A novel panel of blood-based microRNAs capable of discrimination between benign breast disease and breast cancer at early stages.

Breast cancer (BC) as a leading cause of cancer death among women, exhibits a wide range of genetic heterogeneity in affected individuals. Satisfactory management of BC depends on early diagnosis and proper monitoring of patients' response to therapy. In this study, we aimed to assess the relation between the expression patterns of blood-based microRNAs (miRNAs) with demographic characteristics of the patients with BC in an attempt to find novel diagnostic markers for BC with acceptable precision in clinical applications. To this end, we performed comprehensive statistical analysis of the data of the Cancer Genome Atlas (TCGA) database and the blood miRNome dataset (GSE31309). As a result, 21 miRNAs were selected for experimental verification by quantitative RT-PCR on blood samples of 70 BC patients and 60 normal individuals (without any lesions or benign breast diseases). Statistical one-way ANOVA revealed no significant difference in the blood levels of the selected miRNAs in BC patients compared to any lesions or benign breast diseases. However, the multi-marker panel consisting of hsa-miR-106b-5p, -126-3p, -140-3p, -193a-5p, and -10b-5p could detect early-stages of BC with 0.79 sensitivity, 0.86 specificity and 0.82 accuracy. Furthermore, this multi-marker panel showed the potential of detecting benign breast diseases from BC patients with 0.67 sensitivity, 0.80 specificity, and 0.74 accuracy. In conclusion, these data indicate that the present panel might be considered an asset in detecting benign breast disease and BC.

Breast cancer detection in automated 3D breast ultrasound using iso-contours and cascaded RUSBoosts.

Automated 3D breast ultrasound (ABUS) is a new popular modality as an adjunct to mammography for detecting cancers in women with dense breasts. In this paper, a multi-stage computer aided detection system is proposed to detect cancers in ABUS images. In the first step, an efficient despeckling method called OBNLM is applied on the images to reduce speckle noise. Afterwards, a new algorithm based on isocontours is applied to detect initial candidates as the boundary of masses is hypo echoic. To reduce false generated isocontours, features such as hypoechoicity, roundness, area and contour strength are used. Consequently, the resulted candidates are further processed by a cascade classifier whose base classifiers are Random Under-Sampling Boosting (RUSBoost) that are introduced to deal with imbalanced datasets. Each base classifier is trained on a group of features like Gabor, LBP, GLCM and other features. Performance of the proposed system was evaluated using 104 volumes from 74 patients, including 112 malignant lesions. According to Free Response Operating Characteristic (FROC) analysis, the proposed system achieved the region-based sensitivity and case-based sensitivity of 68% and 76% at one false positive per image.