Malignant melanoma poses a clinical diagnostic pro-blem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic tech-nology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histo-pathological analysis. A deep neural network algorithm was trained twice to distinguish between histopatho-logically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Further-more, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasive melanoma, 88 melanoma in situ, 115 dysplastic naevi, and 48 non-dysplastic naevi. The study included a training set of 358,800 pixels and a vali-dation set of 7,313 pixels, which was then tested with a training set of 24,375 pixels. The majority vote classification achieved high overall sensitivity of 95% and a specificity of 92% (95% confidence interval (95% CI) 0.024–0.029) in differentiating malignant from benign lesions. In the pixel-wise classification, the overall sensitivity and specificity were both 82% (95% CI 0.005–0.005). When divided into 4 subgroups, the diagnostic accuracy was lower. Hyperspectral imaging provides high sensitivity and specificity in distinguis-hing between naevi and melanoma. This novel method still needs further validation.
|Number of pages||7|
|Publication status||Published - 2022|
|MoE publication type||A1 Journal article-refereed|
Bibliographical notePublisher Copyright:
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Fields of Science
- hyperspectral imaging
- machine learning
- malignant melanoma
- non-invasive diagnostic
- 3121 General medicine, internal medicine and other clinical medicine