The erythema Q-score, an imaging biomarker for redness in skin inflammation

Abstract

Physician rating of cutaneous erythema is central to clinical dermatological assessment as well as quantification of outcome measures in clinical trials in a number of dermatologic conditions. However, issues with inter-rater reliability and variability in the setting of higher Fitzpatrick skin types make visual erythema assessment unreliable. We developed and validated a computer-assisted image-processing algorithm (EQscore) to reliably quantify erythema (across a range of skin types) in the dermatology clinical setting. Our image processing algorithm evaluated erythema based upon green light suppression differentials between affected and unaffected skin. A group of four dermatologists used a 4-point Likert scale as a human evaluation of similar erythematous patch tests. The algorithm and dermatologist scores were compared across 164 positive patch test reactions. The intra-class correlation coefficient of groups and the correlation coefficient between groups were calculated. The EQscore was validated on and independent image set of psoriasis, minimal erythema dose testing and steroid-induced blanching images. The reliability of the erythema quantification method produced an intra-class correlation coefficient of 0.84 for the algorithm and 0.67 for dermatologists. The correlation coefficient between groups was 0.85. The EQscore demonstrated high agreement with clinical scoring and superior reliability compared with clinical scoring, avoiding the pitfalls of erythema underrating in the setting of pigmentation. The EQscore is easily accessible (http://lab.rockefeller.edu/krueger/EQscore), user-friendly, and may allow dermatologists to more readily and accurately rate the severity of dermatological conditions and the response to therapeutic treatments.

Keywords: biomarkers; inflammation; inflammatory skin diseases.

FIGURE 1

Sample EQscore analyses. The original images for a severe reaction from mascara and mild reaction from dust mite. The rectangular regions, set by 4 user coordinate inputs, show the technician‐generated choice areas for the gradient‐containing and normal regions. The text above the images was not available to the technicians during review. The EQ score of each lesion is output to the display box above each image, illustrating the online application resource provided

FIGURE 2

Correlation between human and computer metrics. (A) Each blue data point plots the mean erythema score assigned by the group of four dermatologists as a function of the mean erythema score assigned by the group of four technicians using the semi‐automated technique. While any value was possible for the continuous EQCI, the EQHI was the mean of 4 integers that were strongly corelated (due to the visual sensory cue being the same driving the cue), so they appear in increments of 0.25. The blue curve, which is the second‐degree polynomial fit, shows that a polynomial of order 2 fits the data better than a linear fit (red line). The correlation between the EQHI and EQCIs scores is characterized by the correlation coefficient r = 0.88, while the inter‐observer agreement is characterized by the inter‐class correlation agreement ICC = 0.64. These data show that although the EQHI does not agree with the EQCIs, with only AP = 45% of the EQHI scores agreeing with the EQCIs, the EQCIs can be used to predict the EQHI using the second order polynomial. B, Correlation between the erythema Q‐score (EQscore), which is a transformation of the EQCIs, and erythema quantification human index. All the lesions that received each EQHI index (eg. green oval marks those that received EQHI = 2) were stratified into the per cent that received each integer‐discretized EQscore. The example highlighted (green oval) shows that 82% of the lesions that received a mean EQHI = 2 across the 4 expert dermoscopists also received an integer‐discretized mean EQscore of 2 across the technicians, and thus were in agreement. The overall fraction of agreements, where was AP = 76% and the inter‐class agreement coefficient was ICC = 0.88

FIGURE 3

EQscore on minimal erythema dose (MED ²¹ ) tests by processing published figure images. The chart shows the results of our QEscore algorithm versus images from the literature ¹⁶ , ¹⁷ , ¹⁸ , ¹⁹ . Each literature image leads to a color‐coded data set. Vertical dashed coloured lines indicated MED stated in the literature. The x‐axis for the Bodakaer data isD = 100 times the standard erythema dose (SED) such thatD = 100 × SED for the purposes of graphing the data together. The slope of the linear fit is an imaging biomarker that quantifies the incremental erythema per incremental light dose and quantitates, for instance, sunburn risk.

FIGURE 4

EQscore of Psoriatic Skin (A) and Topical Steroid applied skin (B). A, Psoriatic lesion analysed with three different choices of the gradient region, illustrating that within a lesion, there can be different erythema severity regions. B, Topical steroid application of a pea‐sized amount of clobetasol 0.05% in three locations at 4 h . The EQscore analysis was executed with the “Inverter” (see Figure 1) button selected, thereby achieving a quantitative measurement of blanching caused by the agent