ArticleMagma (New York, N.Y.)2018
2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution.
Article in Magma (New York, N.Y.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 41 citations in OpenAlex.
- Multiparametric-MRI habitat radiomics analysis for discriminating pathological types of brain metastases.Frontiers in oncology · 2025Article
- Can radiomics in brain magnetic resonance imaging predict the mutational status of primary lung cancer based on brain metastasis?Polish journal of radiology · 2025Article
- Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities.Frontiers in neurology · 2025Review
- Radiomic signatures of brain metastases on MRI: utility in predicting pathological subtypes of lung cancer.Translational cancer research · 2024Article
- MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases.Frontiers in neurology · 2024Article
- An Effective Approach to Improve the Automatic Segmentation and Classification Accuracy of Brain Metastasis by Combining Multi-phase Delay Enhanced MR Images.Journal of digital imaging · 2023Article
- Association between breast cancer's prognostic factors and 3D textural features of non-contrast-enhancedThe British journal of radiology · 2022Article
- Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation.Korean journal of radiology · 2022Article
- A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor.Frontiers in neuroinformatics · 2022Article
- Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer.Frontiers in oncology · 2021Article
- Inflammatory lesions and brain tumors: is it possible to differentiate them based on texture features in magnetic resonance imaging?The journal of venomous animals and toxins including tropical diseases · 2020Article
- Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases.Magnetic resonance imaging · 2020Article
- Breast Cancer Brain Metastasis: The Potential Role of MRI Beyond Current Clinical Applications.Cancer management and research · 2020Review
- Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas.European radiology · 2019Article
- Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.European radiology · 2018Article
- Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma.Molecular imagingArticle
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo find structural differences between brain metastases of lung and breast cancer, computing their heterogeneity parameters by means of both 2D and 3D texture analysis (TA). MATERIALS AND
methodsPatients with 58 brain metastases from breast (26) and lung cancer (32) were examined by MR imaging. Brain lesions were manually delineated by 2D ROIs on the slices of contrast-enhanced T1-weighted (CET1) images, and local binary patterns (LBP) maps were created from each region. Histogram-based (minimum, maximum, mean, standard deviation, and variance), and co-occurrence matrix-based (contrast, correlation, energy, entropy, and homogeneity) 2D, weighted average of the 2D slices, and true 3D TA were obtained on the CET1 images and LBP maps.
resultsFor LBP maps and 2D TA contrast, correlation, energy, and homogeneity were identified as statistically different heterogeneity parameters (SDHPs) between lung and breast metastasis. The weighted 3D TA identified entropy as an additional SDHP. Only two texture indexes (TI) were significantly different with true 3D TA: entropy and energy. All these TIs discriminated between the two tumor types significantly by ROC analysis. For the CET1 images there was no SDHP at all by 3D TA.
conclusionOur results indicate that the used textural analysis methods may help with discriminating between brain metastases of different primary tumors.
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