Clinical Sensitivity and Specificity of the PROSTest in an American Cohort.

Introduction: Prostate cancer (PCa) is the most commonly diagnosed cancer in men in the United States, following skin cancer, with an incidence rate of 112.7 per 100,000 men per year. The need for a reliable, non-invasive diagnostic tool for early PCa detection (screening, biochemical residual disease) remains unmet due to the limitations of PSA testing, which often leads to overdiagnosis and overtreatment.

Early detection of heart failure using in-patient longitudinal electronic health records.

Heart Failure (HF) is common, with worldwide prevalence of 1%-3% and a lifetime risk of 20% for individuals 40 years or older. Despite its considerable health economic burden, techniques for early detection of HF in the general population are sparse. In this work we tested the hypothesis that a simple Transformer neural network, trained on comprehensive collection of secondary care data across the general population, can be used to prospectively (three-year predictive window) identify patients at an increased risk of first hospitalisation due to HF (HHF).

Evaluation of a multigenomic liquid biopsy (PROSTest) for prostate cancer detection and follow-up in a Caribbean population.

Introduction: The PROSTest is a novel machine learning-based liquid biopsy assay that functions as a diagnostic and prognostic tool in prostate cancer (PCa). The algorithm outcome (scored 0-100) has a cutoff of >50 to indicate PCa. In this study, we evaluated the screening utility of the test in comparison with the commonly used PSA test.

Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records.

Introduction And Objectives: Despite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model.

Patients And Methods: n = 940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction.

Development of a multigenomic liquid biopsy (PROSTest) for prostate cancer in whole blood.

Introduction: We describe the development of a molecular assay from publicly available tumor tissue mRNA databases using machine learning and present preliminary evidence of functionality as a diagnostic and monitoring tool for prostate cancer (PCa) in whole blood.

Materials And Methods: We assessed 1055 PCas (public microarray data sets) to identify putative mRNA biomarkers. Specificity was confirmed against 32 different solid and hematological cancers from The Cancer Genome Atlas (n = 10,990).

A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge.

An Artificial Neural Network for Nasogastric Tube Position Decision Support.

Purpose: To develop and validate a deep learning model for detection of nasogastric tube (NGT) malposition on chest radiographs and assess model impact as a clinical decision support tool for junior physicians to help determine whether feeding can be safely performed in patients (feed/do not feed).

Materials And Methods: A neural network ensemble was pretrained on 1 132 142 retrospectively collected (June 2007-August 2019) frontal chest radiographs and further fine-tuned on 7081 chest radiographs labeled by three radiologists. Clinical relevance was assessed on an independent set of 335 images.

C-type lectin receptor CLEC4A2 promotes tissue adaptation of macrophages and protects against atherosclerosis.

Macrophages are integral to the pathogenesis of atherosclerosis, but the contribution of distinct macrophage subsets to disease remains poorly defined. Using single cell technologies and conditional ablation via a LysM Clec4a2 mouse strain, we demonstrate that the expression of the C-type lectin receptor CLEC4A2 is a distinguishing feature of vascular resident macrophages endowed with athero-protective properties. Through genetic deletion and competitive bone marrow chimera experiments, we identify CLEC4A2 as an intrinsic regulator of macrophage tissue adaptation by promoting a bias in monocyte-to-macrophage in situ differentiation towards colony stimulating factor 1 (CSF1) in vascular health and disease.

Development and prospective validation of COVID-19 chest X-ray screening model for patients attending emergency departments.

Chest X-rays (CXRs) are the first-line investigation in patients presenting to emergency departments (EDs) with dyspnoea and are a valuable adjunct to clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to facilitate rapid triage of CXRs for further patient testing and/or isolation. In this work we develop an AI algorithm, CovIx, to differentiate normal, abnormal, non-COVID-19 pneumonia, and COVID-19 CXRs using a multicentre cohort of 293,143 CXRs.

Early Identification of Residual Disease After Neuroendocrine Tumor Resection Using a Liquid Biopsy Multigenomic mRNA Signature (NETest).

Introduction: Surgery is the only cure for neuroendocrine tumors (NETs), with R0 resection being critical for successful tumor removal. Early detection of residual disease is key for optimal management, but both imaging and current biomarkers are ineffective post-surgery. NETest, a multigene blood biomarker, identifies NETs with >90% accuracy.

Neuroendocrine Tumor Omic Gene Cluster Analysis Amplifies the Prognostic Accuracy of the NETest.

Background: The NETest is a multigene assay comprising 51 circulating neuroendocrine tumor (NET)-specific transcripts. The quotient of the 51-gene assay is based upon an ensemble of machine learning algorithms. Eight cancer hallmarks or "omes" (apoptome, epigenome, growth factor signalome, metabolome, proliferome, plurome, secretome, SSTRome) represent 29 genes.

Microbiota-Derived Metabolites Suppress Arthritis by Amplifying Aryl-Hydrocarbon Receptor Activation in Regulatory B Cells.

The differentiation of IL-10-producing regulatory B cells (Bregs) in response to gut-microbiota-derived signals supports the maintenance of tolerance. However, whether microbiota-derived metabolites can modulate Breg suppressive function remains unknown. Here, we demonstrate that rheumatoid arthritis (RA) patients and arthritic mice have a reduction in microbial-derived short-chain fatty acids (SCFAs) compared to healthy controls and that in mice, supplementation with the SCFA butyrate reduces arthritis severity.

Supervised and unsupervised language modelling in Chest X-Ray radiological reports.

Chest radiography (CXR) is the most commonly used imaging modality and deep neural network (DNN) algorithms have shown promise in effective triage of normal and abnormal radiograms. Typically, DNNs require large quantities of expertly labelled training exemplars, which in clinical contexts is a major bottleneck to effective modelling, as both considerable clinical skill and time is required to produce high-quality ground truths. In this work we evaluate thirteen supervised classifiers using two large free-text corpora and demonstrate that bi-directional long short-term memory (BiLSTM) networks with attention mechanism effectively identify Normal, Abnormal, and Unclear CXR reports in internal (n = 965 manually-labelled reports, f1-score = 0.

The clinical applications of a multigene liquid biopsy (NETest) in neuroendocrine tumors.

Purpose: There are few effective biomarkers for neuroendocrine tumors. Precision oncology strategies have provided liquid biopsies for real-time and tailored decision-making. This has led to the development of the first neuroendocrine tumor liquid biopsy (the NETest).

PRRT neuroendocrine tumor response monitored using circulating transcript analysis: the NETest.

Purpose: Peptide receptor radionuclide therapy (PRRT) is effective for metastatic/inoperable neuroendocrine tumors (NETs). Imaging response assessment is usually efficient subsequent to treatment completion. Blood biomarkers such as PRRT Predictive Quotient (PPQ) and NETest are effective in real-time.

Aryl Hydrocarbon Receptor Contributes to the Transcriptional Program of IL-10-Producing Regulatory B Cells.

Regulatory B cells (Bregs) play a critical role in the control of autoimmunity and inflammation. IL-10 production is the hallmark for the identification of Bregs. However, the molecular determinants that regulate the transcription of IL-10 and control the Breg developmental program remain unknown.

Utility of a ready-to-use PCR system for neuroendocrine tumor diagnosis.

Background: Multigene-based PCR tests are time-consuming and limiting aspects of the protocol include increased risk of operator-based variation. In addition, such protocols are complex to transfer and reproduce between laboratories.

Aims: Evaluate the clinical utility of a pre-spotted PCR plate (PSP) for a novel multigene (n = 51) blood-based gene expression diagnostic assay for neuroendocrine tumors (NETs).

Blood Chromogranin A Is Not Effective as a Biomarker for Diagnosis or Management of Bronchopulmonary Neuroendocrine Tumors/Neoplasms.

Background: Identification of circulating tumor markers for clinical management in bronchopulmonary (BP) neuroendocrine tumors/neoplasms (NET/NEN) is of considerable clinical interest. Chromogranin A (CgA), a "universal" NET biomarker, is considered controversial as a circulating biomarker of BPNEN.

Aim: Assess utility of CgA in the diagnosis and management of BPNEN in a multicentric study.

Measurement of circulating transcript levels (NETest) to detect disease recurrence and improve follow-up after curative surgical resection of well-differentiated pancreatic neuroendocrine tumors.

Background: Recurrence of pancreatic neuroendocrine tumors (pNET) after surgery is common. Strategies to detect recurrence have limitations. We investigated the role of clinical criteria and the multigene polymerase chain reaction-based NETest during post-operative follow-up of pNET.

The NETest: The Clinical Utility of Multigene Blood Analysis in the Diagnosis and Management of Neuroendocrine Tumors.

The neuroendocrine neoplasms test (NETest) is a multianalyte liquid biopsy that measures neuroendocrine tumor gene expression in blood. This unique signature precisely defines the biological activity of an individual tumor in real time. The assay meets the 3 critical requirements of an optimal biomarker: diagnostic accuracy, prognostic value, and predictive therapeutic assessment.

PRRT genomic signature in blood for prediction of Lu-octreotate efficacy.

Background: Peptide receptor radionuclide therapy (PRRT) utilizes somatostatin receptor (SSR) overexpression on neuroendocrine tumors (NET) to deliver targeted radiotherapy. Intensity of uptake at imaging is considered related to efficacy but has low sensitivity. A pretreatment strategy to determine individual PRRT response remains a key unmet need.

A liquid biopsy for bronchopulmonary/lung carcinoid diagnosis.

No effective blood biomarker exists to detect and clinically manage bronchopulmonary (BP) neuroendocrine tumors (NET). We developed a blood-based 51 NET-specific transcript set for diagnosis and monitoring and evaluated clinical performance metrics. It accurately diagnosed the tumor and differentiated stable from progressive disease as determined by RECIST criteria.

Molecular strategies in the management of bronchopulmonary and thymic neuroendocrine neoplasms.

Thoracic NETs [bronchopulmonary NETs (BPNETs) and thymic NETs (TNET)] share a common anatomic primary location, likely a common cell of origin, the "Kulchitsky cell" and presumably, a common etiopathogenesis. Although they are similarly grouped into well-differentiated [typical carcinoids (TC) and atypical carcinoids (AC)] and poorly differentiated neoplasms and both express somatostatin receptors, they exhibit a wide variation in clinical behavior. TNETs are more aggressive, are frequently metastatic, and have a lower 5-year survival rate (~50% .