Melanoma Epidemiology, Staging and Prognostic Factors

Almashali, Mohammed; Ellis, Robert; Paragh, György

doi:10.1007/978-3-030-27400-9_4

Cited by 3 publications

(7 citation statements)

References 48 publications

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“…Melanoma is a malignant melanocytic tumor that accounts for the majority of the skin cancer-related mortality [ 1 , 2 , 3 ]. Approximately 232,100 new invasive melanomas are diagnosed worldwide, and melanoma accounts for more than 55,000 deaths annually [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

Bozsányi

Varga

Farkas

et al. 2021

JCM

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“…In addition to determining histological types of melanoma, pathologists also make observations that bear prognostic significance. The predictive histological features include the presence of ulceration, mitotic rate, lymphovascular invasion, microsatellites, neurotropism, and tumor-infiltrating lymphocytes [ 6 , 7 , 8 ]. Despite many years of using these methods as the standard of care, there is still much room for improvement [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma

Grant

Andrew

Alvarez

et al. 2022

Cancers

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Melanoma is among the most devastating human malignancies. Accurate diagnosis and prognosis are essential to offer optimal treatment. Histopathology is the gold standard for establishing melanoma diagnosis and prognostic features. However, discrepancies often exist between pathologists, and analysis is costly and time-consuming. Deep-learning algorithms are deployed to improve melanoma diagnosis and prognostication from histological images of melanoma. In recent years, the development of these machine-learning tools has accelerated, and machine learning is poised to become a clinical tool to aid melanoma histology. Nevertheless, a review of the advances in machine learning in melanoma histology was lacking. We performed a comprehensive literature search to provide a complete overview of the recent advances in machine learning in the assessment of melanoma based on hematoxylin eosin digital pathology images. In our work, we review 37 recent publications, compare the methods and performance of the reviewed studies, and highlight the variety of promising machine-learning applications in melanoma histology.

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“…In the absence of efficacious treatment, melanoma prognostic factors only predicted the inevitable. With the advent of targeted melanoma therapies and immunotherapy, predicting melanoma outcomes and treatment response became essential 2 . Although several novel prognostic factors and predictive tools have been described, one of the most critical melanoma prognostic factors remains ulceration 2 …”

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confidence: 99%

“…Melanoma incidence has risen rapidly over the past decades 1,2 . Although melanoma is the most lethal skin cancer, of the 106 100 people with invasive melanomas diagnosed annually in the USA, over 93·3% will survive at least 5 years 1 .…”

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confidence: 99%