Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters

Abstract

Dermatoscopy, high-frequency ultrasonography (HFUS) and spectrophotometry are promising quantitative imaging techniques for the investigation and diagnostics of cutaneous melanocytic tumors. In this paper, we propose the hybrid technique and automatic prognostic models by combining the quantitative image parameters of ultrasonic B-scan images, dermatoscopic and spectrophotometric images (melanin, blood and collagen) to increase accuracy in the diagnostics of cutaneous melanoma. The extracted sets of various quantitative parameters and features of dermatoscopic, ultrasonic and spectrometric images were used to develop the four different classification models: logistic regression (LR), linear discriminant analysis (LDA), support vector machine (SVM) and Naive Bayes. The results were compared to the combination of only two techniques out of three. The reliable differentiation between melanocytic naevus and melanoma were achieved by the proposed technique. The accuracy of more than 90% was estimated in the case of LR, LDA and SVM by the proposed method.

Investigating Serum and Tissue Expression Identified a Cytokine/Chemokine Signature as a Highly Effective Melanoma Marker

“…We therefore analyzed the entire panel of 27 cytokines/chemokines with the SVM machine learning algorithm, to investigate simultaneously all molecules as predictors of melanoma state. In other studies, SVM effectively discriminated melanoma on the basis of dermoscopic images [44], ultrasonic and spectrophotometric images [45], BRAF status [46], or dermo-fluorescence spectra [47], with a reported accuracy up to 90%. SVM was previously used for prognostic purposes in melanoma patients [48] but, to our knowledge, the present study is the first applying the SVM analysis to cytokine/chemokine-expression values to discriminate melanoma from controls, both in serum and in tissue, in a large group of controls and patients.…”

First clinical experience with a novel optical-ultrasound imaging device on various skin pathologies

“…Firstly, the combination of optical and ultrasound information has already been shown to increase the diagnostic accuracy of skin tumours. 14 Similarly, since ultrasound measures of skin inflammation have been shown to correlate well with the semi-quantitatively observed level of skin inflammation, 22,12,41 even to the extent of detecting subclinical inflammation, 11 the combined use of optical and ultrasound information may serve to provide better choices for personalized treatment.…”

“…7 Ultrasound imaging has the potential to play an important role in improving the detection, treatment planning and follow-up of the above-mentioned skin diseases with significant prevalence. [8][9][10][11][12][13] Combining the ultrasound information with optical imaging can further strengthen diagnostic accuracy, 14 since ultrasound information alone may not be enough for the diagnosis of skin diseases such as skin cancer. 15 1.2.…”

The Concept of AI-Based Algorithm: Analysis of CEUS Images and HSPs for Identification of Early Parenchymal Changes in Severe Acute Pancreatitis

“…To shorten the time of postprocessing (Tiwari et al, 2020), we are proposing the combined set of quantitative parameters (S h , S n , P ) extracted from the diagnostic images and perfusion dynamic curves to be used for the classification, instead of the whole CEUS images. The proposed list of quantitative parameters covers entropy, energy, contrast, correlation, energy from grayscale cooccurrence matrix, homogeneity, mean, standard deviation, RMS, variance, smoothness, kurtosis and skewness (Tiwari et al, 2020). Such parameters were successfully applied in our previous work for detection of skin cancer by analysing ultrasonic images (Tiwari et al, 2020).…”

“…The proposed list of quantitative parameters covers entropy, energy, contrast, correlation, energy from grayscale cooccurrence matrix, homogeneity, mean, standard deviation, RMS, variance, smoothness, kurtosis and skewness (Tiwari et al, 2020). Such parameters were successfully applied in our previous work for detection of skin cancer by analysing ultrasonic images (Tiwari et al, 2020).…”