ArticleEuropean radiology2022
MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 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
- Application of contrast-enhanced CT-based radiomics with machine learning to predict WHO/ISUP grading in clear cell renal cell carcinoma.BMC cancer · 2026Article
- TMAN: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images.Journal of imaging informatics in medicine · 2026Article
- Case Report: Recurrent malignant phyllodes tumor with malignant peripheral nerve sheath tumor-like differentiation and loss of H3K27me3/RB1 expression.Frontiers in oncology · 2026Article
- Synchronous bilateral phyllodes tumors with discordant grades and false-positive axillaryFrontiers in oncology · 2026Article
- A convenient model based on mammography and magnetic resonance imaging for preoperative differentiation of breast phyllodes tumors and fibroadenomas.Gland surgery · 2025Article
- Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Intra-tumor and peritumoral radiomics and deep learning based on ultrasound for differentiating fibroadenoma and phyllodes tumor: a multicenter study.Frontiers in oncology · 2025Article
- The potential role of breast MRI in evaluation of triple-negative breast cancer and fibroadenoma of less than 3 cm.Translational cancer research · 2024Article
- Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT.Dento maxillo facial radiology · 2024Article
- Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears.Journal of orthopaedic surgery and research · 2024Article
- An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.Current oncology (Toronto, Ont.) · 2024Review
- Article
- Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.Sensors (Basel, Switzerland) · 2023Article
- Clinical outcomes and biomarkers of phyllodes tumors of the breast: A single-center retrospective study.Cancer medicine · 2023Article
- Editorial for "Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors".Journal of magnetic resonance imaging : JMRI · 2023Article
- Gender Medicine in Clinical Radiology Practice.Journal of personalized medicine · 2023Review
- Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography.Current oncology (Toronto, Ont.) · 2023Article
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8 authors.
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Abstract
objectivesTo evaluate the diagnostic performance of MRI-based radiomics model for differentiating phyllodes tumors of the breast from fibroadenomas.
methodsThis retrospective study included 88 patients (32 with phyllodes tumors and 56 with fibroadenomas) who underwent MRI. Radiomic features were extracted from T2-weighted image, pre-contrast T1-weighted image, and the first-phase and late-phase dynamic contrast-enhanced MRIs. To create stable machine learning models and balanced classes, data augmentation was performed. A least absolute shrinkage and selection operator (LASSO) regression was performed to select features and build the radiomics model. A radiological model was constructed from conventional MRI features evaluated by radiologists. A combined model was constructed using both radiomics features and radiological features. Machine learning classifications were done using support vector machine, extreme gradient boosting, and random forest. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to assess the performance of each model.
resultsAmong 1070 features, the LASSO logistic regression selected 35 features. Among three machine learning classifiers, support vector machine had the best performance. Compared to the radiological model (AUC: 0.77 ± 0.11), the radiomics model (AUC: 0.96 ± 0.04) and combined model (0.97 ± 0.03) had significantly improved AUC values (both p < 0.01) in the validation set. The combined model had a relatively higher AUC than that of the radiomics model in the validation set, but this was not significantly different (p = 0.391).
conclusionsRadiomics analysis based on MRI showed promise for discriminating phyllodes tumors from fibroadenomas. KEY POINTS: • The radiomics model and the combined model were superior to the radiological model for differentiating phyllodes tumors from fibroadenomas. • The SVM classifier performed best in the current study. • MRI-based radiomics model could help accurately differentiate phyllodes tumors from fibroadenomas.
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