Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful1. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives2. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening. An artificial intelligence (AI) system performs as well as or better than radiologists at detecting breast cancer from mammograms, and using a combination of AI and human inputs could help to improve screening efficiency.

/pdf/international-evaluation-of-an-ai-system-for-breast-cancer-4c1yg7x284.pdf

International evaluation of an AI system for breast cancer screening.

Helicobacter pylori is a gastric pathogen that colonizes approximately 50% of the world's population. Infection with H. pylori causes chronic inflammation and significantly increases the risk of developing duodenal and gastric ulcer disease and gastric cancer. Infection with H. pylori is the strongest known risk factor for gastric cancer, which is the second leading cause of cancer-related deaths worldwide. Once H. pylori colonizes the gastric environment, it persists for the lifetime of the host, suggesting that the host immune response is ineffective in clearing this bacterium. In this review, we discuss the host immune response and examine other host factors that increase the pathogenic potential of this bacterium, including host polymorphisms, alterations to the apical-junctional complex, and the effects of environmental factors. In addition to host effects and responses, H. pylori strains are genetically diverse. We discuss the main virulence determinants in H. pylori strains and the correlation between these and the diverse clinical outcomes following H. pylori infection. Since H. pylori inhibits the gastric epithelium of half of the world, it is crucial that we continue to gain understanding of host and microbial factors that increase the risk of developing more severe clinical outcomes.

Helicobacter pylori and Gastric Cancer: Factors That Modulate Disease Risk

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries, and is approaching approximately 34,986,502 cases worldwide according to the statistics of European Centre for Disease Prevention and Control. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using 5-fold cross validation. Considering the performance results obtained, it has seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

/pdf/automatic-detection-of-coronavirus-disease-covid-19-using-x-562zky4j2h.pdf

Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

/pdf/automatic-detection-of-coronavirus-disease-covid-19-using-x-4sd9k3h18e.pdf

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks.

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

https://acsjournals.onlinelibrary.wiley.com/doi/epdf/10.3322/caac.21552

Artificial intelligence in cancer imaging: Clinical challenges and applications.

In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimately considered useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95. The approach described here has achieved 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85. When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset. Source code, the trained model and an OsiriX plugin are published online at https://github.com/riblidezso/frcnn_cad .

/pdf/detecting-and-classifying-lesions-in-mammograms-with-deep-41p9rqz0mh.pdf

Detecting and classifying lesions in mammograms with Deep Learning

In the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms. The benefits of current CAD technologies appear to be contradictory and they should be improved to be ultimately considered useful. Since 2012 deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95 . The approach described here has achieved the 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85 . When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset. Source code, the trained model and an OsiriX plugin are availaible online at this https URL .

BackgroundMutation of the p53 gene is detectable in most cases of gastric cancer, as it is the most common genetic alteration in human malignancies. It is also well documented that Helicobacter pylori infection plays an important role in gastric carcinogenesis. There is still no clarification, howev

Mutant p53 expression and apoptotic activity of Helicobacter pylori positive and negative gastritis in correlation with the presence of intestinal metaplasia

Objectives : genotypes of Helicobacter pylori are the focus of interest because they play a prominent role in mucosal injury. The purpose of this study was to determine cagA and vacA genotypes of H. pylori using real-time polymerase chain reaction (PCR) method with a double strain DNA binding SYBR Green I.dye, and to compare this with those of two immunohistochemical methods. Methods : forty-three paraffin-embedded biopsy tissue samples were examined by histology, epidermal growth factor receptor (EGFR), proliferating cell nuclear antigen (PCNA) immunohistochemistry and melting curve analysis of real-time PCR. Results : the presence of cagA gene was associated with a significantly higher frequency of gastritis ( P =0.003) than that of vacA gene with intestinal metaplasia ( P =0.045). Significant difference was found between the presence of cagA gene and EGFR expression in intestinal metaplasia cases in comparison with cagA negative samples ( P =0.0418). Statistically significant difference was detected between increased cell proliferation and the presence of gastritis. Conclusions : this method seems to be suitable for H. pylori genotype determination. Sensitivity, speed and simplicity are key areas in the development of PCR assays for H. pylori . Results supported the notion that infection with cagA positive H. pylori strain causes more augmentated cell proliferation in the stomach mucosa.

Determination of Helicobacter pylori cagA, vacA genotypes with real-time PCR melting curve analysis.

Objectives: the effect of Helicobacter pylori infection on gastric epithelial cell proliferation and apoptosis is still controversial. Our aim was to evaluate the effect of H. pylori infection on cell kinetic parameters in normal gastric epithelium, gastritis with/without intestinal metaplasia and gastric cancer. Patients and methods: antral biopsies were taken from 121 patients (61 women, 60 men, mean age 58.5±14.3 years of age) who underwent routine gastroscopy for upper gastrointestinal symptoms. Sections were scored for normal epithelia (n=15), gastritis without intestinal metaplasia (n=74), gastritis with intestinal metaplasia (n=24), and gastric adenocarcinoma (n=8). Fifty-two patients had H. pylori positive gastritis, and success of H. pylori eradication therapy was controlled in 12 cases, all with intestinal metaplasia. To characterize cell proliferation and assess apoptosis, immunohistochemistry [Proliferating Cell Nuclear Antigen (PCNA)], histochemistry [Argyrophil Nucleolar Organizer Regions (AgNOR)], and terminal deoxynucleotidyl transferase (TdT)-mediated deoxyuridinetriphosphate (dUTP) nick end-labeling (TUNEL) were used, respectively. Results: both cell proliferation and apoptosis is was higher in chronic gastritis when compared with normal epithelia, but neither PCNA LI (54.79±19.1 vs. 53.20±20.7) nor AgNOR counts (291.43±44.3 vs. 277.8±57.54) were different in H. pylori positive versus negative chronic gastritis. A significant positive correlation (P<0.05) was found in this group between PCNA and AgNOR techniques. Apoptosis was significantly higher (P<0.05) in H. pylori positive cases only when intestinal metaplasia was not present. Cell proliferation in intestinal metaplasia decreased to the activity of normal epithelium after successful eradication of H. pylori but remained high if eradication therapy failed. Conclusions: epithelial cell proliferation does not depend on H. pylori status in chronic gastritis. H. pylori increases apoptosis only in the absence of intestinal metaplasia.

Zsuzsa Unger

Papers

Detecting and classifying lesions in mammograms with Deep Learning

Detecting and classifying lesions in mammograms with Deep Learning

Mutant p53 expression and apoptotic activity of Helicobacter pylori positive and negative gastritis in correlation with the presence of intestinal metaplasia

Determination of Helicobacter pylori cagA, vacA genotypes with real-time PCR melting curve analysis.

Effect of Helicobacter pylori infection and eradication on gastric epithelial cell proliferation and apoptosis.