Semi-automated non-invasive diagnostics method for melanoma differentiation from nevi and pigmented basal cell carcinomas

Lihacova, Ilze; Boločko, Katrina; Lihachev, Alexey

doi:10.1117/12.2295773

Cited by 5 publications

(4 citation statements)

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“…We built in an additional first step into the algorithm to exclude nevi from the analysis using parameter s’. Parameter s’ is an improved formula based on our previous findings to differentiate melanoma from nevi [ 40 , 41 , 42 , 43 ]. It utilizes the intensity values of the lesion and the surrounding skin in G and R channels to calculate a predictive value.…”

Section: Discussionmentioning

confidence: 99%

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Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

Bozsányi

Varga

Farkas

et al. 2021

JCM

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Breslow thickness is a major prognostic factor for melanoma. It is based on histopathological evaluation, and thus it is not available to aid clinical decision making at the time of the initial melanoma diagnosis. In this work, we assessed the efficacy of multispectral imaging (MSI) to predict Breslow thickness and developed a classification algorithm to determine optimal safety margins of the melanoma excision. First, we excluded nevi from the analysis with a novel quantitative parameter. Parameter s’ could differentiate nevi from melanomas with a sensitivity of 89.60% and specificity of 88.11%. Following this step, we have categorized melanomas into three different subgroups based on Breslow thickness (≤1 mm, 1–2 mm and >2 mm) with a sensitivity of 78.00% and specificity of 89.00% and a substantial agreement (κ = 0.67; 95% CI, 0.58–0.76). We compared our results to the performance of dermatologists and dermatology residents who assessed dermoscopic and clinical images of these melanomas, and reached a sensitivity of 60.38% and specificity of 80.86% with a moderate agreement (κ = 0.41; 95% CI, 0.39–0.43). Based on our findings, this novel method may help predict the appropriate safety margins for curative melanoma excision.

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Section: Discussionmentioning

confidence: 99%

“…We used a novel parameter to exclude nevi as the first step of the melanoma classification algorithm. Parameter s ’ is based on our previous studies [ 40 , 41 , 42 , 43 ].

where I G : intensity of lesion in green channel,…”

Section: Methodsmentioning

confidence: 99%

Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

Bozsányi

Varga

Farkas

et al. 2021

JCM

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“…The sensitivity of the determination of melanoma was 90%, and specificity was 84%. However, it is important to note that described above results [24][25][26] were obtained on a small number of samples (only five-ten melanomas were included in the studied cohort). Evolution of the proposed technique may be based on the approach of spatial evaluation of diagnostic dermatoscopic signs, and for example, may be implemented with convolutional neural networks analysis [27].…”

Section: Resultsmentioning

confidence: 95%

“…Considering alternative optical methods of the proposed approach, one may say that the most frequently used today method is multispectral analysis with additional spectral ranges or native fluorescence analysis. For example, it was proposed to use the spectral region up to 1000 nm and analyze the content of NADH/ NAD+ [24,25]. The total sensitivity and specificity for separation of MM from nevus and BCC were 97% and 96% respectively.…”

Section: Resultsmentioning

confidence: 99%

In vivo hyperspectral analysis of skin hemoglobin and melanin content for neoplasia detection

Bratchenko

Myakinin

Sherendak

et al. 2018

J-BPE

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We present results of main skin chromophores (melanin and hemoglobin) optical analysis. Analysis of 91 in vivo skin tissues (50 benign and 41 malignant) was performed in visible spectral region with hyperspectral imaging technique. To assess the malignancy of skin tissues we proposed two methods for calculating the integral index of tissue optical density and performed a comparison of their effectiveness and effectiveness of physician survey. As the main diagnostic feature, we propose to use data of integral optical index dispersion from the studied tissue and healthy tissue area. The results of skin tissues classification with discriminant analysis are presented. The possibility of the proposed approaches application in the clinical practice is shown.

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