Primary breast lymphoma is a rare disease and accounts for 0.5% of cases of breast cancer. Most primary breast lymphomas develop from B cells, and the involvement of T cells is rare. Anaplastic large cell lymphoma (ALCL) is a recently discovered T-cell lymphoma associated with breast implants. Only a few cases have been reported to date. It is believed that the incidence of ALCL is increasing because of the increasing number of breast implants. The clinical presentation is variable and can manifest as a palpable mass in the breast or armpit, breast pain, or capsular contracture. Because of the rarity of the disease and the lack of knowledge to date, clinical diagnosis is often delayed, with consequent delays in treatment. The cause and pathogenesis have not been fully elucidated, and there are no evidence-based guidelines for diagnosis, treatment, or follow-up of this disease. We present a review of cases of patients with silicone breast implants, including ALCL, a rare type of breast cancer that is still under study, and silicone-induced granuloma of breast implant capsule and its differential diagnosis, and discuss if a silicone-induced granuloma of breast implant capsule could be the precursor of the disease.

The imporTance of breasT elasTography added To The bi-rads® ( Conflicts of interest: noneObjective: the aim of this study was to investigate the addition of elastography to the BI-RADS ® lexicon for the classification of breast lesions. Methods: a total of 955 consecutive patients who were subjected to breast percutaneous biopsy from January 2010 to December 2012 were retrospectively assessed. Overall, 26 patients who did not present with masses on conventional ultrasound were excluded. The patients were classified according to the fifth edition of the breast imaging and reporting data system (BI-RADS ® ) lexicon, which includes elastographic findings. The BI-RADS ® classification is based on the same classification principles that have been suggested by the author, which classify lesions as soft, intermediate, or hard. Results: the addition of elastographic findings to the BI-RADS ® lexicon improved the sensitivity (S), specificity (SP), and diagnostic accuracy (DA) of ultrasound in the assessment of breast lesions, which increased from 93.85, 72.07, and 76.64 to 95.90, 80.65, and 91.39%, respectively. Conclusion: these findings suggest that the addition of elastography to the BI-RADS ® lexicon will improve the SP and DA of ultrasound in the screening of breast lesions.

Currently, attention has been given to complications related to breast implants, especially due to the presence of anaplastic large cell lymphoma (ALCL) related to silicone implants. Many manuscripts attempt to associate silicone presence with clinical complaints reported by patients, while others try to demonstrate the mechanisms of silicone bleeding by permeability loss of breast implant surfaces. There also are reports of foreign body type reactions from implant fibrous capsule to silicone corpuscles. However, there seems to be no study that correlates the clinical, radiological, and histological correlations of these lesions. The objective of this review is to correlate radiological findings of silicone-induced granuloma of breast implant capsule (SIGBIC) from breast MRI (BMRI) scans and complementary findings of ultrasound (US) and positron emission tomography (PET) scan, and its histology originated from surgical breast implant capsulectomy. To make this correlation possible, we divided SIGBIC into three radiological findings: (1) intracapsular SIGBIC, (2) SIGBIC with extracapsular extension, and (3) mixed SIGBIC associated with seroma. Our experience demonstrates histological-radiological correlation in SIGBIC diagnosis. Knowledge of these findings may demonstrate its real importance in terms of public health and patient management. We believe that SIGBIC is currently underdiagnosed by lack of training, guidance, and management in our clinical practice.

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).