Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.

Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer.

Characterization of Benign Breast Diseases and Association With Age, Hormonal Factors, and Family History of Breast Cancer Among Women in Sweden.

Importance: Benign breast diseases (BBDs) are common and associated with breast cancer risk, yet the etiology and risk of BBDs have not been extensively studied. Objective: To investigate the risk of BBDs by age, hormonal factors, and family history of breast cancer. Design, Setting, and Participants: This retrospective cohort study assessed 70 877 women from the population-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) who attended mammographic screening or underwent clinical mammography from January 1, 2011, to March 31, 2013, at 4 Swedish hospitals. Participants took part in a comprehensive questionnaire on recruitment. All participants had complete follow-up through high-quality Swedish national registers until December 31, 2015. Pathology medical records on breast biopsies were obtained for the participants, and BBD subtypes were classified according to the latest European guidelines. Analyses were conducted from January 1 to July 31, 2020. Exposures: Hormonal risk factors and family history of breast cancer. Main Outcomes and Measures: For each BBD subtype, incidence rates (events per 100 000 person-years) and multivariable Cox proportional hazards ratios (HRs) with time-varying covariates were estimated between the ages of 25 and 69 years. Results: A total of 61 617 women within the mammographic screening age of 40 to 69 years (median age, 53 years) at recruitment with available questionnaire data were included in the study. Incidence rates and risk estimates varied by age and BBD subtype. At premenopausal ages, nulliparity (compared with parity ≥3) was associated with reduced risk of epithelial proliferation without atypia (EP; HR, 0.62; 95% CI, 0.46-0.85) but increased risk of cysts (HR, 1.38; 95% CI, 1.03-1.85). Current and long (≥8 years) oral contraceptive use was associated with reduced premenopausal risk of fibroadenoma (HR, 0.65; 95% CI, 0.47-0.90), whereas hormone replacement therapy was associated with increased postmenopausal risks of epithelial proliferation with atypia (EPA; HR, 1.81; 95% CI, 1.07-3.07), fibrocystic changes (HR, 1.60; 95% CI, 1.03-2.48), and cysts (HR, 1.98; 95% CI, 1.40-2.81). Furthermore, predominantly at premenopausal ages, obesity was associated with reduced risk of several BBDs (eg, EPA: HR, 0.31; 95% CI, 0.17-0.56), whereas family history of breast cancer was associated with increased risk (eg, EPA: HR, 2.11; 95% CI, 1.48-3.00). Conclusions and Relevance: These results suggest that the risk of BBDs varies by subtype, hormonal factors, and family history of breast cancer and is influenced by age. Better understanding of BBDs is important to improve the understanding of benign and malignant breast diseases.

Point-of-care Ultrasound to Evaluate Breast Pathology in the Emergency Department.

INTRODUCTION: As physician-performed point-of-care ultrasound (POCUS) becomes more prevalent in the evaluation of patients presenting with various complaints in the emergency department (ED), one application that is significantly less used is breast ultrasound. This study evaluates the utility of POCUS for the assessment of patients with breast complaints who present to the ED and the impact of POCUS on medical decision-making and patient management in the ED. METHODS: This was a retrospective review of ED patients presenting with breast symptoms who received a POCUS examination. An ED POCUS database was reviewed for breast POCUS examinations. We then reviewed electronic health records for demographic characteristics, history, physical examination findings, ED course, additional imaging studies, and impact of the POCUS study on patient care and disposition. RESULTS: We included a total of 40 subjects (36 females, 4 males) in the final analysis. Most common presenting symptoms were breast pain (57.5%) and a palpable mass (37.5%). "Cobblestoning," ie, dense bumpy appearance, was the most common finding on breast POCUS, seen in 50% of the patients. Simple fluid collections were found in 37.5% of patients. CONCLUSION: Our study findings illustrate the utility of POCUS in the evaluation of a variety of breast complaints in the ED.

Is Breast Implant-Associated Anaplastic Large Cell Lymphoma Better Classified as a Lymphoproliferative Disorder and How Surgeons Reduce Risk?

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a complex topic with evolving classification and etiology. Commonalities between BIA-ALCL and lymphoproliferative disorders exist, suggesting that BIA-ALCL may be better represented on a spectrum of disease from benign effusion to malignant metastatic lymphoma. Meticulous sterile surgical technique, involving the use of betadine-containing irrigation, should be used to decrease the biological burden introduced into the surgical field and possibly prevent future incidences of BIA-ALCL.

Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.

BACKGROUND: Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. OBJECTIVES: The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. METHODS: The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. RESULTS: Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. CONCLUSION: Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.

To assist radiologists in breast cancer classification in automated breast ultrasound (ABUS) imaging, we propose a computer-aided diagnosis based on a convolutional neural network (CNN) that classifies breast lesions as benign and malignant. The proposed CNN adopts a modified Inception-v3 architecture to provide efficient feature extraction in ABUS imaging. Because the ABUS images can be visualized in transverse and coronal views, the proposed CNN provides an efficient way to extract multiview features from both views. The proposed CNN was trained and evaluated on 316 breast lesions (135 malignant and 181 benign). An observer performance test was conducted to compare five human reviewers' diagnostic performance before and after referring to the predicting outcomes of the proposed CNN. Our method achieved an area under the curve (AUC) value of 0.9468 with five-folder cross-validation, for which the sensitivity and specificity were 0.886 and 0.876, respectively. Compared with conventional machine learning-based feature extraction schemes, particularly principal component analysis (PCA) and histogram of oriented gradients (HOG), our method achieved a significant improvement in classification performance. The proposed CNN achieved a >10% increased AUC value compared with PCA and HOG. During the observer performance test, the diagnostic results of all human reviewers had increased AUC values and sensitivities after referring to the classification results of the proposed CNN, and four of the five human reviewers' AUCs were significantly improved. The proposed CNN employing a multiview strategy showed promise for the diagnosis of breast cancer, and could be used as a second reviewer for increasing diagnostic reliability.

Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images.

BACKGROUND: The purpose of this work was to evaluate computable Breast Imaging Reporting and Data System (BI-RADS) radiomic features to classify breast masses on ultrasound B-mode images. METHODS: The database consisted of 206 consecutive lesions (144 benign and 62 malignant) proved by percutaneous biopsy in a prospective study approved by the local ethical committee. A radiologist manually delineated the contour of the lesions on greyscale images. We extracted the main ten radiomic features based on the BI-RADS lexicon and classified the lesions as benign or malignant using a bottom-up approach for five machine learning (ML) methods: multilayer perceptron (MLP), decision tree (DT), linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). We performed a 10-fold cross validation for training and testing of all classifiers. Receiver operating characteristic (ROC) analysis was used for providing the area under the curve with 95% confidence intervals (CI). RESULTS: The classifier with the highest AUC at ROC analysis was SVM (AUC = 0.840, 95% CI 0.6667-0.9762), with 71.4% sensitivity (95% CI 0.6479-0.8616) and 76.9% specificity (95% CI 0.6148-0.8228). The best AUC for each method was 0.744 (95% CI 0.677-0.774) for DT, 0.818 (95% CI 0.6667-0.9444) for LDA, 0.811 (95% CI 0.710-0.892) for RF, and 0.806 (95% CI 0.677-0.839) for MLP. Lesion margin and orientation were the optimal features for all the machine learning methods. CONCLUSIONS: ML can aid the distinction between benign and malignant breast lesion on ultrasound images using quantified BI-RADS descriptors. SVM provided the highest ROC-AUC (0.840).

[Value of shear wave elastrography image classification in the diagnosis of breast masses].

Objective: To analyze the image features of shear wave elastrography (SWE) in breast masses, and to evaluate their values in the differentiation of benign and malignant breast lesions. Methods: A total of 361 patients with 403 breast lesions who simultaneously underwent conventional ultrasound and SWE examination from February 2015 to January 2018 were selected. Diagnosis in all patients was confirmed by aspiration biopsy or operative pathology. The SWE images were collected and the elastic images were divided into 5 types. The SWE image features of different breast pathological types were summarized, and their values in benign and malignant breast lesion diagnoses were evaluated. Results: The main features of benign breast lesion were type Ⅰ and Ⅱ, the main features of the malignant lesion were type Ⅳ and Ⅴ, and the proportion of which were 43.6% (71/163), 37.4% (61/163), 22.1% (53/240) and 57.9% (139/240), respectively. Type Ⅲ accounted for a certain proportion in both benign and malignant lesions. The SWE image features of benign and malignant lesions were compared and a significant difference was observed (P<0.001). The type Ⅴ features were mainly observed in invasive ductal carcinoma, invasive lobular carcinoma and other types of invasive carcinoma, while the type Ⅳ features were mostly presented in ductal carcinoma in situ and mucinous carcinoma. Fibroadenoma, fibroadenosis accompanied with fibroadenoma, and fibroadenosis were featured with type Ⅰ. Both intraductal papilloma and benign phyllodes tumor were mostly type Ⅱ, while type Ⅲ and Ⅴ were more common in chronic granulomatous mastitis. When type Ⅰ and typeⅡof breast lesions were classified as benign features while type Ⅳ and Ⅴ were malignant features, the sensitivity and specificity of breast malignant lesion diagnosis were 91.2% and 84.7% by application of SWE combined with breast imaging reporting and data system (BI-RADS). The sensitivity of combined diagnosis was slightly lower than that of conventional ultrasound (P>0.05), but the specificity was significantly higher than conventional ultrasound (P<0.01). Conclusion: The SWE is a simple and effective method. Combination of SWE with conventional ultrasound may improve the diagnostic differentiation of benign and malignant breast lesions.

Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features.

BACKGROUND: Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose (18F-FDG)-PET. METHODS: The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used. RESULTS: In 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. 18F-FDG-PET and morphologic features were less predictive. CONCLUSION: Our CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions.

Comparison of synthetic and digital mammography with digital breast tomosynthesis or alone for the detection and classification of microcalcifications.

OBJECTIVE: To compare the performance of synthetic mammography (SM) and digital mammography (DM) with digital breast tomosynthesis (DBT) or alone for the evaluation of microcalcifications. METHODS: This retrospective study includes 198 mammography cases, all with DM, SM, and DBT images, from January to October 2013. Three radiologists interpreted images and recorded the presence of microcalcifications and their conspicuity scores and final BI-RADS categories (1, 2, 3, 4a, 4b, 4c, 5). Readers' area under the ROC curves (AUCs) were analyzed for SM plus DBT vs. DM plus DBT and SM alone vs. DM alone using the BI-RADS categories for the overall group and dense breast subgroup. RESULTS: Conspicuity scores of detected microcalcifications were neither significantly different between SM and DM with DBT nor alone (p>0.05). In predicting malignancy of detected microcalcifications, no significant difference was found between readers' AUCs for SM and DM with DBT or alone in the overall group or dense breast subgroup (p>0.05). CONCLUSIONS: Diagnostic performances of SM and DM for the evaluation of microcalcifications are not significantly different, whether performed with DBT or alone. KEY POINTS: • In DBT-imaging, SM and DM show comparable performances when evaluating microcalcifications. • For BI-RADS classification of microcalcifications, SM and DM show similar AUCs. • DBT with SM may be sufficient for diagnosing microcalcifications, without DM.

Skin diseases of the breast and nipple: Inflammatory and infectious diseases.

Certain dermatologic conditions are unique to the breast and nipple, whereas others may incidentally involve these structures. All require a nuanced approach to diagnosis and treatment because of the functional, sexual, and aesthetic importance of this area. The lactating patient requires special management because certain treatment options are contraindicated. All dermatologic conditions involving the breast and nipple require careful evaluation because malignancy of the breast can be mistaken for a benign condition or may trigger the development of certain dermatologic conditions. The second article in this continuing medical education series reviews common and uncommon inflammatory and infectious conditions of the breast and nipple and provides insight into both the diagnosis and the treatment of this heterogeneous group of diseases. For the purposes of this article, these conditions are divided into 4 distinct categories: 1) dermatitis; 2) radiation-induced changes; 3) mastitis; and 4) miscellaneous dermatologic conditions of the breast and nipple.

Skin diseases of the breast and nipple: Benign and malignant tumors.

The evaluation and management of dermatologic diseases of the breast and nipple requires an understanding of the unique anatomy of the breast and nipple and an awareness of the significant emotional, cultural, and sexual considerations that may come into play when treating this anatomic area. The first article in this continuing medical education series reviews breast anatomy, congenital breast anomalies, and benign and malignant breast tumors. An emphasis is placed on inflammatory breast cancer and breast cancer with noninflammatory skin involvement and on cutaneous metastases to the breast and from breast cancer. Familiarity of the dermatologist with the cutaneous manifestations of breast cancer will facilitate the diagnosis of breast malignancy and assist with staging, prognostication, and evaluation for recurrence. This article also discusses genodermatoses that predispose to breast pathology and provides imaging recommendations for evaluating a palpable breast mass.

How Do We Approach Benign Proliferative Lesions?

PURPOSE OF REVIEW: The aim of this review is to summarize recently published literature addressing atypical ductal hyperplasia (ADH), lobular neoplasia (atypical lobular hyperplasia [ALH] and classic lobular carcinoma in situ [C-LCIS]), non-classic lobular carcinoma in situ (NC-LCIS), papillary lesions, and flat epithelial atypia (FEA). RECENT FINDINGS: While ADH, ALN, and C-LCIS are well-established markers of an increased risk of future breast cancers, the risk implications are less clear for papillary lesions and FEA. NC-LCIS is the least well-characterized lesion, with scant published literature on its natural history and surgical management when encountered on needle biopsy. Recent data suggest that lobular neoplasia on core biopsy of a BI-RADS ≤ 4 concordant lesion does not require an excision, while ADH, atypical papillomas, and NC-LCIS should be excised. Evidence on FEA and papillomas without atypia suggests a low risk of upgrade on excision, and prospective studies on the upgrade of these lesions are ongoing.

Tuberous Breast: Past, Present, and Future: Personal Classification, Treatment, and Surgical Outcomes.

BACKGROUND: Tuberous breast (TB) is a rare congenital deformity, which may appear in different clinical forms representing various degrees of a single pathological entity. The worst cases are characterized by severe hypoplasia. Following a detailed analysis of the available relevant literature and a significant number of treated cases, in this article, the authors propose a new classification, with the aim of summarizing and simplifying a more intuitive categorization of the malformation, considering all the clinical aspects and including all types of TBs, even the minor ones, thus allowing a more immediate diagnosis and surgical planning. METHODS: Between September 2006 and December 2015, 78 patients with TBs underwent surgical procedures to correct the deformity. The patients' mean age was 18.6 years, ranging between 17 and 26 years. There being 11 monolateral deformities, the treated TBs amounted to 145. A periareolar approach, adipo-glandular flaps, and dual plane breast implant placements were performed. Postoperative follow-up include photos collected 12 months after operation. The authors present a personal classification including all the forms of the deformity, plus the minor forms based on the following 2 principal categories: hypoplastic and normoplastic TBs, taking into account all the clinical aspects of the malformation including the morphology and the consistency of the breast. CONCLUSIONS: Preoperative identification of the type of the deformity is essential to obtain satisfactory results and a complete and intuitive classification including all the possible variants of the deformity, even the minor forms, and fundamental in diagnosing and resolving the problem. In this article, the authors propose a personal classification and surgical procedure to resolve the malformation.

Mammographic classification of breast lesions amongst women in Enugu, South East Nigeria.

OBJECTIVES: The study was to classify lesions identified on mammograms using Breast Imaging Reporting and Data System (BIRADS) grading method. This was in view of ascertaining the rate of occurrence of breast malignancy of the studied population. METHODS: A retrospective cohort study of 416 mammographic reports of women was undertaken. The reports were written by consultant radiologists of 10 years' experience and above. The reports were evaluated and characterised using Breast Imaging Reporting and Data system (BIRADS). Demographic data of patients were sourced from the request cards. The data was entered into a proforma and analysed using SPSS version 17. All request cards with incomplete data were excluded from the study. RESULTS: Using the BI-RADS Classification, the mammographic reports shows that 29.57% of the lesions were benign, and 4.57% were suspicious and biopsy recommended, while 3.60% were highly suggestive of malignancy. The right breast was predominantly affected with 42.7% of the patients (P<0.05). CONCLUSION: Classification of breast lesion using BI-RADS grading system is a veritable tool in the diagnosis of the breast lesion. The present study shows that 3.6% of the population has a high index of malignancy.

Benign Breast Diseases: Evaluation and Management.

Benign breast disease is a spectrum of common disorders. The majority of patients with a clinical breast lesion will have benign process. Management involves symptom control when present, pathologic-based and imaging-based evaluation to distinguish from a malignant process, and counseling for patients that have an increased breast cancer risk due to the benign disorder.

Positive predictive value of breast lesions of uncertain malignant potential (B3): Can we identify high risk patients? The value of a multidisciplinary team and implications in the surgical treatment.

BACKGROUND AND AIM: The use of conventional needle core biopsy for palpable masses and vacuum-assisted needle core biopsy for microcalcifications has significantly increased the preoperative diagnosis rate, but the strategy for those patients with lesions of uncertain malignant potential (B3) still remains controversial. The aim of this study was to evaluate the positive predictive value (PPV) of the malignancy of B3 lesions in order to establish their correct management in the setting of a multidisciplinary care pathway. METHODS: Data from all patients who had a Needle Core Biopsy (NCB) or a Vacuum-Assisted Needle Core Biopsy (VANCB) between 2005 and 2014 were retrospectively collected and analyzed. The B3 patients were discussed by the Multidisciplinary Team (MDT) deciding for surgery or for follow-up, based on a score in which clinical-instrumental factors and environmental factors were considered. The PPV of malignancy of all surgically excised B3 lesions was calculated. RESULTS: One hundred and seventy-eight B3 NCBs were included in the study and Atypical Epithelial Proliferation of Ductal Type (AEDPT) was the most represented subcategory. The final histopathology report of the 128 patients operated on showed 94 benign and 34 malignant lesions. The PPV of B3 patients referred to surgery was 26.5%. CONCLUSION: B3 patients should be evaluated by a breast MDT in order to make the right therapeutic decision, in particular for patients with contrasting clinical/diagnostic findings. Larger prospective studies are required to assess the definitive PPV of each B3 subcategory.

Calcificaciones mamarias: descripción y clasificación según la 5ª edición BI-RADS / Breast calcifications: description and classification according to BI-RADS 5th edition

Abstract. Breast calcifications are frequent findings in mammography. Most of them have a benign origin, such as in the case of the response to inflammatory disease of the ducts or coarse calcifications in benign nodules. Many of these calcifications show a characteristic benign appearance, and they do not need to be magnified or monitored. However, other calcifications can show a grouped pattern, have a suspicious appearance, and transform into an in situ ductal carcinoma or a high risk breast lesion. It is important to know the morphological and distribution patterns of these calcifications in order to make right decisions for each case. In the 5th edition of the BI-RADS atlas, 2013, categories and levels of suspicion for some patterns were modified. The objective of this article is to update descriptors and categories of BI-RADS micro-calcifications, pointing out their most important features and malignancy risk linked to each descriptor.

Resumen. Las calcificaciones mamarias son un hallazgo frecuente en mamografía. La mayoría de ellas tienen un origen benigno, como puede ser la respuesta a patología inflamatoria de los conductos o calcificaciones gruesas en nódulos benignos. Muchas de estas calcificaciones presentan un aspecto benigno característico y no requieren ser magnificadas o controladas. Otras calcificaciones sin embargo pueden presentarse agrupadas, tener un aspecto sospechoso y originarse en un carcinoma ductal in situ o una lesión de alto riesgo. Es relevante conocer los patrones morfológicos y de distribución de estas calcificaciones a fin de tomar la conducta adecuada para cada caso. En la 5.ª edición del atlas BI-RADS, 2013, las categorías y grados de sospecha de algunos patrones fueron modificados. El objetivo del presente artículo es realizar una actualización de los descriptores y las categorías BI-RADS de las microcalcificaciones, señalando sus características más importantes y el riesgo de malignidad asociado a cada descriptor.

National survey of B1 and B2 reporting of breast needle core biopsies.

AIM: This survey investigated the variation in the use of the breast core biopsy categories B1 normal and B2 benign. METHOD: A survey with case scenarios was circulated to 701 breast pathologists in the UK. RESULTS: The response rate was 40%. If there was concordance between the radiological and histological findings, then there was a clear consensus on the appropriate B category. However, if there was discordance between the radiological and histological findings, then frequently there was poor agreement on the appropriate category. Analysis of these cases and supplementary questions on the criteria used to make a pathological categorisation showed that some pathologists are influenced by the radiological features or by the multidisciplinary discussion, rather than just using the histological features. CONCLUSIONS: This survey shows that pathologists frequently do not follow the National Health Service breast screening guideline that B categories should be based solely on the histological changes.

[Benign proliferative breast disease with and without atypia]. / Prise en charge des proliférations épithéliales du sein avec et sans atypies : hyperplasie canalaire atypique, métaplasie cylindrique avec atypie, néoplasies lobulaires, proliférations épithéliales sans atypie, mastopathie fibrokystique, adénose, cicatrices radiaires, mucocèles, lésions prolifératives apocrines : recommandations pour la pratique clinique.

In the last few years, diagnostics of high-risk breast lesions (atypical ductal hyperplasia [ADH], flat epithelial atypia [FEA], lobular neoplasia: atypical lobular hyperplasia [ALH], lobular carcinoma in situ [LCIS], radial scar [RS], usual ductal hyperplasia [UDH], adenosis, sclerosing adenosis [SA], papillary breast lesions, mucocele-like lesion [MLL]) have increased with the growing number of breast percutaneous biopsies. The management of these lesions is highly conditioned by the enlarged risk of breast cancer combined with either an increased probability of finding cancer after surgery, either a possible malignant transformation (in situ or invasive cancer), or an increased probability of developing cancer on the long range. An overview of the literature reports grade C recommendations concerning the management and follow-up of these lesions: in case of ADH, FEA, ALH, LCIS, RS, MLL with atypia, diagnosed on percutaneous biopsies: surgical excision is recommended; in case of a diagnostic based on vacuum-assisted core biopsy with complete disappearance of radiological signal for FEA or RS without atypia: surgical abstention is a valid alternative approved by multidisciplinary meeting. In case of ALH (incidental finding) associated with benign lesion responsible of radiological signal: abstention may be proposed; in case of UDH, adenosis, MLL without atypia, diagnosed on percutaneous biopsies: the concordance of radiology and histopathology findings must be ensured. No data is available to recommend surgery; in case of non-in sano resection for ADH, FEA, ALH, LCIS (except pleomorphic type), RS, MLL: surgery does not seem to be necessary; in case of previous ADH, ALH, LCIS: a specific follow-up is recommended in accordance with HAS's recommendations. In case of FEA and RS or MLL combined with atypia, little data are yet available to differ the management from others lesions with atypia; in case of UDH, usual sclerosing adenosis, RS without atypia, fibro cystic disease: no specific follow-up is recommended in agreement with HAS's recommendations.