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Automated, computer-guided PASI measurements by digital image analysis versus conventional physicians' PASI calculations: study protocol for a comparative, single-centre, observational study.

ABSTRACT: INTRODUCTION:Reliable and accurate assessment of severity in psoriasis is very important in order to meet indication criteria for initiation of systemic treatment or to evaluate treatment efficacy. The most acknowledged tool for measuring the extent of psoriatic skin changes is the Psoriasis Area and Severity Index (PASI). However, the calculation of PASI can be tedious and subjective and high intraobserver and interobserver variability is an important concern. Therefore, there is a great need for a standardised and objective method that guarantees a reproducible PASI calculation. Within this study we will investigate the precision and reproducibility of automated, computer-guided PASI measurements in comparison to trained physicians to address these limitations. METHODS AND ANALYSIS:Non-interventional analyses of PASI calculations by either physicians in a prospective versus retrospective setting or an automated computer-guided algorithm in 120 patients with plaque psoriasis. All retrospective PASI calculations by physicians or by the computer algorithm are based on total body digital images. The primary objective of this study is comparison of automated computer-guided PASI measurements by means of digital image analysis versus conventional, prospective or retrospective physicians' PASI assessments. Secondary endpoints include (1) the assessment of physicians' interobserver variance in PASI calculations, (2) the assessment of physicians' intraobserver variance in PASI assessments of the same patients' images after a time interval of at least 4 weeks, (3) the assessment of the deviation between physicians' prospective versus retrospective PASI calculations, and (4) the reproducibility of automated computer-guided PASI measurements by assessment of two sets of total body digital images of the same patients taken at one time point. ETHICS:Ethical approval was provided by the Ethics Committee of the Medical Faculty of the University of Heidelberg (ethics approval number S-379/2016). TRIAL REGISTRATION NUMBER:DRKS00011818; Results.

SUBMITTER: Fink C

PROVIDER: S-EPMC5961567 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Automated, computer-guided PASI measurements by digital image analysis versus conventional physicians' PASI calculations: study protocol for a comparative, single-centre, observational study.

Fink Christine C Uhlmann Lorenz L Klose Christina C Haenssle Holger A HA

BMJ open 20180517 5

<h4>Introduction</h4>Reliable and accurate assessment of severity in psoriasis is very important in order to meet indication criteria for initiation of systemic treatment or to evaluate treatment efficacy. The most acknowledged tool for measuring the extent of psoriatic skin changes is the Psoriasis Area and Severity Index (PASI). However, the calculation of PASI can be tedious and subjective and high intraobserver and interobserver variability is an important concern. Therefore, there is a grea ...[more]

PMID: 29773695

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Project description:Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features' variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification.

Dataset Information

Automated, computer-guided PASI measurements by digital image analysis versus conventional physicians' PASI calculations: study protocol for a comparative, single-centre, observational study.

Publications

Automated, computer-guided PASI measurements by digital image analysis versus conventional physicians' PASI calculations: study protocol for a comparative, single-centre, observational study.

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