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Ultrasound (US) is the preferred modality for fatty liver disease diagnosis due to its noninvasive, real-time, and cost-effective imaging capabilities. However, traditional B-mode US is qualitative, and therefore, the assessment is very subjective. Computer-aided diagnostic tools can improve the specificity and sensitivity of US and help clinicians to perform uniform diagnoses. In this work, we propose a novel deep learning model for nonalcoholic fatty liver disease classification from US data. We design a multi-feature guided multi-scale residual convolutional neural network (CNN) architecture to capture features of different receptive fields. B-mode US images are combined with their corresponding local phase filtered images and radial symmetry transformed images as multi-feature inputs for the network. Various fusion strategies are studied to improve prediction accuracy. We evaluate the designed network architectures on B-mode in vivo liver US images collected from 55 subjects. We also provide quantitative results by comparing our proposed multi-feature CNN architecture against traditional CNN designs and machine learning methods. Quantitative results show an average classification accuracy above 90% over tenfold cross-validation. Our proposed method achieves a 97.8% area under the ROC curve (AUC) for the patient-specific leave-one-out cross-validation (LOOCV) evaluation. Comprehensive validation results further demonstrate that our proposed approaches achieve significant improvements compared to training mono-feature CNN architectures ( $$p

Purpose: Recently, the outbreak of the novel coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. In fighting against COVID-19, effective diagnosis of infected patient is critical for preventing the spread of diseases. Due to limited availability of test kits, the need for auxiliary diagnostic approach has increased. Recent research has shown radiography of COVID-19 patient, such as CT and X-ray, contains salient information about the COVID-19 virus and could be used as an alternative diagnosis method. Chest X-ray (CXR) due to its faster imaging time, wide availability, low cost, and portability gains much attention and becomes very promising. In order to reduce intra- and inter-observer variability, during radiological assessment, computer-aided diagnostic tools have been used in order to supplement medical decision making and subsequent management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologist in the interpretation of the collected data. Method: In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images. CXR images are enhanced using a local phase-based image enhancement method. The enhanced images, together with the original CXR data, are used as an input to our proposed CNN architecture. Using ablation studies, we show the effectiveness of the enhanced images in improving the diagnostic accuracy. We provide quantitative evaluation on two datasets and qualitative results for visual inspection. Quantitative evaluation is performed on data consisting of 8851 normal (healthy), 6045 pneumonia, and 3323 COVID-19 CXR scans. Results: In Dataset-1, our model achieves 95.57% average accuracy for a three classes classification, 99% precision, recall, and F1-scores for COVID-19 cases. For Dataset-2, we have obtained 94.44% average accuracy, and 95% precision, recall, and F1-scores for detection of COVID-19. Conclusions: Our proposed multi-feature-guided CNN achieves improved results compared to single-feature CNN proving the importance of the local phase-based CXR image enhancement. Future work will involve further evaluation of the proposed method on a larger-size COVID-19 dataset as they become available.

PURPOSE: Limited epidemiologic data are available on the expression of adipokines leptin (LEP) and adiponectin (ADIPOQ) and adipokine receptors (LEPR, ADIPOR1, ADIPOR2) in the breast tumor microenvironment (TME). The associations of gene expression of these biomarkers with tumor clinicopathology is not well understood. METHODS: NanoString multiplexed assays were used to assess the gene expression levels of LEP, LEPR, ADIPOQ, ADIPOR1, and ADIPOR2 within tumor tissues among 162 Black and 55 White women with newly diagnosed breast cancer. Multivariate mixed effects models were used to estimate associations of gene expression with breast tumor clinicopathology (overall and separately among Blacks). RESULTS: Black race was associated with lower gene expression of LEPR (P=0.002) and ADIPOR1 (P=0.01). Lower LEP, LEPR, and ADIPOQ gene expression were associated with higher tumor grade (P=0.0007, P

Despite significant scientific and medical discoveries, the genetics of novel infectious diseases like COVID-19 remains far from understanding. SARS-CoV-2 is a single-stranded RNA respiratory virus that causes COVID-19 by binding to the ACE2 receptor in the lung and other organs. Understanding its clinical presentation and metabolomic and genetic profile will lead to the discovery of diagnostic, prognostic and predictive biomarkers, which may lead to more effective medical therapy. It is important to investigate correlations and overlap between reported diagnoses of a patient with COVID-19 in clinical data with identified germline and somatic mutations, and highly expressed genes from genomics data analysis. Timely model clinical, genomics and metabolomics data to find statistical patterns across millions of features to identify underlying biological pathways, modifiable risk factors and actionable information that supports early detection and prevention of COVID-19, and development of new therapies for better patient care. Next, ensuring security reconcile noise, need to build and train machine learning prognostic models to find actionable information that supports early detection and prevention of COVID-19. Based on the myriad data, applying appropriate machine learning algorithms to stratify patients, understand scenarios, optimise decision-making, identify high-risk rare variants (including ACE2, TMPRSS2) and making medically relevant predictions. Innovative and intelligent solutions are required to improve the traditional symptom-driven practice, and allow earlier interventions using predictive diagnostics and tailor better personalised treatments, when confronted with the challenges of pandemic situations.

Multiple endocrine neoplasia type 1 (MEN1) is a genetic syndrome in which patients develop neuroendocrine tumors (NETs), including pancreatic neuroendocrine tumors (PanNETs). The prolonged latency of tumor development in MEN1 patients suggests a likelihood that other mutations cooperate with Men1 to induce PanNETs. We propose that Pten loss combined with Men1 loss accelerates tumorigenesis. To test this, we developed two genetically engineered mouse models (GEMMs)—MPR (Men1flox/flox Ptenflox/flox RIP-Cre) and MPM (Men1flox/flox Ptenflox/flox MIP-Cre) using the Cre-LoxP system with insulin-specific biallelic inactivation of Men1 and Pten. Cre in the MPR mouse model was driven by the transgenic rat insulin 2 promoter while in the MPM mouse model was driven by the knock-in mouse insulin 1 promoter. Both mouse models developed well-differentiated (WD) G1/G2 PanNETs at a much shorter latency than Men1 or Pten single deletion alone and exhibited histopathology of human MEN1-like tumor. The MPR model, additionally, developed pituitary neuroendocrine tumors (PitNETs) in the same mouse at a much shorter latency than Men1 or Pten single deletion alone as well. Our data also demonstrate that Pten plays a role in NE tumorigenesis in pancreas and pituitary. Treatment with the mTOR inhibitor rapamycin delayed the growth of PanNETs in both MPR and MPM mice, as well as the growth of PitNETs, resulting in prolonged survival in MPR mice. Our MPR and MPM mouse models are the first to underscore the cooperative roles of Men1 and Pten in cancer, particularly neuroendocrine cancer. The early onset of WD PanNETs mimicking the human counterpart in MPR and MPM mice at 7 weeks provides an effective platform for evaluating therapeutic opportunities for NETs through targeting the MENIN-mediated and PI3K/AKT/mTOR signaling pathways.

Background: Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI’s Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features). Materials and Methods: As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. Results: Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. Conclusion: To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.