ArticleAcademic radiology2021
Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis.
Article in Academic radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- The conformation-selective PI3Kδ inhibitor roginolisib in patients with advanced or metastatic cancer, including metastatic uveal melanoma: first-in-human clinical trial.Nature communications · 2026Article
- A Predictive MRI Radiomics Model for Histologic Differentiation in Soft Tissue Sarcomas.Cancers · 2026Article
- Exploring the recurrence and metastasis of breast invasive ductal carcinoma based on machine learning and survival analysis.Frontiers in oncology · 2026Article
- Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer.Tomography (Ann Arbor, Mich.) · 2025Article
- Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma.Diagnostics (Basel, Switzerland) · 2022Article
- A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials.JCO clinical cancer informatics · 2022Article
- Role of hepatic metastatic lesion size on inter-reader reproducibility of CT-based radiomics features.European radiology · 2022Article
- Molecular Mechanism of Secondary Endocrine Resistance in Luminal Breast Cancer.BioMed research international · 2021Article
- Radiomics of Liver Metastases: A Systematic Review.Cancers · 2020Review
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Authors and funding
6 authors.
Funding
Abstract
purposeTo evaluate the effect of the anatomic size on 3D radiomic imaging features of the breast cancer hepatic metastases. MATERIALS AND
methodsCT scans of 81 liver metastases from 54 patients with breast cancer were evaluated. Ten most common 3D radiomic features from the histogram and gray level co-occurrence matrix (GLCM) categories were calculated for the hepatic metastases (HM) and compared to normal liver (NL). The effect of size was evaluated by using linear mixed-effects regression models. The effect of size on different radiomic features was analyzed for both liver lesions and background liver.
resultsThree-dimensional radiomic features from GLCM demonstrate an important size dependence. The texture-feature size dependence was found to be different among feature categories and between the HM and NL, thus demonstrating a discriminatory power for the tissue type. Significant difference in the slope was found for GLCM homogeneity (NL slope = 0.004, slope difference 95% confidence interval [CI] 0.06-0.1, p <0.001), contrast (NL slope = 45, slope difference 95% CI 205-305, p <0.001), correlation (NL slope = 0.04, slope difference 95% CI 0.11-0.21, p <0.001), and dissimilarity (NL slope = 0.7, slope difference 95% CI 3.6-5.4, p <0.001). The GLCM energy (NL slope = 0.002, slope difference 95% CI -0.0005 to -0.0003, p <0.007), and entropy (NL slope = 1.49, slope difference 95% CI 0.07-0.52, p <0.009) exhibited size-dependence for both NL and HM, although demonstrating a difference in the slope between themselves.
conclusionRadiomic features of breast cancer hepatic metastasis exhibited significant correlation with tumor size. This finding demonstrates the complex behavior of imaging features and the need to include feature-specific properties into radiomic models.
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