There is a need for a noninvasive diagnostic method for early detection of basal cell carcinoma which is the most common type of cancer in the general population. Basal cell nevus syndrome is a rare autosomal dominant disorder that increases predisposition to basal cell carcinoma, with a lower average age of onset and a higher number of lesions. Autofluorescence and autofluorescence photobleaching imaging is a potential approach to early diagnosis and determining whether an aggressive form of basal cell carcinoma is present earlier, however, the mechanism is still not fully understood. Investigation of basal cell nevus syndrome associated basal cell carcinoma autofluorescence intensity and autofluorescence photobleaching kinetics could assist in early detection and assessment of basal cell carcinoma in general.
An imaging device with 405 nm LED illumination at power density 7 mW/cm2 was used for cutaneous autofluorescence excitation. Autofluorescence photobleaching was detected by imaging under continuous irradiation for 20 seconds. It was found that on average basal cell carcinoma in patients with basal cell nevus syndrome has a lower autofluorescence intensity at the first second of excitation, as well as smaller decrease in intensity after 20 seconds of irradiation compared to sporadic basal cell carcinoma. This may show that basal cell carcinoma in patients with basal cell nevus syndrome have a different composition of endogenous fluorophores than in sporadic cases which could be investigated in further research.
Rare diseases place a high burden on society as they are often associated with significant disability, potential years of life lost, high rate of hospitalization and admission to long-term care, high cost of illness and immense mortality rate. Effective screening approaches followed by verification with genetics analysis would be crucial for early diagnosis and treatment in order to prevent complications and decrease the disease burden. This study proposes development and clinical validation of rare skin diseases early screening System for identification of rare disease groups followed by multimodal spectroscopic dermoscopic evaluation. For image acquisition an imaging prototype was used utilizing 526 nm, 663 nm and 964 nm multispectral LEDs for diffuse reflectance imaging and 405 nm LEDs for autofluorescence excitation, as well as Nuance multispectral imaging camera with spectral range 450 – 950 nm. Spectral reflectance and autofluorescence images were analyzed to determine informative/efficient parameters suitable for identification and diagnostics of rare diseases.
In this work post-operative skin cancer scar evaluation with LED screening device has been described. The wavelength used for inducing autofluorescence (AF) of chromophores in the skin is 405nm. The green channel of the captured images is the best to calculate AF intensity ratio from the scar and the surrounding skin of 10 patients with healthy healing and scars with cancer recurrence. This non-invasive multispectral screening method can help dermato-oncologist to make a decision on evaluating if the scar is healing correctly and evaluate any pigmentation that could be suspected as a recurrent cancer.
Pseudoxanthoma elasticum and Fabry disease are rare multi-systemic diseases with characteristic skin manifestations. Their rarity and complicated diagnostic process causes disability, decreased quality of life, and increased medical costs for the patients before the diagnosis. Therefore, a new non-invasive diagnostic approach will be developed using multispectral imaging to analyze images of Pseudoxanthoma elasticum skin manifestations and angiokeratomas which are characteristic of Fabry disease. For image acquisition an imaging prototype will be used utilizing 526 nm, 663 nm and 964 nm multispectral LEDs for diffuse reflectance imaging and 405 nm LEDs for autofluorescence excitation, as well as Nuance camera with spectral range 450 – 950 nm. Spectral reflectance and autofluorescence images will be analyzed to determine informative/efficient parameters suitable for identification and diagnostics of rare diseases.
This work will describe the challenges involved in setting up automatic processing for a large differentiated data set. In this study, a multispectral (skin diffuse reflection images using 526nm (green), 663nm (red), and 964nm (infrared) illumination and autofluorescence (AF) image using 405 nm excitation) data set with 756 lesions (3024 images) was processed. Previously, using MATLAB software, finding markers, correctly segmenting images with dark edges and image alignment were the main causes of the problems in automatic data processing. To improve automatic processing and eliminate the use of licensed software, the latter was substituted with the open source Python environment. For more precise segmentation of skin markers and skin lesions, as well for image alignment, the processing of artificial neural networks was utilized. The resulting processing method solves most of the issues of the MATLAB script. However, for even more accurate results, it is necessary to provide more accurate ground-truth segmentation masks and generate more input data to increase the training image database by using data augmentation.
Skin cancer is the most common type of malignant tumors in humans. Early diagnosis is the key to successful surgical treatment. In this work we present a non-invasive screening tool for early stage detection of skin cancer and also for the evaluation of post-operative scars.
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