Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake.

In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome.

Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network.

Background: After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural network (dCNN) may be used to detect surgical clips in follow-up mammograms after BCS.

Methods: 884 mammograms and 517 tomosynthetic images depicting surgical clips and calcifications were manually segmented and classified.

Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks.

Objectives: The aim of this study was to develop and validate a commercially available AI platform for the automatic determination of image quality in mammography and tomosynthesis considering a standardized set of features.

Materials And Methods: In this retrospective study, 11,733 mammograms and synthetic 2D reconstructions from tomosynthesis of 4200 patients from two institutions were analyzed by assessing the presence of seven features which impact image quality in regard to breast positioning. Deep learning was applied to train five dCNN models on features detecting the presence of anatomical landmarks and three dCNN models for localization features.

Green composites based on volcanic red algae Cyanidiales, cellulose, and coffee waste biomass modified with magnetic nanoparticles for the removal of methylene blue.

In this paper, green nanocomposites based on biomass and superparamagnetic nanoparticles were synthesized and used as adsorbents to remove methylene blue (MB) from water with magnetic separation. The adsorbents were synthesized through the wet co-precipitation technique, in which iron-oxide nanoparticles coated the cores based on coffee, cellulose, and red volcanic algae waste. The procedure resulted in materials that could be easily separated from aqueous solutions with magnets.

Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks.

Objectives: High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reconstructions.

Methods: In total, 4605 synthetic 2D images (1665 patients, age: 57 ± 37 years) were labeled according to the ACR (American College of Radiology) density (A-D).

Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principle.

Objective: In this study, we investigate the feasibility of a deep Convolutional Neural Network (dCNN), trained with mammographic images, to detect and classify microcalcifications (MC) in breast-CT (BCT) images.

Methods: This retrospective single-center study was approved by the local ethics committee. 3518 icons generated from 319 mammograms were classified into three classes: "no MC" (1121), "probably benign MC" (1332), and "suspicious MC" (1065).

BI-RADS-Based Classification of Mammographic Soft Tissue Opacities Using a Deep Convolutional Neural Network.

The aim of this study was to investigate the potential of a machine learning algorithm to classify breast cancer solely by the presence of soft tissue opacities in mammograms, independent of other morphological features, using a deep convolutional neural network (dCNN). Soft tissue opacities were classified based on their radiological appearance using the ACR BI-RADS atlas. We included 1744 mammograms from 438 patients to create 7242 icons by manual labeling.

Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification.

Background: We investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT).

Methods: In this retrospective single-centre study, we analysed 10,000 images from 400 PC-BCT examinations of 200 patients. Images were categorised into four-level density scale (a-d) using Breast Imaging Reporting and Data System (BI-RADS)-like criteria.

Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network.

The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) "breast tissue", (2) "benign lymph nodes", and (3) "suspicious lymph nodes". Following data preprocessing, a dCNN model was trained and validated with 5385 images.

Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network.

Purpose: The aim of this study was to develop and test a post-processing technique for detection and classification of lesions according to the BI-RADS atlas in automated breast ultrasound (ABUS) based on deep convolutional neural networks (dCNNs).

Methods And Materials: In this retrospective study, 645 ABUS datasets from 113 patients were included; 55 patients had lesions classified as high malignancy probability. Lesions were categorized in BI-RADS 2 (no suspicion of malignancy), BI-RADS 3 (probability of malignancy < 3%), and BI-RADS 4/5 (probability of malignancy > 3%).

Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?

The aim of this study was to investigate the potential of a machine learning algorithm to accurately classify parenchymal density in spiral breast-CT (BCT), using a deep convolutional neural network (dCNN). In this retrospectively designed study, 634 examinations of 317 patients were included. After image selection and preparation, 5589 images from 634 different BCT examinations were sorted by a four-level density scale, ranging from A to D, using ACR BI-RADS-like criteria.

Deep learning for the standardized classification of Ki-67 in vulva carcinoma: A feasibility study.

Background: The aim of this study is to demonstrate the feasibility of automatic classification of Ki-67 histological immunostainings in patients with squamous cell carcinoma of the vulva using a deep convolutional neural network (dCNN).

Material And Methods: For evaluation of the dCNN, we used 55 well characterized squamous cell carcinomas of the vulva in a tissue microarray (TMA) format in this retrospective study. The tumor specimens were classified in 3 different categories C1 (0-2%), C2 (2-20%) and C3 (>20%), representing the relation of the number of KI-67 positive tumor cells to all cancer cells on the TMA spot.

Classification of Mammographic Breast Microcalcifications Using a Deep Convolutional Neural Network: A BI-RADS-Based Approach.

Materials And Methods: Over 56,000 images of 268 mammograms from 94 patients were labeled to 3 classes according to the BI-RADS standard: "no microcalcifications" (BI-RADS 1), "probably benign microcalcifications" (BI-RADS 2/3), and "suspicious microcalcifications" (BI-RADS 4/5). Using the preprocessed images, a dCNN was trained and validated, generating 3 types of models: BI-RADS 4 cohort, BI-RADS 5 cohort, and BI-RADS 4 + 5 cohort. For the final validation of the trained dCNN models, a test data set consisting of 141 images of 51 mammograms from 26 patients labeled according to the corresponding BI-RADS classification from the radiological reports was applied.

Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approach.

Marked enhancement of the fibroglandular tissue on contrast-enhanced breast magnetic resonance imaging (MRI) may affect lesion detection and classification and is suggested to be associated with higher risk of developing breast cancer. The background parenchymal enhancement (BPE) is qualitatively classified according to the BI-RADS atlas into the categories "minimal," "mild," "moderate," and "marked." The purpose of this study was to train a deep convolutional neural network (dCNN) for standardized and automatic classification of BPE categories.

Association between ins4436A in 11β-hydroxysteroid dehydrogenase type 1 gene and essential hypertension in Polish population.

Background: Essential hypertension (EH) is the most common cardiovascular disease worldwide, and it has a strong genetic component. Cortisol homeostasis is an important factor in controlling blood pressure, and the availability of this hormone is regulated by 11βhydroxysteroid dehydrogenase type 1 enzyme (11βHSD1), which converts cortisone into cortisol.

Materials And Methods: We investigated the correlation between EH and the single nucleotide polymorphism (SNP) ins4436A located on the hydroxysteroid (11-beta) dehydrogenase 1 gene among the Polish population.

Association between single nucleotide polymorphisms of the G-protein γ5 subunit and the risk of essential hypertension in the population of Poland.

Introduction: Polymorphisms in genes coding G-protein subunits (α, β, and γ) may affect the response of stimulated α2A-adrenergic receptors, which are involved in the regulation of blood pressure. OBJECTIVES The aim of the present study was to determine the association between the rs11559300 (A/G), rs199705300 (C/A), rs61754630 (C/T), rs13093 (C/A), and rs41284589 (C/T) single nucleotide polymorphisms (SNPs) of the gene coding G-protein γ5 subunit (GNG5) and the risk of essential hypertension in the population of Poland.

Patients And Methods: A total number of 838 subjects were included in the study: 536 patients with diagnosed essential hypertension and 302 controls.

Finding NEMO in preeclampsia.

Background: The mechanism of preeclampsia and its way of inheritance are still a mystery. Biochemical and immunochemical studies reveal a substantial increase in tumor necrosis factor alpha, interleukin-1 beta, and interleukin-6 concentrations in the blood of women with preeclampsia. The level of these factors is regulated by nuclear facxtor-kappa B, whose activation in a classical pathway requires inhibitory kappa B kinase gamma (known as NEMO or IKBKG).

Hydrogel Nanofilaments via Core-Shell Electrospinning.

Recent biomedical hydrogels applications require the development of nanostructures with controlled diameter and adjustable mechanical properties. Here we present a technique for the production of flexible nanofilaments to be used as drug carriers or in microfluidics, with deformability and elasticity resembling those of long DNA chains. The fabrication method is based on the core-shell electrospinning technique with core solution polymerisation post electrospinning.

Ammonium adsorption on Brønsted acidic centers on low-index vanadium pentoxide surfaces.

Vanadium-based catalysts are used in many technological processes, among which the removal of nitrogen oxides (NOx) from waste gases is one of the most important. The chemical reaction responsible for this selective catalytic reaction (SCR) is based on the reduction of NOx molecules to N2, and a possible reductant in this case is pre-adsorbed NH3. In this paper, NH3 adsorption on Brønsted OH acid centers on low-index surfaces of V2O5 (010, 100, 001) is studied using a theoretical DFT method with a gradient-corrected functional (RPBE) in the embedded cluster approximation model.

Collagenous repeat-containing sequence of 26 kDa protein - a newly discovered adipokine - sensu lato - A minireview.

Background: Increasing evidence from numerous research studies in internal medicine shows that adipocytes and adipokines are involved in primary inflammatory processes and disease. CORS-26 (collagenous repeat- containing sequence of 26 kDa protein) is a newly discovered adipokine of the C1q/TNF molecular superfamily C1q/TNF-related protein-3 (CTRP-3) secreted, inter alia in murine monocytes and adipocytes and in human adipocytes. Reported recently as a gene product of adipocyte differentiation, it shares structural similarity with the adipocyte, adiponectin.

Pigment epithelium-derived factor as a new marker of metabolic syndrome in Caucasian population.

Authors present that serum pigment epithelium derived factor (PEDF) is an independent marker of metabolic syndrome in Caucasianpopulation. PEDF was measured with new ELISA sandwich test. J.