Concordance analysis of dermoscopic features between five observers in a sample of 200 dermoscopic images

Rodríguez‐Lomba, Enrique; Lozano-Masdemont, Belén; Nieto‐Benito, Lula María; Torre, Elisa Hernández de la; Suárez‐Fernández, R.; Avilés-Izquierdo, J.A.

doi:10.1016/j.abd.2020.12.010

Cited by 1 publication

(4 citation statements)

References 5 publications

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“…17 These explain why so many structures and vascular patterns suggestive of melanoma have been described in recent years. [11][12][13][14][15][16] In our study, almost all melanomas presented at least one melanoma-specific structure, and 82.5% had two or more. In 2016, a group of researchers from the International Dermoscopy Society (IDS) the largest comparative study between dermoscopic algorithms to date.…”

Section: Discussionsupporting

confidence: 48%

“…The identification of melanoma‐specific dermoscopic structures is a common basis in almost all these algorithms, 5–8 but some melanomas might not present any dermoscopic‐specific criteria 17 . These explain why so many structures and vascular patterns suggestive of melanoma have been described in recent years 11–16 . In our study, almost all melanomas presented at least one melanoma‐specific structure, and 82.5% had two or more.…”

Section: Discussionmentioning

confidence: 62%

“…Interobserver agreement analysis revealed that the identification of colors and asymmetries presented a higher concordance rate than most of the dermoscopic structures. 7,14 Previous studies have determined that features regarding the overall organization, colors, and symmetries have a higher agreement and discriminatory power than many well-known diagnostic structures such as atypical pigment network or irregular blotches. [6][7][8] Some dermoscopic algorithms, such as CASH and TADA algorithms, are based on this idea.…”

Section: Discussionmentioning

confidence: 99%

“…To develop this algorithm, we followed different steps. First, in order to simplify the execution of the algorithm and lower the interobserver variability, the evaluation in each dermoscopy image was restricted to the presence or absence of melanoma‐specific structures with the highest interobserver agreement (Kappa coefficient ≥0.30) 14 . Those included: polychromy, defined as the presence of ≥3 colors (light brown, dark brown, black, blue/gray, red/pink, white); asymmetry of color and/or structures in one or two axes; and the presence of the following melanoma‐specific dermoscopic structures: atypical pigmented network, irregular globules, irregular blotches, streaks, negative pigment network, regression structures, blue‐white veil, shiny white streaks, milky red areas, pseudolacunae, rainbow pattern, and irregular vessels.…”

Section: Methodsmentioning

confidence: 99%

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Do not PASS any melanoma without diagnosis: a new simplified dermoscopic algorithm

et al. 2023

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Introduction Dermoscopic algorithms for melanoma diagnosis could be time‐expending, and their reliability in daily practice lower than expected. Objective To propose a simplified dermoscopic algorithm for melanoma diagnosis. Material and methods A multicenter retrospective analysis of 1,120 dermoscopic images of atypical melanocytic tumors (320 melanomas and 800 non‐melanomas) was performed. An algorithm based on polychromia, asymmetry in colors or structures, and some melanoma‐specific structures was designed. Univariate and multivariate logistic regression analysis was calculated to estimate the coefficients of each potential predictor for melanoma diagnosis. A score was developed based on the dermoscopic evaluations performed by four experts blinded to histological diagnosis. Results Most melanomas had ≥3 colors (280; 84.5%), asymmetry in colors or structures (289; 90.3%), and at least one melanoma‐specific structure (316; 98.7%). PASS score ≥3 had a 91.9% sensibility, 87% specificity, and 88.4% diagnostic accuracy for melanoma. PASS algorithm showed an area under the curve (AUC) of 0.947 (95% CI 0.935–0.959). Limitations This study was retrospective. A comparison between the performances of different dermoscopic algorithms is difficult because of their designs. Conclusion PASS algorithm showed a very good diagnostic accuracy, independently of the observers' experience, and it seems easier to perform than previous dermoscopic algorithms.

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