Hyperspectral imaging reveals spectral differences and can distinguish malignant melanoma from pigmented basal cell carcinomas : A pilot study

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 pigmented 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 histopathological diagnosis. For 2-class classifier (melanocytic tumours vs pigmented basal cell carcinomas) using the majority of…

Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma

Ablative fractional laser-assisted photodynamic therapy for lentigo maligna: a prospective pilot study.

Background Lentigo maligna (LM) is an in‐situ form of melanoma carrying a risk of progression to invasive lentigo maligna melanoma (LMM). LM poses a clinical challenge, with subclinical extension and high recurrence rates after incomplete surgery. Alternative treatment methods have been investigated with varying results. Photodynamic therapy (PDT) with methylaminolaevulinate (MAL) has already proved promising in this respect. Objectives To investigate the efficacy of ablative fractional laser (AFL)‐assisted PDT with 5‐aminolaevulinic acid nanoemulsion (BF‐200 ALA) for treating LM. Methods In this non‐sponsored, prospective pilot study ten histologically verified LMs were treated with AFL‐as…

Hexyl aminolevulinate, 5‐aminolevulinic acid nanoemulsion and methyl aminolevulinate in photodynamic therapy of non‐aggressive basal cell carcinomas: A non‐sponsored, randomized, prospective and double‐blinded trial

Background In the photodynamic therapy (PDT) of non‐aggressive basal cell carcinomas (BCCs), 5‐aminolevulinic acid nanoemulsion (BF‐200ALA) has shown non‐inferior efficacy when compared with methyl aminolevulinate (MAL), a widely used photosensitizer. Hexyl aminolevulinate (HAL) is an interesting alternative photosensitizer. To our knowledge, this is the first study using HAL‐PDT in the treatment of BCCs. Objectives To compare the histological clearance, tolerability (pain and post‐treatment reaction), and cosmetic outcome of MAL, BF‐200 ALA, and low‐concentration HAL in the PDT of non‐aggressive BCCs. Methods Ninety‐eight histologically verified non‐aggressive BCCs met the inclusion criter…

Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network

In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery. peerReviewed

Unsupervised Numerical Characterization in Determining the Borders of Malignant Skin Tumors from Spectral Imagery

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 spectra…

Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions

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 invasi…

Convolutional neural networks in skin cancer detection using spatial and spectral domain

Skin cancers are world wide deathly health problem, where significant life and cost savings could be achieved if detection of cancer can be done in early phase. Hypespectral imaging is prominent tool for non-invasive screening. In this study we compare how use of both spectral and spatial domain increase classification performance of convolutional neural networks. We compare five different neural network architectures for real patient data. Our models gain same or slightly better positive predictive value as clinicians. Towards more general and reliable model more data is needed and collection of training data should be systematic. peerReviewed