Breast Mass with Bloody Discharge: A Case of Idiopathic Granulomatous Mastitis

One of the rare identity of breast diseases is Idiopathic Granulomatous Mastitis (IGM), a chronic inflammatory breast condition that can mimic advanced breast cancer. The case came with nipple discharge and mass with skin changes, which was definitively diagnosed following core-cut biopsy of the mastitis group idiopathic granulomatous.

Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor

This paper introduces a comparative analysis of the proficiencies of various textures and geometric features in the diagnosis of breast masses on mammograms. An improved machine learning-based framework was developed for this study. The proposed system was tested using 106 full field digital mammography images from the INbreast dataset, containing a total of 115 breast mass lesions. The proficiencies of individual and various combinations of computed textures and geometric features were investigated by evaluating their contributions towards attaining higher classification accuracies. Four state-of-the-art filter-based feature selection algorithms (Relief-F, Pearson correlation coefficient, neighborhood component analysis, and term variance) were employed to select the top 20 most discriminative features. The Relief-F algorithm outperformed other feature selection algorithms in terms of classification results by reporting 85.2% accuracy, 82.0% sensitivity, and 88.0% specificity. A set of nine most discriminative features were then selected, out of the earlier mentioned 20 features obtained using Relief-F, as a result of further simulations. The classification performances of six state-of-the-art machine learning classifiers, namely k-nearest neighbor (k-NN), support vector machine, decision tree, Naive Bayes, random forest, and ensemble tree, were investigated, and the obtained results revealed that the best classification results (accuracy = 90.4%, sensitivity = 92.0%, specificity = 88.0%) were obtained for the k-NN classifier with the number of neighbors having k = 5 and squared inverse distance weight. The key findings include the identification of the nine most discriminative features, that is, FD26 (Fourier Descriptor), Euler number, solidity, mean, FD14, FD13, periodicity, skewness, and contrast out of a pool of 125 texture and geometric features. The proposed results revealed that the selected nine features can be used for the classification of breast masses in mammograms.

Metastatic Breast Signet-Ring Cell Carcinoma from a Colonic Primary: Review of a Rare Case Report

Introduction: Colonic signet-ring cell carcinoma is a distinctive rare subtype of adenocarcinoma with a predilection for early metastasis. Among the rare extramammary metastatic adenocarcinomas to the breast, colonic signet-ring cell carcinomas constitute a small percentage. The distinction of a primary from a secondary breast signet ring cell carcinoma is indispensable since it may result in different therapeutic approaches. Here we presented a rare case of metastatic breast signet-ring cell carcinoma from a rectal origin and review its distinctive histopathologic features. Case Presentation: A 37-year-old woman presented with a breast mass 3 months after undergoing low anterior resection surgery to remove a rectal mass, diagnosed as signet ring cell carcinoma. Histopathologic examination of the core needle breast mass biopsy revealed tumor cells with signet-ring cell cytomorphology. The performed immunohistochemistry confirmed carcinoma of colonic origin. Conclusions: Colorectal signet-ring cell carcinoma is a rare and aggressive tumor. Its metastatic spread is most seen in the intra-abdominal area, with seldom reported cases of breast metastasis. Histologically, it can mimic a primary breast carcinoma, especially if no prior history of colonic origin exists. Accurate diagnosis is important since these 2 entities carry different therapeutic management. Proper immunophenotyping, obtaining a thorough clinical history and imaging studies facilitate a correct diagnosis.

Malignant Phyllodes Tumor of the Breast and Pregnancy: A Rare Case Report and Literature Review

Phyllodes Tumor (PT) is a rare fibroepithelial breast tumor that can behave differently depending on its biologic features. Traditionally, PTs are classified by their histologic features into benign, borderline, and malignant. In most cases that were reported, all PTs may recur, but only the borderline and malignant PT can metastasize. PT usually occurs as a breast lump or accidental finding on ultrasound (US) examination. The clinical features include a well-defined breast mass, regular or lobulated. The diagnosis is based on the integration of morphology features, but remains challenging, particularly in the distinction from fibroadenomas. We report a case of a 36-year-old patient who presented for a voluminous breast mass, rapidly growing in the past 3–4 months. At presentation, the patient was 19 weeks pregnant. The breast tumor had the clinical and US aspect of PT. A core needle biopsy was obtained, confirming a benign PT, and local excision was performed with no postoperative complications. The final pathology report showed a borderline PT with close resection margins of 1 mm. Immunohistochemistry (IHC) established the diagnosis of malignant PT with heterologous sarcomatous differentiation. The case was discussed in the multidisciplinary tumor board (MDT) and mastectomy was recommended. The patient fully consented but refused surgery at 25 weeks’ gestation, fearing premature delivery. The right breast was closely monitored by US, and at 9 weeks after the first surgery, signs of local recurrence were detected. At 35 weeks’ gestation, right mastectomy was performed, with no perioperative complications. The pregnancy was closely followed up and no complication were found. The final pathology report describes multiples PT recurrences with heterologous sarcomatous differentiation. The pregnancy outcome was uneventful, and the patient delivered a healthy child vaginally at term with no peripartum complication. Postpartum, a computer tomography (CT) examination of the head, thorax, abdomen and pelvis was performed, with no evidence of metastases. Adjuvant chemotherapy and radiotherapy completed the treatment. The follow-up and CT scan showed no metastases or further recurrence 4 years after diagnosis. In conclusion, diagnosis of PT can be difficult, especially because of the easy confusion with fibroadenoma of the breast. There are rare cases when a pathology exam needs further assessment and IHC is recommended for accurate diagnosis. Although malignant PT is rare and accounts for <1% of all breast cancers, the diagnosis and treatment that are recommended are based on the reported cases. Moreover, when complete surgical excision is achieved, the rates of recurrence and distant metastases are low, and adjuvant therapy might not be necessary.

Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study

Radiologists’ diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists’ diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists.

Nodular Fasciitis of the breast – An uncommon benign reactive proliferative lesion

Nodular fasciitis is rarely reported in breast. It is benign reactive proliferative lesion of fibroblast. A 65 year old female presented to surgical department for left breast mass since 2 months duration, rapidly enlarging without any regional lymphadenopathy. Mammography showed hyperdensity masses with irregular margin. On FNAC reported as benign spindle cell lesion. Left sided two breast masses measuring 4 x 3.3 x 2.5cm and 2.0 x 1.6 x 0.5cm were excised and on histopathology reported as Nodular Fasciitis of left breast. We are presenting this extremely rare case of nodular fasciitis of breast for its clinical, radiological and histopathological findings.

Connected-UNets: a deep learning architecture for breast mass segmentation

Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic systems for breast mass segmentation to assist radiologists in their diagnosis. With the rapid development of deep learning and its application to medical imaging challenges, UNet and its variations is one of the state-of-the-art models for medical image segmentation that showed promising performance on mammography. In this paper, we propose an architecture, called Connected-UNets, which connects two UNets using additional modified skip connections. We integrate Atrous Spatial Pyramid Pooling (ASPP) in the two standard UNets to emphasize the contextual information within the encoder–decoder network architecture. We also apply the proposed architecture on the Attention UNet (AUNet) and the Residual UNet (ResUNet). We evaluated the proposed architectures on two publically available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Experiments were also conducted using additional synthetic data using the cycle-consistent Generative Adversarial Network (CycleGAN) model between two unpaired datasets to augment and enhance the images. Qualitative and quantitative results show that the proposed architecture can achieve better automatic mass segmentation with a high Dice score of 89.52%, 95.28%, and 95.88% and Intersection over Union (IoU) score of 80.02%, 91.03%, and 92.27%, respectively, on CBIS-DDSM, INbreast, and the private dataset.