Differentiation between Phyllodes Tumors and Fibroadenomas Based on Mammographic Sonographic and MRI Features
Lale Duman,Naciye Sinem Gezer,Pinar Balci,Canan Altay,Isil Basara,Merih Güray Durak,Ali İbrahim Sevinç +6 more
TL;DR: M mammographic, sonographic, and MRI findings of fibroadenomas and phyllodes tumors could help radiologists to ascertain imaging-histological concordance and guide clinicians in their decision making regarding adequate follow-up or the necessity of biopsy.
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Abstract: Background: This study was performed to compare the mammographic, sonographic, and magnetic resonance imaging (MRI) characteristics of phyllodes tumors and fibroadenomas, which may resemble each other. Methods: Preoperative mammograms, B-mode and Doppler sonograms, and dynamic breast MRIs of 72 patients with pathologically proven fibroadenomas and 70 patients with pathologically proven phyllodes tumor were evaluated in this retrospective study. Statistical significance was evaluated using chi-square and Fisher's exact tests. Correlations in lesion size among radiological methods were examined by Pearson's correlation analysis. Results: The features that differed on mammogram were size, shape, and margin of the mass. Sonograms showed significant differences in size, shape, margin, echo pattern, and vascularization of the mass. Pearson's correlation analysis showed strong agreement among radiological methods in terms of assessment of size. Tumor size ≥ 3 cm, irregular shape, microlobulated margins, complex internal echo pattern, and hypervascularity were significant findings of phyllodes tumors. Internal cystic areas on MRI were frequently associated with phyllodes tumors. Conclusion: Mammographic, sonographic, and MRI findings of fibroadenomas and phyllodes tumors could help radiologists to ascertain imaging-histological concordance and guide clinicians in their decision making regarding adequate follow-up or the necessity of biopsy.
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Discrimination between phyllodes tumor and fibro-adenoma: Does artificial intelligence-aided mammograms have an impact?
TL;DR: In this paper , the authors assess the ability of artificial intelligence-aided mammograms to aid the ultrasound in the discrimination between phyllodes tumors and fibro-adenomas, and the results show that the AI abnormality scoring of 49.5% upgraded the sensitivity to 89.6% and specificity to 94.8% in the ability to discriminate PT from FA masses.
Metastatic and Malignant Phyllodes Tumors of the Breast: An Update for Current Management.
Brandon Goodwin,A. Oyinlola,Meejan Palhang,Danielle Lehman,Rebecca M. Platoff,U. Atabek,Francis Spitz,Young Seob Hong +7 more
TL;DR: Metastatic and malignant phyllodes tumors of the breast are rare and aggressive neoplasms with limited treatment options. Current management practices lack consensus. Surgically, metastatectomy has shown promise in increasing overall survival. Radiotherapy and chemotherapy can provide palliation and pain control, while radiation has been shown to reduce local recurrence. Further research is needed to refine diagnosis and treatment strategies for this rare malignancy.
2
Giant fibroadenoma mimicking phylloides tumor in post-menopausal female: a case report and review of literature
TL;DR: Fibroadenomas are common benign lesions of breast before the age of 30 years and phylloides tumor of breast have different approach of management, it is important to distinguish them preoperatively.
Giant phyllodes tumor of the breast: A case report
Michael Mousa,Shiv Bhanu,Michael Chin +2 more
TL;DR: The case of a 34-year-old female surrogate mother without any reported personal or family history of breast cancer who presented with a rapidly growing left breast mass, pathologically proven to be a phyllodes tumor is presented, believed to be the first case report of phyllodes tumor related to a surrogate pregnancy.
2
Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians
Zhao‐ting Shi,Xiaowen Ma,Anqi Jin,Jian Zhou,Na Li,Danli Sheng,Cai Chang,Jiangang Chen,Jiawei Li +8 more
TL;DR: In this article , three deep learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify fibroadenomas (FAs) and phyllodes tumors (PTs).
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