Search

With rising melanoma incidence and mortality, early detection and surgical removal of primary lesions is essential. Multispectral imaging is a new, non-invasive technique that can facilitate skin cancer detection by measuring the reflectance spectra of biological tissues. Currently, incident illumination allows little light to be reflected from deeper skin layers due to high surface reflectance. A pilot study was conducted at the University Hospital Basel to evaluate, whether multispectral imaging with direct light coupling could extract more information from deeper skin layers for more accurate dignity classification of melanocytic lesions. 27 suspicious pigmented lesions from 23 patients were included (6 melanomas, 6 dysplastic nevi, 12 melanocytic nevi, 3 other). Lesions were imaged before excision using a prototype snapshot mosaic multispectral camera with incident and direct illumination with subsequent dignity classification by a pre-trained multispectral image analysis model. Using incident light, a sensitivity of 83.3% and a specificity of 58.8% were achieved compared to dignity as determined by histopathological examination. Direct light coupling resulted in a superior sensitivity of 100% and specificity of 82.4%. Convolutional neural network classification of corresponding red, green, and blue lesion images resulted in 16.7% lower sensitivity (83.3%, 5/6 malignant lesions detected) and 20.9% lower specificity (61.5%) compared to direct light coupling with multispectral image classification. Our results show that incorporating direct light multispectral imaging into the melanoma detection process could potentially increase the accuracy of dignity classification. This newly evaluated illumination method could improve multispectral applications in skin cancer detection. Further larger studies are needed to validate the camera prototype., (© 2024. The Author(s).)

In the development and optimization of biotechnological cultivation processes the continuous monitoring through the acquisition and interpretation of spectral and morphological properties of bioparticles are challenging. There is therefore a need for the parallel acquisition and interpretation of spatially and spectrally resolved measurements with which particles can be characterized and classified in-flow with high throughput. Therefore, in this paper we investigated the scientific and technological connectivity of standard imaging flow cytometry (IFC) with filter-on-chip based spatially and spectrally resolving snapshot-mosaic cameras for photonic sensing and control in a smart and innovative microfluidic device. For the investigations presented here we used the microalgae Haematococcus pluvialis (HP). These microalgae are used commercially to produce the antioxidant keto-carotenoid astaxanthin. Therefore, HP is relevant to practically demonstrate the usability of the developed system for Multispectral Imaging Flow Cytometry (MIFC) platform. The extension of standard IFC with snapshot-mosaic cameras and multivariate data processing is an innovative approach for the in-flow characterization and derived classification of bioparticles. Finally, the multispectral data acquisition and the therefore developed methodology is generalizable and enables further applications far beyond the here characterized population of HP cells.