Importance: Improved screening methods for women with dense breasts are needed because of their increased risk of breast cancer and of failed early diagnosis by screening mammography. Objective: To compare the screening performance of abbreviated breast MRI (AB-MR), and digital breast tomosynthesis (DBT) in women with dense breasts. Design, Setting, and Participants: Cross-sectional study with longitudinal follow-up at 48 academic, community hospital, and private practice sites in the US and Germany, conducted between December 2016 and November 2017, that included average-risk women aged 40-75 years with heterogeneously dense or extremely dense breasts undergoing routine screening. Follow up ascertainment of cancer diagnoses was complete through September 12 th , 2019. Exposure: All women underwent screening by both DBT and AB-MR, performed in randomized order and read independently to avoid interpretation bias. Main outcome measures: The primary endpoint was the invasive cancer detection rate. Secondary outcomes included sensitivity, specificity, the additional-imaging-recommendation-rate, and positive predictive value (PPV) of biopsy, using invasive cancer and DCIS to define a positive reference standard. All outcomes are reported at the participant level. Pathology of core or surgical biopsy was the reference standard for cancer detection rate and PPV; interval cancers reported until the next annual screen were included in the reference standard for sensitivity and specificity. Results: Among 1516 enrolled women, 1444 (median age 54, range 40-75) completed both examinations and were included in the analysis. The reference standard was positive for invasive cancer with or without DCIS in 17 women, and for DCIS alone in another 6. No interval cancers were observed during follow-up. AB-MR detected all 17 women with invasive cancer, and 5/6 women with DCIS. DBT detected 7/17 women with invasive cancer, and 2/6 women with DCIS. The invasive-cancer-detection-rate was 11.8 per 1000 women [95% CI 7.4-18.8] for AB-MR versus 4.8 per 1000 women [95% CI 2.4-10.0] for DBT, a difference of 7 per 1000 women [95% CI for the difference 2.2-11.6] (exact McNemar p=0.002). For detection of invasive cancer and Comstock et al.

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Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spectral, and statistical features of each input mammogram and generates models of real MCs to be used as training samples through a simplified learning phase of the Bayesian classifier. Followed by an estimation of the classifier's decision function parameters, a mammogram is segmented into the identified targets (MCs) against background (healthy tissue). The proposed algorithm has been tested using 23 mammograms from the mini-MIAS database. Experimental results achieved MCs detection with average true positive (sensitivity) and false positive (specificity) of 91.3% and 98.6%, respectively. Results also indicate that the modeling of the real MCs plays a significant role in the performance of the classifier and thus should be given further investigation.

Breast cancer is a major cause of death and morbidity among women all over the world, and it is a fact that early detection is a key in improving outcomes. Therefore development of algorithms that aids radiologists in identifying changes in breast tissue early on is essential. In this work an algorithm that investigates the use of principal components analysis (PCA) is developed to identify suspicious regions on mammograms. The algorithm employs linear structure and curvelinear modeling prior to PCA implementations. Evaluation of the algorithm is based on the percentage of correct classification, false positive (FP) and false negative (FN) in all experimental work using real data. Over 90% accuracy in block classification is achieved using mammograms from MIAS database.

A novel, simple and reliable high-performance thin-layer chromatography (HPTLC) method was developed and validated for the quantification of trehalulose in stingless bee honey. The chromatographic separation was performed using silica gel 60 F254 HPTLC plates and 1-butanol‒2-propanol‒aqueous boric acid solution (5 mg/mL) (30:50:10, V/V) as the mobile phase. The retardation factor (RF) for trehalulose was found to be 0.045. The method showed linearity over the concentration range of 100–800 ng per band with a coefficient of correlation (R) of 0.9996. The limit of detection and limit of quantification for trehalulose were found to be 20.04 ng per band and 60.72 ng per band, respectively and the mean per cent recovery of trehalulose was 101.8%. The method has been validated for its specificity, linearity, sensitivity, precision, accuracy, repeatability and robustness following the International Council for Harmonisation Q2 (R1), and it has been successfully applied in the determination of trehalulose in stingless bee honey.