ArticleAmerican journal of translational research2020
Magnetic resonance imaging semantic and quantitative features analyses: an additional diagnostic tool for breast phyllodes tumors.
Article in American journal of translational research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Diagnostic value of a multiparametric magnetic resonance imaging-based nomogram incorporating apparent diffusion coefficient in preoperative differentiation of phyllodes tumors from breast cancer: a retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- 2.5D Deep Learning and Machine Learning for Discriminative DLBCL and IDC with Radiomics on PET/CT.Bioengineering (Basel, Switzerland) · 2025Article
- Phyllodes tumors of the breast: A comprehensive review of diagnosis, treatment, and follow-up.Clinics (Sao Paulo, Brazil) · 2025Review
- Editorial for "Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors".Journal of magnetic resonance imaging : JMRI · 2023Article
- Can DWI provide additional value to Kaiser score in evaluation of breast lesions.European radiology · 2022Article
- Assessment of quantitative dynamic contrast-enhanced MRI in distinguishing different histologic grades of breast phyllode tumor.European radiology · 2022Article
- Predicting the pathological grade of breast phyllodes tumors: a nomogram based on clinical and magnetic resonance imaging features.The British journal of radiology · 2021Article
- The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors.Frontiers in oncology · 2021Article
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Authors and funding
11 authors.
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Abstract
objectiveThis study aimed to differentiate benign and non-benign (borderline/malignant) phyllodes tumors of the breast by the semantic and quantitative features in magnetic resonance imaging (MRI).
methodsThe female patients, diagnosed with phyllodes tumors by MRI and pathological test, were retrospectively selected from December, 2006 to April, 2019. The MRI of benign, borderline and malignant phyllodes tumors was analyzed using 8 semantic features and 20 computed quantitative features from diffuse contrast-enhanced magnetic resonance imaging (DCE-MRI). The semantic features were analyzed by univariate analysis. The least absolute shrinkage and selection operator (LASSO) method was used to identify the optimal subset of MRI quantitative features. According to the results from multivariate logistic regression for the semantic and quantitative features, the model was constructed to differentiate benign and non-benign (borderline/malignant) phyllodes tumors.
resultsThirty-two benign (58.18%), 13 borderline (23.64%) and 10 malignant (18.18%) phyllodes tumors were identified in 54 patients. Five semantic features were proved to be significantly correlated with pathologic grade, including size, the T1 weighted image signal intensity, fat-saturated T2-weighted image signal intensity, enhanced signal intensity, and kinetic curve pattern. With the analysis of LASSO method, three quantitative texture features with significant predictive ability were selected. The model combining both the semantic and quantitative features was proved to have good performance in differentiation on phyllodes tumors, yielding an area under receiver operating characteristic curve, accuracy, sensitivity and specificity of 0.893, 0.933, 1.000, and 0.818, respectively.
conclusionThe constructed model based on the semantic and quantitative features of DCE-MRI can significantly improve the differential diagnosis of phyllodes tumors in breast.
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32509202PMC7270016What OpenQuestion holds
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