Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload.

Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system\u27s use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications

Prediction-based classification for longitudinal biomarkers

Assessment of circulating CD4 count change over time in HIV-infected subjects on antiretroviral therapy (ART) is a central component of disease monitoring. The increasing number of HIV-infected subjects starting therapy and the limited capacity to support CD4 count testing within resource-limited settings have fueled interest in identifying correlates of CD4 count change such as total lymphocyte count, among others. The application of modeling techniques will be essential to this endeavor due to the typically nonlinear CD4 trajectory over time and the multiple input variables necessary for capturing CD4 variability. We propose a prediction-based classification approach that involves first stage modeling and subsequent classification based on clinically meaningful thresholds. This approach draws on existing analytical methods described in the receiver operating characteristic curve literature while presenting an extension for handling a continuous outcome. Application of this method to an independent test sample results in greater than 98% positive predictive value for CD4 count change. The prediction algorithm is derived based on a cohort of $n=270$ HIV-1 infected individuals from the Royal Free Hospital, London who were followed for up to three years from initiation of ART. A test sample comprised of $n=72$ individuals from Philadelphia and followed for a similar length of time is used for validation. Results suggest that this approach may be a useful tool for prioritizing limited laboratory resources for CD4 testing after subjects start antiretroviral therapy.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS326 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Evaluation of waist-to-height ratio to predict 5 year cardiometabolic risk in sub-Saharan African adults

Simple, low-cost central obesity measures may help identify individuals with increased cardiometabolic disease risk, although it is unclear which measures perform best in African adults. We aimed to: 1) cross-sectionally compare the accuracy of existing waist-to-height ratio (WHtR) and waist circumference (WC) thresholds to identify individuals with hypertension, pre-diabetes, or dyslipidaemia; 2) identify optimal WC and WHtR thresholds to detect CVD risk in this African population; and 3) assess which measure best predicts 5-year CVD riskPeer reviewedFinal Accepted Versio

PresenceAbsence: An R Package for Presence Absence Analysis

The PresenceAbsence package for R provides a set of functions useful when evaluating the results of presence-absence analysis, for example, models of species distribution or the analysis of diagnostic tests. The package provides a toolkit for selecting the optimal threshold for translating a probability surface into presence-absence maps specifically tailored to their intended use. The package includes functions for calculating threshold dependent measures such as confusion matrices, percent correctly classified (PCC), sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It also includes functions to plot the Receiver Operator Characteristic (ROC) curve and calculates the associated area under the curve (AUC), a threshold independent measure of model quality. Finally, the package computes optimal thresholds by multiple criteria, and plots these optimized thresholds on the graphs.

Meta-analysis of diagnostic accuracy studies in mental health

Objectives To explain methods for data synthesis of evidence from diagnostic test accuracy (DTA) studies, and to illustrate different types of analyses that may be performed in a DTA systematic review. Methods We described properties of meta-analytic methods for quantitative synthesis of evidence. We used a DTA review comparing the accuracy of three screening questionnaires for bipolar disorder to illustrate application of the methods for each type of analysis. Results The discriminatory ability of a test is commonly expressed in terms of sensitivity (proportion of those with the condition who test positive) and specificity (proportion of those without the condition who test negative). There is a trade-off between sensitivity and specificity, as an increasing threshold for defining test positivity will decrease sensitivity and increase specificity. Methods recommended for meta-analysis of DTA studies --such as the bivariate or hierarchical summary receiver operating characteristic (HSROC) model --jointly summarise sensitivity and specificity while taking into account this threshold effect, as well as allowing for between study differences in test performance beyond what would be expected by chance. The bivariate model focuses on estimation of a summary sensitivity and specificity at a common threshold while the HSROC model focuses on the estimation of a summary curve from studies that have used different thresholds. Conclusions Meta-analyses of diagnostic accuracy studies can provide answers to important clinical questions. We hope this article will provide clinicians with sufficient understanding of the terminology and methods to aid interpretation of systematic reviews and facilitate better patient care

Receiver Operating Characteristic (ROC) Analysis

Visual expertise covers a broad range of types of studies and methodologies. Many studies incorporate some measure(s) of observer performance or how well participants perform on a given task. Receiver Operating Characteristic (ROC) analysis is a method commonly used in signal detection tasks (i.e., those in which the observer must decide whether or not a target is present or absent; or must classify a given target as belonging to one category or another), especially those in the medical imaging literature. This frontline paper will review some of the core theoretical underpinnings of ROC analysis, provide an overview of how to conduct an ROC study, and discuss some of the key variants of ROC analysis and their applications