Clinical presentation and radiologic imaging findings of phyllodes tumors: benign and borderline/malignant phyllodes tumors.

Background: Phyllodes tumor is a rare fibroepithelial neoplasm of the breast, which is classified histologically as benign, borderline, or malignant. Accurate preoperative diagnosis allows the correct surgical planning and reoperation avoidance. Objective: To describe the clinical presentation and radiologic features of phyllodes tumors and differentiate between benign and non-benign (borderline and malignant) groups. Methods: A retrospective study of 57 patients with a diagnosis of phyllodes tumor who had preoperative imaging (mammography, ultrasound, or CT chest) and histological confirmation. The data was collected from 1 June 2011 to 30 September 2021. The imaging features of the phyllodes tumors were described according to the 5th edition of the ACR BI-RADS lexicon. For comparing between two groups, the student t-test, Wilcoxon rank sum test, Chi-square test, and Fisher's exact test were used for statistical analyses. The logistic regression analysis was calculated for non-benign phyllodes tumor prediction. Results: From 57 patients, the pathologic results were benign for 43 cases and non-benign phyllodes tumors for 14 cases. There was no differentiation of mammographic and CT features between benign and non-benign groups. Non-benign phyllodes tumors had the statistical significance of menopausal status, entire breast involvement, tumor size larger than 10 cm, and heterogeneous echo on univariable analysis. After multivariable analysis, menopausal status (odd ratios=13.79, p=0.04) and presence of vessels in the rim (odd ratios=16.51, p=0.019) or absent vascularity (odd ratios=8.45, p=0.047) on doppler ultrasound were significantly increased possibility of non-benign phyllodes tumor. Conclusions: Menopausal status and presence of vessels in the rim or absent vascularity on Doppler ultrasound were important predictors for the diagnosis of non-benign phyllodes tumor.

Clinical and radiological evaluation of a phyllodes tumor of the breast: a case report.

Phyllodes tumors (PTs) are rare breast tumors characterized by varying biological behavior and heterogeneous clinical findings. As a result, accurately diagnosing PTs preoperatively is challenging, often leading to misdiagnosis. A 49-year-old patient presented with a steadily growing right breast mass that had persisted over a 10-year period. Breast mammography and ultrasonography results indicated the presence of a PT. Following a lumpectomy, the patient was diagnosed with a borderline PT. However, nearly 1 year later, she was readmitted due to the recurrence of a palpable mass at the site. Consequently, 1 year and 8 months after the initial operation, she underwent thoracoscopic lobectomy to address solitary lung metastases. Subsequently, the patient experienced brain metastasis and massive hemorrhage 14 months later. Long-term follow-up was recommended. This case study presents an instance of borderline PT with clinical and imaging features that are crucial for guiding clinical operations and evaluating patient prognosis.

Small Bowel Metastases From Malignant Phyllodes Breast Tumor Detected by 18 F-FDG PET/CT.

ABSTRACT: The most common sites of distant metastases are lung, bone, pleura, and mediastinum in malignant phyllodes breast tumors. However, small bowel metastasis from malignant phyllodes breast tumors is rare. We reported that using CT and FDG PET/CT imaging we identified a case with small bowel metastasis from breast cancer. PET/CT scan showed that high 18 F-FDG uptake occurred in the duodenum and jejunum. Histopathology and immunohistochemistry analyses further confirmed that malignant phyllodes tumors are derived from the breast.

Imaging and Management of Fibroepithelial Lesions of the Breast: Radiologic-Pathologic Correlation.

Fibroepithelial lesions (FELs) are among the most common breast masses encountered by breast radiologists and pathologists. They encompass a spectrum of benign and malignant lesions, including fibroadenomas (FAs) and phyllodes tumors (PTs). FAs are typically seen in young premenopausal women, with a peak incidence at 20-30 years of age, and have imaging features of oval circumscribed hypoechoic masses. Although some FA variants are especially sensitive to hormonal influences and can exhibit rapid growth (eg, juvenile FA and lactational adenomas), most simple FAs are slow growing and involute after menopause. PTs can be benign, borderline, or malignant and are more common in older women aged 40-50 years. PTs usually manifest as enlarging palpable masses and are associated with a larger size and sometimes with an irregular shape at imaging compared with FAs. Although FA and FA variants are typically managed conservatively unless large and symptomatic, PTs are surgically excised because of the risk of undersampling at percutaneous biopsy and the malignant potential of borderline and malignant PTs. As a result of the overlap in imaging and histologic appearances, FELs can present a diagnostic challenge for the radiologist and pathologist. Radiologists can facilitate accurate diagnosis by supplying adequate tissue sampling and including critical information for the pathologist at the time of biopsy. Understanding the spectrum of FELs can facilitate and guide appropriate radiologic-pathologic correlation and timely diagnosis and management of PTs. Published under a CC BY 4.0 license. Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

68 Ga-DOTA-FAPI-04 Imaging of Bilateral Phyllodes Tumor.

ABSTRACT: We present a case of bilateral Phyllodes tumor located in both breasts in a 41-year-old woman who was detected with increased uptake on 68 Ga-FAPI-04 (fibroblast activation protein inhibitor) and 18 F-FDG PET/CT imaging. The tumor filling up the right breast was identified as borderline Phyllodes. The tumor with mild uptake in the left breast was reported as a benign Phyllodes tumor.

Differentiation Between Phyllodes Tumors and Fibroadenomas of Breast Using Mammography-based Machine Learning Methods: A Preliminary Study.

OBJECTIVE: To investigate the diagnostic performance of a mammography-based radiomics model for distinguishing phyllodes tumors (PTs) from fibroadenomas (FAs) of the breast. MATERIALS AND METHODS: A total of 156 patients were retrospectively included (75 with PTs, 81 with FAs) and divided into training and validation groups at a ratio of 7:3. Radiomics features were extracted from craniocaudal and mediolateral oblique images. The least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were performed to select features. Three machine learning classifiers, including logistic regression (LR), K-nearest neighbor classifier (KNN) and support vector machine (SVM), were implemented in the radiomics model, imaging model and combined model. Receiver operating characteristic curves, area under the curve (AUC), sensitivity and specificity were computed. RESULTS: Among 1084 features, the LASSO algorithm selected 17 features, and PCA further selected 6 features. Three machine learning classifiers yielded the same AUC of 0.935 in the validation group for the radiomics model. In the imaging model, KNN yielded the highest accuracy rate of 89.4% and AUC of 0.947 in the validation set. For the combined model, the SVM classifier reached the highest AUC of 0.918 with an accuracy rate of 86.2%, sensitivity of 83.9%, and specificity of 89.4% in the training group. In the validation group, LR yielded the highest AUC of 0.973. The combined model had a relatively higher AUC than the radiomics model or imaging model, especially in the validation group. CONCLUSIONS: Mammography-based radiomics features demonstrate good diagnostic performance for discriminating PTs from FAs.

Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.

Mammography-based radiomics analysis and imaging features for predicting the malignant risk of phyllodes tumours of the breast.

AIM: To determine whether the mammography (MG)-based radiomics analysis and MG/ultrasound (US) imaging features could predict the malignant risk of phyllodes tumours (PTs) of the breast. MATERIALS AND METHODS: Seventy-five patients with PTs were included retrospectively (39 with benign PTs, 36 with borderline/malignant PTs) and divided into thetraining (n=52) and validation groups (n=23). The clinical information, MG and US imaging characteristics, and histogram features were extracted from craniocaudal (CC) and mediolateral oblique (MLO) images. The lesion region of interest (ROI) and perilesional ROI were delineated. Multivariate logistic regression analysis was performed to determine the malignant factors of PTs. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC), sensitivity, and specificity were calculated. RESULTS: There was no significant difference found in the clinical or MG/US features between benign and borderline/malignant PTs. In the lesion ROI, variance in the CC view and mean and variance in the MLO view were independent predictors. The AUC was 0.942, sensitivity and specificity were 96.3% and 92%, respectively, in the training group. In the validation group, the AUC was 0.879, the sensitivity was 91.7%, and the specificity was 81.8%. In the perilesional ROI, the AUCs were 0.904 and 0.939, sensitivities were 88.9% and 91.7%, and the specificities were 92% and 90.9% in the training and validation groups, respectively. CONCLUSIONS: MG-based radiomic features could predict the risk of malignancy of patients with PTs and may be used as a potential tool to differentiate benign and borderline/malignant PTs.

Tumor phyllodes: reporte de un caso tratado con cirugía oncoplástica extrema y revisión de la literatura / Phyllodes tumor: Case report treated with extreme oncoplastic surgery and literature review

El tumor phyllodes representa el 1% de los tumores primarios de la mama, es una rara neoplasia fibroepitelial que tiene a la cirugía como piedra angular en el tratamiento: la resección con márgenes amplios (mayores o iguales a 1 cm) y la mastectomía son los tratamientos recomendados. Tomando en cuenta el impacto de los bordes quirúrgicos en la recurrencia local, el gran volumen de resección, la velocidad de crecimiento y la dificultad de predecir el comportamiento tumoral con el estudio preoperatorio de biopsia percutánea; la cirugía de conservación es un reto en muchos casos. Presentamos el caso de una paciente tratada con cirugía oncoplástica extrema, así como la revisión de la literatura de esta entidad. (AU)

The phyllodes tumor represents 1% of the primary tumors of the breast, it is a rare fibroepithelial neoplasia that has surgery as a cornerstone in the treatment: resection with wide margins (greater than or equal to 1 cm) and mastectomy are the recommended treatments; considering the impact of the surgical edges on local recurrence, the large resection volume, growth speed and the difficulty of predicting tumor behavior with the pre-operative percutaneous biopsy study; Conservation surgery is challenging in numerous instances. We present the case of a patient treated with extreme oncoplastic surgery, as well as a review of the literature on this entity. (AU)

Rare Case of Phyllodes Tumor of Breast with Cardiac and Pancreatic Metastases: Findings on 18F-FDG PET/CT.

Phyllodes tumor (PT) represents a rare type of breast tumor arising from the stromal component rather than the epithelium. Metastatic spread occurs hematogenously, with lung, bone, brain, and liver being the most common sites. We present the 18F-FDG PET/CT scan of one such case of phyllodes tumor showing cardiac and pancreatic metastases, which are an extremely rare occurrence.

High-Risk Breast Lesions Diagnosed by Ultrasound-Guided Vacuum-Assisted Excision.

PURPOSE: The aim of this study was to analyze the role of ultrasound-guided vacuum-assisted excision (US-guided VAE) in the treatment of high-risk breast lesions and to evaluate the clinical and US features of the patients associated with recurrence or development of malignancy. MATERIALS AND METHODS: Between April 2010 and September 2021, 73 lesions of 73 patients underwent US-guided VAE and were diagnosed with high-risk breast lesions. The incidence of recurrence or development of malignancy for high-risk breast lesions was evaluated at follow-up period. The clinical and US features of the patients were analyzed to identify the factors affecting the recurrence or development of malignancy rate. RESULTS: Only benign phyllodes tumors on US-guided VAE showed recurrences, while other high-risk breast lesions that were atypical ductal hyperplasia (ADH), lobular neoplasia (atypical lobular hyperplasia/lobular carcinoma in situ), radial scar, and flat epithelial atypia did not show recurrences or malignant transformation. The recurrence rate of the benign phyllodes tumor was 20.8% (5/24) in a mean follow-up period of 34.3 months. The recurrence rate of benign phyllodes tumor with distance from nipple of less than 1 cm was significantly higher than that of lesions with distance from nipple of more than 1 cm (75% vs. 10%, p < 0.05). CONCLUSIONS: Benign phyllodes tumors without concurrent breast cancer could be safely followed up instead of surgical excision after US-guided VAE when the lesions were classified as BI-RADS 3 or 4A by US.

Machine Learning-Based Ultrasound Texture Analysis in Differentiation of Benign Phyllodes Tumors from Borderline-Malignant Phyllodes Tumors.

PURPOSE: Phyllodes tumors (PTs) are uncommon fibroepithelial breast lesions that are classified as three different forms as benign phyllodes tumor (BPT), borderline phyllodes tumor (BoPT), and malignant phyllodes tumor (MPT). Conventional radiologic methods make only a limited contribution to exact diagnosis, and texture analysis data increase the diagnostic performance. In this study, we aimed to evaluate the contribution of texture analysis of US images (TAUI) of PTs in order to discriminate between BPTs and BoPTs-MPTs. METHODS: The number of patients was 63 (41 BPTs, 12 BoPTs, and 10 MPTs). Patients were divided into two groups (Group 1-BPT, Group 2-BoPT/MPT). TAUI with LIFEx software was performed retrospectively. An independent machine learning approach, MATLAB R2020a (Math- Works, Natick, Massachusetts) was used with the dataset with p < 0.004. Two machine learning approaches were used to build prediction models for differentiating between Group 1 and Group 2. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance of statistically significant texture data between phyllodes subgroups. RESULTS: In TAUI, 10 statistically significant second order texture values were identified as significant factors capable of differentiating among the two groups (p < 0.05). Both of the models of our dataset make a diagnostic contribution to the discrimination between BopTs-MPTs and BPTs. CONCLUSION: In PTs, US is the main diagnostic method. Adding machine learning-based TAUI to conventional US findings can provide optimal diagnosis, thereby helping to choose the correct surgical method. Consequently, decreased local recurrence rates can be achieved.

Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors.

BACKGROUND: Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs). PURPOSE: To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs. STUDY TYPE: Retrospective. POPULATION: A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs). FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI). ASSESSMENT: A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model. STATISTICAL TESTS: Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models. RESULTS: In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model. DATA CONCLUSION: Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Fibroepithelial lesions of the breast: improving the accuracy of imaging diagnosis and reducing unnecessary biopsy.

Fibroadenomas and phyllodes tumours are fibroepithelial lesions of the breast. Fibroadenomas are common benign breast masses encountered both symptomatically and incidentally. Phyllodes tumours are uncommon and usually present symptomatically. Management of fibroadenomas focuses on reducing biopsies without missing cancers while radiological management of phyllodes tumours should focus on predicting the need for surgical excision with or without margins.

An integrative ultrasound-pathology approach to improve preoperative phyllodes tumor classification: A pilot study.

OBJECTIVE: Preoperative diagnosis of phyllodes tumor (PT) is challenging, core-needle biopsy (CNB) has a significant rate of understaging, resulting in suboptimal surgical planification. We hypothesized that the association of imaging data to CNB would improve preoperative diagnostic accuracy compared to biopsy alone. METHODS: In this retrospective pilot study, we included 59 phyllodes tumor with available preoperative imaging, CNB and surgical specimen pathology. RESULTS: Two ultrasound features: tumor heterogeneity and tumor shape were associated with tumor grade, independently of CNB results. Using a machine learning classifier, the association of ultrasound features with CNB results improved accuracy of preoperative tumor classification up to 84%. CONCLUSION: An integrative approach of preoperative diagnosis, associating ultrasound features and CNB, improves preoperative diagnosis and could thus optimize surgical planification.

Logistic regression analysis of ultrasound findings in predicting the malignant and benign phyllodes tumor of breast.

OBJECTIVE: To evaluate ultrasound characteristics in the prediction of malignant and benign phyllodes tumor of the breast (PTB) by using Logistic regression analysis. METHODS: 79 lesions diagnosed as PTB by pathology were analyzed retrospectively. The ultrasound features of PTB were recorded and compared between benign and malignant tumors by using single factor and multiple stepwise Logistic regression analysis. Moreover, the Logistic regression model for malignancy prediction was also established. RESULTS: There were 79 patients with PTB, including 39 benign PTBs and 40 malignant PTBs (33 borderline PTBs and 7 malignant PTBs by pathologic classification). The area under the ROC curve (AUC) of lesion size and age were 0.737 and 0.850 respectively. There were significant differences in age, lesion size, shape, internal echo, liquefaction, and blood flow between malignant and benign PTBs by using single-factor analysis (P<0.05). Age, internal echo, and liquefaction were significant features by using Logistic regression analysis. The corresponding regression equation In (p/(1 - p) = -3.676+2.919 internal echo +3.029 liquefaction +4.346 age). CONCLUSION: Internal echo, age, and liquefaction are independent ultrasound characteristics in predicting the malignancy of PTBs.

MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas.

OBJECTIVES: To evaluate the diagnostic performance of MRI-based radiomics model for differentiating phyllodes tumors of the breast from fibroadenomas. METHODS: This 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. RESULTS: Among 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). CONCLUSIONS: Radiomics 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.

Effect of pregnancy and lactation on a benign phyllodes tumour.

Phyllodes tumours occurring in pregnancy are very rare. While most cases presented as rapidly enlarging masses, we present a benign phyllodes tumour which had the most growth in the first half of pregnancy followed by gradual growth in the latter half of pregnancy and lactation, as characterised on ultrasound imaging. This is the first report, to the best of our knowledge, which has objective measurements of the lesion before, during and after pregnancy. It also highlighted the need for a vigilant approach to fibroepithelial lesions in pregnancy, instead of attributing the growth of these lesions solely to hormonal changes.

Native T1 mapping of the breast in MRI to differentiate fibroadenomas from benign phyllodes tumors: a preliminary study.

OBJECTIVE: Both fibroadenomas (FAs) and phyllodes tumors (PTs) are classified as fibroepithelial lesions. PTs are rare fibroepithelial neoplasms that have a morphologic spectrum ranging from benign to malignant. The differentiation of these entities is important as PTs are to be enucleated surgically. The purpose of this study was to calculate the T1 relaxation times of fibroadenomas and phyllodes tumors and assess the potency of native T1 mapping for the differentiation of these tumors. MATERIALS AND METHODS: This prospective study included 11 patients with a proven diagnosis of benign PT and 14 patients with a proven diagnosis of FA. All the patients underwent T1 mapping prior to conventional dynamic contrast-enhanced MRI (DCE-MRI). Two radiologists, in consensus, selected lesion locations using freehand regions of interest from the DCE images and copied them onto T1 maps to acquire T1 relaxation times. The T1 relaxation times of the FA and PT groups were compared statistically. RESULTS: The mean T1 relaxation times were higher in the PT group compared to the FA group (p ≤ 0.001). The receiver operating characteristic analysis showed that the T1 relaxation time being longer than 1,478 ms differentiated PTs from FAs with a sensitivity of 0.89, specificity of 1, and area under the curve value of 0.93. CONCLUSION: We found that benign PTs had longer relaxation times in T1 mapping compared to FAs. Native T1 mapping can be used to differentiate PTs from FAs and adding T1 mapping in breast MRI in cases with fast-growing fibroepithelial lesions or multiple fibroepithelial lesions can facilitate the decision-making process.