We report the case of a 27-year-old woman with primary actinomycosis of the breast. Diagnosis was established by culture examination of specimen recovered by fine-needle aspiration cytology (FNAC) under ultrasound guidance. To our knowledge, this is the first description in the literature of a case of primary actinomycosis of the breast caused by Actinomyces viscosus. Twenty-nine previous cases of primary actinomycosis of the breast have been published, but these were caused by the more common species Actinomyces israelii. Targeted antibiotic therapy did not ameliorate the condition, thus drainage and excision of the mass were carried out. No other medical therapy was administered. Six years after surgery, no recurrence has been observed on both ultrasonographic and mammographic examinations.

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Gross examination showed that the lesion was a large elastic mass (5.2 9 5 9 2 cm) with a short pedicle ( Fig. 3).Histologic examination showed that the lesion had a core of fibrovascular tissue with dense collagen fibers surrounded by a hyperplastic epidermis (Fig. 4).No sebaceous glands, sweat glands, smooth muscles, or ductal elements were observed in the mass. Therefore, the lesion was recognized as a fibroepithelial polyp of the nipple, which is a subtype of skin tag lesions.

Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset—namely CMTD—of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.