A neural network with a human learning paradigm for breast fibroadenoma segmentation in sonography.

BACKGROUND: Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography. METHODS: Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods. RESULTS: Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD. CONCLUSIONS: Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing.

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.

Feasibility and efficacy of ultrasound-guided high-intensity focused ultrasound of breast fibroadenoma.

OBJECTIVE: This nonrandomized prospective clinical trial aimed to assess the efficacy, safety and follow-up outcomes of ultrasound-guided high-intensity focused ultrasound (USgHIFU) surgery in patients with breast fibroadenoma. METHODS: With the approval of the institutional ethics committee and written informed consent, a total of 113 patients diagnosed with breast fibroadenoma by core-needle biopsy in our hospital were recruited. USgHIFU surgery was performed under local anesthesia. Contrast-enhanced ultrasound (CEUS) or contrast-enhanced MRI (CEMRI) was performed to evaluate the nonperfused volume (NPV). The patients were followed up with physical examination and ultrasound imaging. RESULTS: The clinical outcome of 85 patients with 147 fibroadenomas with a follow-up time of more than 3 months was analyzed in this study. Fifty-two patients had one lesion, twenty-one patients had two lesions and twelve patients had more than two lesions. During USgHIFU, the median localization time for all fibroadenomas was 3 (interquartile range: 1, 5) min, and the median treatment time was 9 (interquartile range: 5, 15) min. Under local anesthesia, all the patients tolerated the treatment well. No serious epidermal burns were observed in any of the patients. Based on CEUS or CEMRI imaging evaluation, the median NPV ratio was 100% (interquartile range: 79.2%, 116.8%). The VRR were 26.77 ± 50.05%, 50.22 ± 42.01% and 72.74 ± 35.39% at 3-6 months, 6-12 months and >12 months, respectively, which showed significant statistical difference (p < .001). CONCLUSION: Ultrasound-guided HIFU surgery is an effective and safe noninvasive alternative technique for the treatment of breast fibroadenoma.

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.

Efficacy and safety of vacuum-assisted excision (VAE) of fibroadenomas: experience in a tertiary centre.

PURPOSE: To evaluate the technical success and efficacy rates of US-guided percutaneous vacuum-assisted excision (VAE) of breast fibroadenomas, also assessing procedural complications and long-term patient satisfaction rates. MATERIALS AND METHODS: The institutional database of a tertiary breast cancer referral centre was retrospectively reviewed to retrieve all women with fibroadenomas who underwent US-guided VAE between May 2011 and September 2019. We subsequently included in this study all fibroadenomas with a maximum diameter of 3 cm at US and an available histological confirmation obtained by core-needle biopsy before VAE. Immediately after VAE, technical success (defined as the correct VAE execution) and the occurrence of procedural complications were evaluated. Imaging follow-up (US ± mammography) after 6, 12, 24 and 36 months was performed to evaluate technical efficacy (defined as the absence of fibroadenoma recurrence at 6-month follow-up). Long-term patient satisfaction was evaluated with telephonic interviews in October 2022. RESULTS: We retrospectively included 108 women (median age 46 years) with 110 fibroadenomas diagnosed at core-needle biopsy with a median lesion size at US of 12 mm. Technical success was obtained in 110/110 VAEs (100%). Minor procedural complications (haematomas) occurred in 7/110 VAEs (6%), whereas 8/110 patients had a fibroadenoma recurrence at 6-month follow-up, resulting in a 93% technical efficacy (102/110 VAEs). All patients available for telephonic follow-up (104/104, 100%) reported high satisfaction with VAE results. CONCLUSION: US-guided VAE is a safe and effective procedure for the excision of fibroadenomas, representing a viable alternative to surgery, with a low complication rate and high patient satisfaction.

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.

Dosimetric analysis of ultrasound-guided high intensity focused ultrasound ablation for breast fibroadenomas: a retrospective study.

OBJECTIVE: To investigate the factors influencing the sonication dosage and efficiency of ultrasound-guided high intensity focused ultrasound (USgHIFU) for breast fibroadenomas. MATERIALS AND METHODS: Forty-nine patients with 78 breast fibroadenomas who underwent USgHIFU were retrospectively analyzed. The energy efficiency factor (EEF) was set as dependent variable, and the factors possibly influencing the sonication dosage, including age, body mass index (BMI), fibroadenoma size, distance from the shallow margin of the fibroadenoma to skin, distance from the deep margin of the fibroadenoma to pectoralis major, types of near field acoustic pathway, and Adler blood flow classification of ultrasound, were set as independent variables. The correlation between EEF and these independent variables were analyzed, and an optimal scaling regression model was established. RESULTS: Fibroadenoma size, distance from the shallow margin of the fibroadenoma to skin and type of near field acoustic pathway had significant correlation with EEF (p < 0.05). An EEF (y) dosimetry model of y= -0.496X1 + 0.287X2 + 0.203X3 was established, in which X1 stands for size of fibroadenoma, X2 stands for the distance from shallow margin of the fibroadenoma to skin, and X3 stands for type of near field acoustic pathway. The predicted EEF value was significantly related to actual EEF (R = 0.698, p = 0.000). CONCLUSIONS: Fibroadenoma size, distance from the shallow margin of the fibroadenoma to skin and type of near field acoustic pathway could be used as predictors to evaluate the dosage delivery of USgHIFU treatment for breast fibroadenomas.

Evaluating the learning curve of high intensity focus ultrasound for breast fibroadenoma by CUSUM analysis: a multi-center study.

OBJECTIVE: To explore the learning curve of high intensity focus ultrasound (HIFU) treatment for breast fibroadenoma. METHODS: A database of 110 patients with 255 breast fibroadenomas who underwent HIFU treatment at two different clinical centers (Center 1 and 2) were retrospectively analyzed. The learning curves of HIFU treatment for breast fibroadenoma were drawn by CUSUM analysis in two centers, respectively. According to the inflection point of the learning curves, the treatment was divided into two groups: initial phase and consolidation phase. HIFU treatment parameters were compared between two groups. The effectiveness and safety results were also evaluated. RESULTS: The inflection points of the learning curves were the 60th treatment in Center 1 and the 65th treatment in Center 2. The screening time, treatment time, sonication time and hyperechoic scale change time were significantly shorter in consolidation phase than those in initial phase of the two centers (p < 0.05). There were no differences in non-perfused volume (NPV) ratio and energy effect factor (EEF) between the two groups in Center 1, while in Center 2, these above-mentioned results in consolidation phase led to a greater improvement than those in initial phase. There was no difference of Visual Analogue Scale (VAS) scores and no adverse event observed in both centers. CONCLUSION: HIFU treatment for breast fibroadenoma was effective and safe. The learning curve of HIFU treatment for breast fibroadenoma can be completed after treating 60-65 tumors without increasing the safety risk.

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.

Prospective clinical trial on the learning curve of high-intensity-focused ultrasound for the treatment of breast fibroadenoma.

INTRODUCTION: High-intensity-focused ultrasound (HIFU) is a safe and feasible treatment option for breast fibroadenoma. However, its learning curve has not been described in the medical literature. METHODS: All patients with biopsy-proven fibroadenoma considered indicated for HIFU were screened for eligibility for HIFU treatment. A total of 60 patients were recruited according to the pre-defined sample size calculation. RESULTS: Sixty consecutive patients were divided into three cohorts in chronological order. The mean tumor volume shrinkage rates in cohorts 1, 2, and 3 at 6 months post-HIFU ablation were 38%, 34%, and 59%, respectively. Significant tumor shrinkage was observed from case 41 onward (p < 0.0001). Similarly, the mean tumor volume shrinkage rates in cohorts 1, 2, and 3 at 12 months post-HIFU ablation were 45%, 51%, and 71%, respectively. Significant tumor shrinkage was observed from case 41 onwards (p < 0.0473). The mean procedure time for the first 20 patients was 48.5 (range 45-75) minutes, while that in the second 20 patients was 39.7 (range 20-60) minutes, and that in the last 20 patients was 28.9 (range 15-45) minutes. The treatment time was significantly shorter from case 41 onwards (p = 0.0481). CONCLUSION: Treatment outcomes and treatment time improved significantly after performing approximately 40 HIFU procedures.

Computer-Aided Detection of Quantitative Signatures for Breast Fibroepithelial Tumors Using Label-Free Multi-Photon Imaging.

Fibroadenomas (FAs) and phyllodes tumors (PTs) are major benign breast tumors, pathologically classified as fibroepithelial tumors. Although the clinical management of PTs differs from FAs, distinction by core needle biopsy diagnoses is still challenging. Here, a combined technique of label-free imaging with multi-photon microscopy and artificial intelligence was applied to detect quantitative signatures that differentiate fibroepithelial lesions. Multi-photon excited autofluorescence and second harmonic generation (SHG) signals were detected in tissue sections. A pixel-wise semantic segmentation method using a deep learning framework was used to separate epithelial and stromal regions automatically. The epithelial to stromal area ratio and the collagen SHG signal strength were investigated for their ability to distinguish fibroepithelial lesions. An image segmentation analysis with a pixel-wise semantic segmentation framework using a deep convolutional neural network showed the accurate separation of epithelial and stromal regions. A further investigation, to determine if scoring the epithelial to stromal area ratio and the SHG signal strength within the stromal area could be a marker for differentiating fibroepithelial tumors, showed accurate classification. Therefore, molecular and morphological changes, detected through the assistance of computational and label-free multi-photon imaging techniques, enable us to propose quantitative signatures for epithelial and stromal alterations in breast tissues.

Image-guided Microinvasive Percutaneous Treatment of Breast Lesions: Where Do We Stand?

Treatment of breast lesions has evolved toward the use of less-invasive or minimally invasive techniques. Minimally invasive treatments destroy focal groups of cells without surgery; hence, less anesthesia is required, better cosmetic outcomes are achieved because of minimal (if any) scarring, and recovery times are shorter. These techniques include cryoablation, radiofrequency ablation, microwave ablation, high-intensity focused US, laser therapy, vacuum-assisted excision, and irreversible electroporation. Each modality involves the use of different mechanisms and requires specific considerations for application. To date, only cryoablation and vacuum-assisted excision have received U.S. Food and Drug Administration approval for treatment of fibroadenomas and have been implemented as part of the treatment algorithm by the American Society of Breast Surgeons. Several clinical studies on this topic have been performed on outcomes in patients with breast cancer who were treated with these techniques. The results are promising, with more data for radiofrequency ablation and cryoablation available than for other minimally invasive methods for treatment of early-stage breast cancer. Clinical decisions should be made on a case-by-case basis, according to the availability of the technique. MRI is the most effective imaging modality for postprocedural follow-up, with the pattern of enhancement differentiating residual or recurrent disease from postprocedural changes. ©RSNA, 2021.

Feasibility and efficacy of microwave ablation for treating breast fibroadenoma.

BACKGROUND: To investigate the safety, efficacy, and follow-up outcomes of microwave ablation (MWA) in patients with breast fibroadenoma. METHODS: An institutional review board-approved this study of patients treated with MWA for breast fibroadenoma from October 2017 to March 2019. Clinical features of patients and breast fibroadenoma were analyzed. At follow-up all patients received physical examination and ultrasound imaging. RESULTS: In total, 171 patients with 271 lesions were enrolled. The mean lesion diameter was 1.35 ± 0.47 cm. The results revealed differential lesion states, including stability, enlargement, reduction, and complete regression, at 1-6, 6-12, and >12 months of follow-up. The size was reduced in 22.14% (31/140), 26.36% (29/110), and 36.36% (16/44) of the lesions at 1-6, 6-12, and >12 months of follow-up, respectively. The proportion of lesions with complete regression was 24.29% (34/140) at 1-6 months, 45.45% (50/110) at 6-12 months, and 40.91% (18/44) at >12 months of follow up. There was no significant relationship between the curative effect and age, lesion location, and blood flow in patients with breast fibroadenoma after MWA (p > .05), but there was statistically significant relationship with lesion diameter (categorized as <1.5 cm and ≥1.5 cm) (p < .05). CONCLUSIONS: The current evidence indicates that MWA is a safe and effective method for treating breast fibroadenoma. Nevertheless, further large-scale prospective trials and well-designed future studies are warranted to validate our findings.

Early onset, multiple, bilateral fibroadenomas of the breast: a case report.

BACKGROUND: While fibroadenomas are common in the general population, affecting 10-20% of women, they are rarely early-onset, multiple, and bilateral. CASE PRESENTATION: An 18-year-old woman presented with a 6 year history of multiple, bilateral breast masses without family history of breast disease. Magnetic resonance imaging (MRI, Fig. 1) of the breasts showed innumerable, bilateral breast masses ranging in size from 0.5 to 4 cm. Two needle biopsies showed fibroadenoma. Although the patient's family history did not meet National Comprehensive Cancer Network (NCCN) guidelines for genetic testing, it was performed due to the rarity of her presentation. Genetic testing identified a pathogenic mutation in the phosphatase and tensin homolog (PTEN) gene. CONCLUSIONS: A germline mutation in PTEN is associated with an increased risk of breast cancer and often occurs as part of Cowden Syndrome. This case highlights the importance of genetic testing in patients with unusual presentations of early-onset, bilateral, and multiple (greater than four) fibroadenomas.

Cystic fibroadenoma of the breast with a diffuse multicystic presentation.

BACKGROUND: Cystic fibroadenoma is an extremely rare form of complex fibroadenoma, characterized by various cystic changes. We present clinical and pathologic findings in a patient with a unique diffuse cystic presentation. CONCLUSION: This is the first report of a case of a cystic fibroadenoma with diffuse changes with multiple small cysts forming a spongy pattern.

Periductal stromal tumor of the breast in a young lady: A case report.

We present the case of a 30-year-old lady who underwent a core needle biopsy for a BIRADS 4 lesion of her left breast and was diagnosed as having a cellular fibroadenoma. The final diagnosis after excision returned to be a periductal stromal tumor (PST). An extremely rare entity, the PST is actually classified as a subtype of phyllodes tumor, with specific criteria that are only found upon histological examination of the specimen. Care should be taken when considering the diagnosis of fibroadenoma based only on clinical, radiological, and biopsy findings. Any nonclassical, clinical, or radiological findings should prompt a wide excision. Prognosis is generally good when clear margins are obtained and in the absence of malignant heterologous elements.

Evaluation of Elastographic Features in Complex Fibroadenomas With Radiologic-Pathologic Correlation.

Fibroadenoma is the most common benign tumor of the breast, and complex fibroadenoma (CFA) is one of its variants. Of the fibroadenomas, 22% are CFAs, and in women with CFAs, the malignancy development is found to be higher than in women with noncomplex fibroadenomas. Although there is an increased risk of malignancy with CFAs, the imaging findings of CFAs are fundamentally similar to those of other variants of fibroadenomas. In the literature, B-mode ultrasound features of CFAs were reported in detail. To our knowledge, there is no study that has specifically described the elastographic findings of CFAs. This article aims to illustrate the elastographic features of CFAs and to correlate radiologic and histopathologic findings of different cases.

The Role of Combined Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MRI for Differentiating Malignant From Benign Breast Lesions Presenting Washout Curve.

PURPOSE: The aim of this study is to evaluate the diagnostic performance of combined breast magnetic resonance imaging (MRI) protocol including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in patients with enhancing lesions that demonstrated washout curve and to determine whether applying apparent diffusion coefficient (ADC) cutoff value could improve the diagnostic value of breast MRI. METHODS: The retrospective study included 116 patients with 116 suspicious breast lesions, which showed washout curve on DCE-MRI, who underwent subsequent biopsy. Morphologic characteristics on DCE-MRI and ADC values on DWI were evaluated. Apparent diffusion coefficient values and morphologic features of benign and malignant lesions were compared. Diagnostic values of DCE-MRI and combined MRI, including DCE-MRI and DWI (applying an ADC cutoff value) for distinguishing malignancy from benign lesions, were calculated. RESULTS: Of the 116 breast lesions, 79 were malignant and 37 were benign. The ADC value of malignant tumors (median ADC, 0.72 × 10-3 mm2/s) was significantly lower than that of benign lesions (median ADC, 1.03 × 10-3 mm2/s; P < .000). The sensitivity and specificity of an ADC cutoff value of 0.89 × 10-3 mm2/s were 92% and 95%, respectively. Dynamic contrast-enhanced MRI alone presented 100% sensitivity and 59.4% specificity. Adding an ADC cutoff value of 0.89 × 10-3 mm2/s provided 100% sensitivity and 81% specificity, which would have prevented biopsy for 21.6% of benign lesions without missing any malignancies. CONCLUSION: Applying an ADC cutoff value to DCE-MRI provides an improvement in the diagnostic value of breast MRI for differentiating among lesions presenting washout curve.

Cellular fibroepithelial lesions diagnosed on core needle biopsy: Is there any role of clinical-sonography features helping to differentiate fibroadenomas and phyllodes tumor?

OBJECTIVE: The purpose of this study is to determine the role of clinico-sonographic features of breast cellular fibroepithelial lesions (CFELs) diagnosed on core needle biopsy (CNB) in the differentiation between fibroadenoma (FA) and phyllodes. MATERIALS AND METHODS: Results of consecutive women with a CNB showing CFEL from 2005 to 2010 were retrospectively reviewed. Clinical and sonographic findings were compared with surgical outcomes. Chi-square and Fisher's exact tests were used followed by a regression model for statistical analysis. RESULTS: A total of 131 women with 134 CFEL were included in the study; 89 (66%) were FAs and 45 (34%) were phyllodes (32 benign; 13 malignant). Significant predictors of increased risk of phyllodes tumor were patient age equal to or greater than 50 years (P = .021) and lesion size less than 2 cm at sonography (P = .043). No other imaging or clinical features were able to differentiate FA from phyllodes tumors. CONCLUSION: CFEL with a larger size in older women is associated with the surgical pathological result of phyllodes tumor and management should be tailored accordingly. Younger patients with small size nodules might be approached less aggressively, depending on a personalized discussion with the surgeons, taking into account the results obtained in this study.

Fibroepithelial Lesions (FELs) of the Breast: Is Routine Excision Always Necessary?

BACKGROUND: When needle core biopsies (NCBs) of the breast reveal fibroepithelial lesions (FELs), excision is often performed to rule out a phyllodes tumor (PT), despite low malignancy rates. Consequently, the natural history of observed FELs is not well described. We analyzed the malignancy risk in excised FELs and the natural history of FELs undergoing active surveillance (AS). METHODS: We retrospectively studied the pathology and imaging records of 215 patients with FELs (n = 252) diagnosed on NCB. Incidence of growth was determined by Kaplan-Meier method. RESULTS: Of 252 FELs, 80% were immediately excised and 20% underwent AS. Of the excised FELs, 198 (98%) were benign: fibroadenoma (FA) or benign breast tissue in 137 (68%), benign PT in 59 (29%), or LCIS in 2 (1%). Borderline PT or malignant lesions were found in 4 (2%). On ultrasound, malignant and borderline PTs were larger than benign lesions [median 3.9 vs 1.3 cm, p = 0.006]. Fifty FELs underwent AS, with a median follow-up of 17 (range 2-79) months. The majority remained stable or decreased in size: at 2 years, only 35% increased in volume by ≥ 50%. Of those tumors undergoing AS that were later excised (n = 4), all were benign. CONCLUSIONS: Almost all FELs (98%) were benign on surgical excision, and the majority undergoing AS remained stable, with benign pathology if later excised. Most FELs on NCB can be safely followed with US, with surgery reserved for patients with FELs that are large, symptomatic, or growing. This could spare most women with FELs unnecessary surgery.