ArticleQuantitative imaging in medicine and surgery2021
Differential diagnosis between small breast phyllodes tumors and fibroadenomas using artificial intelligence and ultrasound data.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A case of giant juvenile fibroadenoma in an adolescent girl: a case report.Journal of medical case reports · 2026Article
- TMAN: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images.Journal of imaging informatics in medicine · 2026Article
- Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Intra-tumor and peritumoral radiomics and deep learning based on ultrasound for differentiating fibroadenoma and phyllodes tumor: a multicenter study.Frontiers in oncology · 2025Article
- A neural network with a human learning paradigm for breast fibroadenoma segmentation in sonography.Biomedical engineering online · 2024Article
- Application of ultrasound-based radiomics models of breast masses to predict invasive components of encapsulated papillary carcinoma.Quantitative imaging in medicine and surgery · 2023Article
- A lightweight neural network for lung nodule detection based on improved ghost module.Quantitative imaging in medicine and surgery · 2023Article
- Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.Sensors (Basel, Switzerland) · 2023Article
- Giant juvenile fibroadenoma in a 14-year old Chinese female: A case report.World journal of clinical cases · 2023Article
- Learning curve in ultrasound-guided vacuum-assisted excision of breast lesions for surgeons and ultrasound physicians.Quantitative imaging in medicine and surgery · 2023Article
- Assessment of the statistical optimization strategies and clinical evaluation of an artificial intelligence-based automated diagnostic system for thyroid nodule screening.Quantitative imaging in medicine and surgery · 2023Article
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18 authors.
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Abstract
backgroundIt is challenging to differentiate between phyllodes tumors (PTs) and fibroadenomas (FAs). Artificial intelligence (AI) can provide quantitative information regarding the morphology and textural features of lesions. This study attempted to use AI to evaluate the ultrasonic images of PTs and FAs and to explore the diagnostic performance of AI features in the differential diagnosis of PTs and FAs.
methodsA total of 40 PTs and 290 FAs <5 cm in maximum diameter found in female patients were retrospectively analyzed. All tumors were segmented by doctors, and the features of the lesions were collated, including circularity, height-to-width ratio, margin spicules, margin coarseness (MC), margin indistinctness, margin lobulation (ML), internal calcification, angle between the long axis of the lesion and skin, energy, grey entropy, and grey mean. The differences between PTs and FAs were analyzed, and the diagnostic performance of AI features in the differential diagnosis of PTs and FAs was evaluated.
resultsStatistically significant differences (P<0.05) were found in the height-to-width ratio, ML, energy, and grey entropy between the PTs and FAs. Receiver operating characteristic (ROC) curve analysis of single features showed that the area under the curve [(AUC) 0.759] of grey entropy was the largest among the four features with statistically significant differences, and the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.925, 0.459, 0.978, and 0.190, respectively. When considering the combinations of the features, the combination of height-to-width ratio, margin indistinctness, ML, energy, grey entropy, and internal calcification was the most optimal of the combinations of features with an AUC of 0.868, and a sensitivity, specificity, PPV, and NPV of 0.734, 0.900, 0.982, and 0.316, respectively.
conclusionsQuantitative analysis of AI can identify subtle differences in the morphology and textural features between small PTs and FAs. Comprehensive consideration of multiple features is important for the differential diagnosis of PTs and FAs.
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