal therapy. Upconversion nanoparticles (UCNPs) were prepared and coated with a Dox-loaded glycopeptide polymer (P-Dox) to form UCNP@P-Dox for improving stability.
View Article and Find Full Text PDFCells
December 2024
Biotechnology Research and Innovation Council-National Institute of Biomedical Genomics, Kalyani 741251, West Bengal, India.
Breast cancer is a cancer with global prevalence and a surge in the number of cases with each passing year. With the advancement in science and technology, significant progress has been achieved in the prevention and treatment of breast cancer to make ends meet. The scientific intradisciplinary subject of "metabolomics" examines every metabolite found in a cell, tissue, system, or organism from different sources of samples.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Background: Huai-he Hospital of Henan University, Kaifeng, China. Electronic address:
Objective: To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors.
Methods: Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO).
J Magn Reson Imaging
January 2025
Advanced Diagnostic and Interventional Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. Computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines.
View Article and Find Full Text PDFKorean J Radiol
January 2025
Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2) and a conventional T2-w FSE Dixon sequence (T2) for breast magnetic resonance imaging (MRI).
Materials And Methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner. Both T2 and T2 sequences were acquired for each patient.
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