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.

Reduction mammoplasty and mastopexy for multiple bilateral fibroadenomas: a case report and literature review.

Treatment of multiple benign breast nodules is sometimes challenging with respect to establishing a surgical plan that achieves both therapeutic and cosmetic goals. Successful application of oncoplastic techniques has been reported in selected cases of benign breast lesions. In this case report, we present the surgical treatment and outcome of a patient with multiple fibroadenomas in ptotic and voluminous breasts. A combined procedure of extensive glandular resection and reduction mammoplasty using a modified vertical pedicle technique was performed on this patient, who desired complete lesion removal, volume reduction, and mastopexy. The cosmetic result was satisfactory at both the short- and mid-term follow-up. In addition, different techniques applied in the treatment of breast fibroadenoma are herein reviewed and discussed.

Whole-tumor histogram analysis of multiparametric breast magnetic resonance imaging to differentiate pure mucinous breast carcinomas from fibroadenomas with high-signal intensity on T2WI.

PURPOSE: To investigate the utility of whole-tumor histogram analysis based on multiparametric MRI in distinguishing pure mucinous breast carcinomas (PMBCs) from fibroadenomas (FAs) with strong high-signal intensity on T2-weighted imaging (T2-SHi). MATERIAL AND METHODS: The study included 20 patients (mean age, 55.80 ± 15.54 years) with single PBMCs and 29 patients (mean age, 42.31 ± 13.91 years) with single FAs exhibiting T2-SHi. A radiologist performed whole-tumor histogram analysis between PBMC and FA groups with T2-SHi using multiparametric MRI, including T2-weighted imaging (T2WI), diffusion weighted imaging (DWI) with apparent diffusion coefficient (ADC) maps, and the first (DCE_T1) and last (DCE_T4) phases of T1-weighted dynamic contrast-enhanced imaging (DCE) images, to extract 11 whole-tumor histogram parameters. Histogram parameters were compared between the two groups to identify significant variables using univariate analyses, and their diagnostic performance was assessed by receiver operating characteristic (ROC) curve analysis and logistic regression analyses. In addition, 15 breast lesions were randomly selected and histogram analysis was repeated by another radiologist to assess the intraclass correlation coefficient for each histogram feature. Pearson's correlation coefficients were used to analyze the correlations between histogram parameters and Ki-67 expression of PMBCs. RESULTS: For T2WI images, mean, median, maximum, 90th percentile, variance, uniformity, and entropy significantly differed in PBMCs and FAs with T2-SHi (all P < 0.05), yielding a combined area under the curve (AUC) of 0.927. For ADC maps, entropy was significantly lower in FAs with T2-SHi than in PMBCs (P = 0.03). In both DCE_T1 and DCE_T4 sequences, FAs with T2-SHi showed significantly higher minimum values than PBMCs (P = 0.007 and 0.02, respectively). The highest AUC value of 0.956 (sensitivity, 0.862; specificity, 0.944; positive predictive value, 0.962; negative predictive value, 0.810) was obtained when all significant histogram parameters were combined. CONCLUSIONS: Whole-tumor histogram analysis using multiparametric MRI is valuable for differentiating PBMCs from FAs with T2-SHi.

68 Ga-PSMA-11 Uptake in a Large Fibroadenoma in a Male Axillary Accessory Breast.

ABSTRACT: Prostate-specific membrane antigen PET imaging has revolutionized the role of prostate cancer diagnosis and management, with very high sensitivity and specificity. To prevent misdiagnosis and for accurate therapy planning, prostate-specific membrane antigen (PSMA) uptake in nonprostatic diseases needs to be recognized correctly. Besides the physiological PSMA expression, 68 Ga-PSMA-11 uptake has been mentioned in multiple oncological and nononcological lesions. The present case report exhibits 68 Ga-PSMA-11 uptake in fibroadenoma in a male accessory breast in the right axillary region.

Tubular Adenoma of the Breast: Radiologic-Pathologic Correlation.

Breast tubular adenomas (TAs) are rare, benign glandular epithelial tumors that arise from a proliferation of acini in the terminal duct lobular units. In the literature, 40 TA cases have previously been reported, and we describe 5 additional cases in this article. In the small number of reported cases, TAs present most often in women of reproductive age but may also occur in postmenopausal women. Mammographically and sonographically, TAs are almost indistinguishable from fibroadenomas (FAs), and they typically present on US as hypoechoic, oval, circumscribed, parallel masses with variable internal vascularity. TAs can also be seen on mammography as oval masses with microlobulated margins, or as grouped coarse, heterogeneous microcalcifications with or without associated mass or asymmetry. On MRI, TAs present as heterogeneously enhancing, T2-hyperintense oval masses with persistent kinetics. Histopathologically, TAs consist of closely packed round tubules with minimal stroma, in distinction to FAs, which have a prominent stromal component that surrounds and can distort the associated tubules. Because of their benign classification and excellent prognosis, patients with biopsy-confirmed TAs may resume routine screening. Complete surgical excision may be considered for cosmetic purposes or for TAs exhibiting associated suspicious calcifications or rapid growth.

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.

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.

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 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.

Bilateral Multiple Giant Juvenile Fibroadenomas in an Adolescent Girl.

Giant juvenile fibroadenomas are usually seen as rare variants of fibroadenomas in adolescents as unilateral solitary masses that may be managed by surgical excision with conservation of normal breast tissue. We report a case of a premenarchal 13-year-old female presenting with bilateral multifocal giant juvenile fibroadenomas requiring essentially bilateral subtotal nipple sparing mastectomies. Surgical evaluation revealed replacement of normal breast tissue on the right side. She then had development of two additional right-sided fibroadenomas requiring excision.

Robotic system for magnetic resonance imaging-guided high-intensity focus ultrasound application: Feasibility of breast fibroadenoma treatment.

PURPOSE: This paper presents a high-intensity focus ultrasound (HIFU) robotic system for treating breast fibroadenoma under the guidance of magnetic resonance imaging (MRI). Based on the thermal and mechanical effects of ultrasound, the system aims to deliver ultrasound energy to a target precisely without damaging the normal tissue. The temperature elevation can be monitored in real time by MRI, and the treatment plan can be adjusted during the procedure. The requirements, design specifications, control system and registration of the robotic system are specified. METHODS: The robotic system was designed with a 3 degrees of freedom manipulator with limit switches and encoders, a customised MRI-compatible breast coil, a water bladder with sets of breast-conforming brackets, and a probe capable of generating ultrasound. Twenty volunteers were recruited for this study, and their data were analysed to provide more precise data for the design. The accuracy of the robot was evaluated in free space using a coordinate measuring machine, phantom and ex vivo porcine tissue in MRI room. The study also verified the signal-to-noise ratio (SNR) of the MRI with the effect of the robotic system. RESULTS: The research findings revealed that the manipulator exhibited a translational precision of 0.10 ± 0.14 mm, a rotational fidelity around the X direction of 0.11 ± 0.09°, and an oscillatory exactness around the Y direction of 0.10 ± 0.08°. The investigation of positioning accuracy demonstrated that the robot's error in free space was 0.26 ± 0.07 mm. When subjected to MRI room with agar-silica phantom and ex vivo porcine tissue, the positioning accuracy amounted to 1.11 ± 0.47 mm and 1.57 ± 0.52 mm. In the presence of the robotic system, the SNR of the MRI experienced a 4.2% reduction, which had a negligible impact on image quality. CONCLUSIONS: The conducted experiments validate the efficacy of the proposed MRI-guided HIFU robotic system in executing agar-silica phantom and ex vivo porcine tissue investigations with adequate positioning accuracy. Consequently, this system exhibits certain feasibility for the treatment of breast fibroadenomas.

Use of shear-wave elastography to distinguish complex and complicated fibroadenomas from simple fibroadenomas.

PURPOSE: Simple fibroadenomas (SFAs), complex fibroadenomas (CFAs), and cellular fibroadenomas (CeFAs) are variants of fibroadenomas. Additionally, some degenerative, hyperplastic, and metaplastic changes may occur in fibroadenomas, forming complicated fibroadenomas. Distinctive ultrasonography (US) features in variants of fibroadenomas and complicated fibroadenomas have not been reported. Shear-wave elastography (SWE) can be applied to effectively discriminate between these variants and complicated fibroadenomas. In this study, we aimed to evaluate SWE findings to discriminate between SFAs and other variants. METHODS: In total, 48 patients (26 with SFAs, 16 with CFAs, 3 with CeFAs, and 3 with complicated fibroadenomas) participated in this study. The lesions were classified into two groups according to histopathologic diagnoses. The SWE evaluation and lesion elasticity scores (Emax, Emean, and Emin) were both assessed in m/s and k/Pa, respectively. Two observers measured Emax, Emean, and Emin. Brightness (B)-mode US findings based on the Breast Imaging Reporting and Data System categorization and elasticity scores were recorded. In the statistical analyses, the chi-square test and non-parametric tests were performed. Fisher's exact test was used to compare independent groups, and Spearman's correlation coefficients were used to correlate the SWE data between the two observers. Additionally, receiver operating characteristic curves were analyzed to evaluate the diagnostic performance of the elasticity values. RESULTS: The B-mode US features in both groups showed no statistical significance. The set of SWE values of both observers demonstrated strong statistical significance in discriminating between group 1 (SFAs) and Group 2 (CFAs, CeFAs, and complicated fibroadenomas). CONCLUSION: As the fibroadenoma variants and complicated fibroadenomas have similar US findings, SWE in addition to a conventional B-mode examination can increase the diagnostic performance to discriminate SFAs from other complex and complicated forms of fibroadenomas.

Complex Fibroadenoma Mimicking Breast Cancer on 68Ga-FAPI-04 and 18F-FDG PET/CT.

ABSTRACT: A 47-year-old woman had complaint of palpable swelling in the left breast for the last 3 weeks. 18F-FDG PET/CT for staging of concomitant invasive carcinoma in the right breast and 68Ga-labeled fibroblast activation protein inhibitor 04 PET/CT imaging in an ongoing study were performed. Increased radiopharmaceutical uptake was observed in both PET/CT imaging in complex fibroadenoma located in the left breast.

Differential Diagnosis of DCIS and Fibroadenoma Based on Ultrasound Images: a Difference-Based Self-Supervised Approach.

Differentiation of ductal carcinoma in situ (DCIS, a precancerous lesion of the breast) from fibroadenoma (FA) using ultrasonography is significant for the early prevention of malignant breast tumors. Radiomics-based artificial intelligence (AI) can provide additional diagnostic information but usually requires extensive labeling efforts by clinicians with specialized knowledge. This study aims to investigate the feasibility of differentially diagnosing DCIS and FA using ultrasound radiomics-based AI techniques and further explore a novel approach that can reduce labeling efforts without sacrificing diagnostic performance. We included 461 DCIS and 651 FA patients, of whom 139 DCIS and 181 FA patients constituted a prospective test cohort. First, various feature engineering-based machine learning (FEML) and deep learning (DL) approaches were developed. Then, we designed a difference-based self-supervised (DSS) learning approach that only required FA samples to participate in training. The DSS approach consists of three steps: (1) pretraining a Bootstrap Your Own Latent (BYOL) model using FA images, (2) reconstructing images using the encoder and decoder of the pretrained model, and (3) distinguishing DCIS from FA based on the differences between the original and reconstructed images. The experimental results showed that the trained FEML and DL models achieved the highest AUC of 0.7935 (95% confidence interval, 0.7900-0.7969) on the prospective test cohort, indicating that the developed models are effective for assisting in differentiating DCIS from FA based on ultrasound images. Furthermore, the DSS model achieved an AUC of 0.8172 (95% confidence interval, 0.8124-0.8219), indicating that our model outperforms the conventional radiomics-based AI models and is more competitive.

Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.

PURPOSE: To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category 4 (BI-RADS 4) lesions and to evaluate whether the combined diagnosis of these models could improve the diagnostic performance of radiologists. METHODS: A total of 319 female patients with 319 pathologically confirmed BI-RADS 4 lesions were randomly divided into training, validation, and testing sets in this retrospective study. The three models were established based on contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and T2-weighted imaging using the training and validation sets. The artificial intelligence (AI) combination score was calculated according to the results of three models. The diagnostic performances of four radiologists with and without AI assistance were compared with the AI combination score on the testing set. The area under the curve (AUC), sensitivity, specificity, accuracy, and weighted kappa value were calculated to assess the performance. RESULTS: The AI combination score yielded an excellent performance (AUC = 0.944) on the testing set. With AI assistance, the AUC for the diagnosis of junior radiologist 1 (JR1) increased from 0.833 to 0.885, and that for JR2 increased from 0.823 to 0.876. The AUCs of senior radiologist 1 (SR1) and SR2 slightly increased from 0.901 and 0.950 to 0.925 and 0.975 after AI assistance, respectively. CONCLUSION: Combined diagnosis of multiparametric MRI-based deep learning models to differentiate TNBC from fibroadenoma magnetic resonance BI-RADS 4 lesions can achieve comparable performance to that of SRs and improve the diagnostic performance of JRs.

Why is the depth/width ratio of a typical fibroadenoma small in breast ultrasonography?

PURPOSE: Typical myxomatous fibroadenomas have a small depth/width (D/W) ratio on ultrasonography. The small D/W ratio of fibroadenomas is speculated to be caused by the softness of the mass and its orientation along the longitudinal aspect of the ductal elements without adhesion to the surrounding tissue; however, this has not been clearly proven. This study aimed to confirm the reason why fibroadenomas present with a small D/W ratio on ultrasonography. METHODS: We retrospectively analyzed imaging data from 17 patients who were diagnosed with typical fibroadenomas on ultrasonography and who underwent magnetic resonance imaging (MRI) at our hospital. RESULTS: The median D/W ratio obtained from ultrasonography images was 0.48 (0.32-0.67), while that obtained from MRI was 1.38 (0.62-1.68). The D/W ratios calculated from MRI were significantly greater than those calculated from ultrasonography images (p < 0.001). The D/W ratio obtained using ultrasonography was not greater than the D/W ratio obtained using MRI in any of the cases. CONCLUSION: This study revealed that the small D/W ratio of fibroadenomas on ultrasonography may be attributable to the horizontal force acting on the breast against the chest wall in the supine position, the elasticity of the fibroadenoma, and the lack of adhesion between the mass and surrounding tissue.

Fine-needle aspiration of intramammary lymph nodes: a clinical, radiographic, and cytologic review.

INTRODUCTION: Recognizing and sampling intramammary lymph nodes (IMLNs) is important in the clinical management of patients with breast carcinomas. We undertook a retrospective study to evaluate the clinical utility of fine-needle aspiration (FNA) in assessing IMLNs. MATERIALS AND METHODS: Our pathology database was searched for all IMLN FNA cytology cases from January 2005 to December 2021. The cytologic findings, radiographic features, and clinical data were reviewed. RESULTS: A total of 149 cases were identified. Eighteen of 149 (12%) patients had synchronous breast tumors, including 13 invasive ductal carcinomas (IDCs), 1 ductal carcinoma in situ (DCIS), and 4 fibroadenomas. Among patients with synchronous IDCs, FNA of IMLNs was positive for metastatic carcinoma in 4 of 13 (30.7%) cases. The 4 patients with positive IMLNs all received mastectomies. Fifteen of 149 (10.7%) patients had a prior history of breast tumors, including 9 IDCs, 4 DCISs, 1 lobular carcinoma in situ (LCIS), and 1 fibroadenoma. Two of 149 (1.3%) patients had a prior history of lymphoma. In the patients with prior history of IDC, DCIS, LCIS, lymphomas and fibroadenomas, IMLN FNAs were all negative for malignancy. Two of 149 cases (1.3%) showed granulomatous lymphadenitis. The remaining 112 cases had negative IMLN FNAs and no significant clinical or pathological findings. CONCLUSIONS: Our study showed that IMLNs are commonly associated with synchronous/metachronous breast tumors (33 of 149, 22.1%). The incidence of positive IMLN FNA in patients with synchronous invasive breast carcinoma was 30.7% (4 of 13). FNA of IMLNs in conjunction with clinical presentation and radiologic findings allows triage of patients for appropriate clinical management and avoids additional unnecessary surgical procedures.

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.