| 1. |
- Paoli, John, 1975, et al.
(författare)
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Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions.
- 2022
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Ingår i: Acta dermato-venereologica. - : Medical Journals Sweden AB. - 1651-2057 .- 0001-5555. ; 102
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Tidskriftsartikel (refereegranskat)abstract
- Malignant melanoma poses a clinical diagnostic problem, 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 technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis. A deep neural network algorithm was trained twice to distinguish between histopathologically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Furthermore, 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 validation 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 distinguishing between naevi and melanoma. This novel method still needs further validation.
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| 2. |
- Pölönen, Ilkka, et al.
(författare)
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Unsupervised Numerical Characterization in Determining the Borders of Malignant Skin Tumors from Spectral Imagery
- 2022
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Ingår i: Intelligent Systems, Control and Automation: Science and Engineering. Tuovinen T., Periaux J., Neittaanmäki P. (eds). - Cham : Springer. - 2213-8986 .- 2213-8994. - 9783030707873 ; , s. 153-176
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- For accurate removal of malignant skin tumors, it is crucial to assure the complete removal of the lesions. In the case of certain ill-defined tumors, it is clinically challenging to see the true borders of the tumor. In this paper, we introduce several computationally efficient approaches based on spectral imaging to guide clinicians in delineating tumor borders. First, we present algorithms that can be used effectively with simulated skin reflectance data. By using simulated data, we gain detailed information about the sensitivity of the different approaches and how variables defined by algorithms act in the skin model. Second, we demonstrate the performance of the algorithms with spectral images taken in-vivo and representing two types of skin cancers with ill-defined borders, namely lentigo maligna and aggressive basal cell carcinoma. The results can be used as a guideline for developing software for the fast delineation of skin cancers.
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| 3. |
- Räsänen, Janne, et al.
(författare)
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Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas: A Pilot Study.
- 2021
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Ingår i: Acta dermato-venereologica. - : Medical Journals Sweden AB. - 0001-5555 .- 1651-2057. ; 1010001-5555:2
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Tidskriftsartikel (refereegranskat)abstract
- Pigmented basal cell carcinomas can be difficult to distinguish from melanocytic tumours. Hyperspectral imaging is a non-invasive imaging technique that measures the reflectance spectra of skin in vivo. The aim of this prospective pilot study was to use a convolutional neural network classifier in hyperspectral images for differential diagnosis between pigment-ed basal cell carcinomas and melanoma. A total of 26 pigmented lesions (10 pigmented basal cell carcinomas, 12 melanomas in situ, 4 invasive melanomas) were imaged with hyperspectral imaging and excised for histopatho-logical diagnosis. For 2-class classifier (melano-cytic tumours vs pigmented basal cell carcinomas) using the majority of the pixels to predict the class of the whole lesion, the results showed a sensitivity of 100% (95% confidence interval 81-100%), specificity of 90% (95% confidence interval 60-98%) and positive predictive value of 94% (95% confidence interval 73-99%). These results indicate that a convolutional neural network classifier can differentiate melanocytic tumours from pigmented basal cell carcinomas in hyperspectral images. Further studies are warranted in order to confirm these preliminary results, using larger samples and multiple tumour types, including all types of melanocytic lesions.
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