Machine learning prediction of progressive subclinical myocardial dysfunction in moderate aortic stenosis

BackgroundModerate severity aortic stenosis (AS) is poorly understood, is associated with subclinical myocardial dysfunction, and can lead to adverse outcome rates that are comparable to severe AS. Factors associated with progressive myocardial dysfunction in moderate AS are not well described. Artificial neural networks (ANNs) can identify patterns, inform clinical risk, and identify features of importance in clinical datasets.MethodsWe conducted ANN analyses on longitudinal echocardiographic data collected from 66 individuals with moderate AS who underwent serial echocardiography at our institution. Image phenotyping involved left ventricular global longitudinal strain (GLS) and valve stenosis severity (including energetics) analysis. ANNs were constructed using two multilayer perceptron models. The first model was developed to predict change in GLS from baseline echocardiography alone and the second to predict change in GLS using data from baseline and serial echocardiography. ANNs used a single hidden layer architecture and a 70%:30% training/testing split.ResultsOver a median follow-up interval of 1.3 years, change in GLS (≤ or >median change) could be predicted with accuracy rates of 95% in training and 93% in testing using ANN with inputs from baseline echocardiogram data alone (AUC: 0.997). The four most important predictive baseline features (reported as normalized % importance relative to most important feature) were peak gradient (100%), energy loss (93%), GLS (80%), and DI < 0.25 (50%). When a further model was run including inputs from both baseline and serial echocardiography (AUC 0.844), the top four features of importance were change in dimensionless index between index and follow-up studies (100%), baseline peak gradient (79%), baseline energy loss (72%), and baseline GLS (63%).ConclusionsArtificial neural networks can predict progressive subclinical myocardial dysfunction with high accuracy in moderate AS and identify features of importance. Key features associated with classifying progression in subclinical myocardial dysfunction included peak gradient, dimensionless index, GLS, and hydraulic load (energy loss), suggesting that these features should be closely evaluated and monitored in AS

Quantitative Analysis of BTF3, HINT1, NDRG1 and ODC1 Protein Over-Expression in Human Prostate Cancer Tissue

Prostate carcinoma is the most common cancer in men with few, quantifiable, biomarkers. Prostate cancer biomarker discovery has been hampered due to subjective analysis of protein expression in tissue sections. An unbiased, quantitative immunohistochemical approach provided here, for the diagnosis and stratification of prostate cancer could overcome this problem. Antibodies against four proteins BTF3, HINT1, NDRG1 and ODC1 were used in a prostate tissue array (> 500 individual tissue cores from 82 patients, 41 case pairs matched with one patient in each pair had biochemical recurrence). Protein expression, quantified in an unbiased manner using an automated analysis protocol in ImageJ software, was increased in malignant vs non-malignant prostate (by 2-2.5 fold, p<0.0001). Operating characteristics indicate sensitivity in the range of 0.68 to 0.74; combination of markers in a logistic regression model demonstrates further improvement in diagnostic power. Triple-labeled immunofluorescence (BTF3, HINT1 and NDRG1) in tissue array showed a significant (p<0.02) change in co-localization coefficients for BTF3 and NDRG1 co-expression in biochemical relapse vs non-relapse cancer epithelium. BTF3, HINT1, NDRG1 and ODC1 could be developed as epithelial specific biomarkers for tissue based diagnosis and stratification of prostate cancer

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer

ROC of putative prostate cancer biomarkers.

<p>ROC curve demonstrates the discriminating performance of the protein expression in the differentiation between malignant and non-malignant tissue cores using the area fraction (probit) data for BTF3, HINT1, NDRG1 and ODC1. The operating characteristic values are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084295#pone.0084295.s005" target="_blank">Table S1</a>. The solid line represents the ROC area of 0.5. </p

Multi-labeled immunofluorescence for BTF3, HINT1 and NDRG1.

<p>Co-expression of three proteins BTF3 (FITC-green), HINT1 (Cy3- blue) and NDRG1 (Cy5-red) in non-malignant (A) and malignant prostate tissue cores (B); images are representative tissue cores from the tissue array with over 200 samples. The three antibodies were incubated simultaneously and labeled with three Cy5 –red different fluorophores using Bond automated staining system. Whole tissue cores were imaged using a Leica confocal system and 4x4 tiling at exactly the same settings for comparison. Images were zoomed 3x using digital zoom in ImageJ. Scale bar =100µm.</p