Introduction: Cutaneous melanoma is the sixth most common malignant cancer in the USA. Among different subtypes of melanoma, nodular melanoma (NM) accounts about 14% of all cases but is responsible for more than 40% of melanoma deaths. Early diagnosis is the best method to improve melanoma prognosis. Unfortunately, early diagnosis of NM is particularly challenging given that patients often lack identifiable risk factors such as many moles or freckles. Moreover, early NM may mimic a range of benign skin lesions that are not routinely excised or biopsied in every day practice. For this reason, specific clinical and skin imaging clues have been proposed to improve early detection of NM.Areas covered: The review discusses about the noninvasive tools to diagnose thin melanoma, particularly NM. Expert commentary: Currently, dermatologists present a wide opportunity of diagnostic tools. Current data suggest that the early diagnosis of NM is a major challenge as the majority of early NM are symmetric, roundish, and lack specific pattern. Another promising strategy is based on recent data suggesting that artificial intelligence based on deep convolutional neural networking is able to outperform average dermatologist. Further research is necessary to validate the performance of this method in the real world and in the clinical setting.

Improving the early diagnosis of early nodular melanoma: can we do better?

Corneli P;Zalaudek I
;
Magaton-Rizzi G;di Meo N
2018-01-01

Abstract

Introduction: Cutaneous melanoma is the sixth most common malignant cancer in the USA. Among different subtypes of melanoma, nodular melanoma (NM) accounts about 14% of all cases but is responsible for more than 40% of melanoma deaths. Early diagnosis is the best method to improve melanoma prognosis. Unfortunately, early diagnosis of NM is particularly challenging given that patients often lack identifiable risk factors such as many moles or freckles. Moreover, early NM may mimic a range of benign skin lesions that are not routinely excised or biopsied in every day practice. For this reason, specific clinical and skin imaging clues have been proposed to improve early detection of NM.Areas covered: The review discusses about the noninvasive tools to diagnose thin melanoma, particularly NM. Expert commentary: Currently, dermatologists present a wide opportunity of diagnostic tools. Current data suggest that the early diagnosis of NM is a major challenge as the majority of early NM are symmetric, roundish, and lack specific pattern. Another promising strategy is based on recent data suggesting that artificial intelligence based on deep convolutional neural networking is able to outperform average dermatologist. Further research is necessary to validate the performance of this method in the real world and in the clinical setting.
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https://www.tandfonline.com/doi/abs/10.1080/14737140.2018.1507822?journalCode=iery20
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2928536
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