Cutaneous squamous cell carcinoma (cSCC), also known as skin squamous cancer, is one of the three primary types of malignant skin tumors in China. The diagnosis of cSCC is primarily based on clinical features. Biopsy, excision, and histological confirmation should be performed for all clinically suspicious lesions to facilitate the prognostic classification and correct management of cSCC. This process often relies on prolonged microscopic examination by experienced pathologists. However, traditional microscopic imaging techniques, which primarily rely on RGB images, have a limited ability to provide additional dimensional information and demand high levels of expertise from physicians. Moreover, inconsistencies in staining standards across different laboratories may lead to uneven staining or overstaining, which could affect the uniformity of diagnostic outcomes. To improve the diagnostic accuracy of cSCC and reduce the labor intensity of pathologists, multispectral imaging (MSI) technology was used to analyze pathological slices from both cSCC and normal skin tissues.

Multispectral imaging (MSI) technology offers both spatial morphology and spectral information across various bands, captures more useful components or markers, and provides physicians with a richer diagnostic basis. In this study, we developed an automated multispectral microscopic imaging system based on narrowband LED illumination. The core of the system is composed of 13 narrow-band LED lighting units that cover a spectral range of 420?680 nm, which provides more diagnostic information than do traditional RGB imaging techniques. The system is equipped with a precision motorized translation stage that facilitates the automated and systematic scanning of tissue samples as well as the capture of clear images with a high-resolution CMOS camera. A software interface developed using the Qt framework offers an intuitive operational environment that allows adjustments to be made to the wavelength selection and exposure settings and also allows for real-time image modifications to ensure optimal image quality and diagnostic accuracy. The captured grayscale images span 13 spectral bands covering each lesion area. The image capture and processing stages include dark current and radiation correction as well as the use of an adaptive two-dimensional gamma function method to effectively correct uneven illumination, which thereby improves the image quality and contrast. The multispectral imaging system was applied to cSCC and normal skin tissue slices from Shanghai Ruijin Hospital, using scale-invariant feature transform (SIFT) technology for image stitching and segmentation. In the 600 nm band, an adaptive threshold algorithm was employed to segment large lipid droplets in normal tissues and keratin pearls and squamous eddies in cancerous tissues. In the 630 nm band, a random forest algorithm was used for the segmentation of cell nuclei. Furthermore, the segmented images underwent pseudocolor processing, and a sliding window technique with a 200×200 pixel window was used to calculate the nuclear-cytoplasmic ratio within the window.

The results indicate that the visualization of pathological structures is significantly enhanced by the pseudocolor processing of multispectral pathological images, which is crucial for distinguishing between cSCC and normal skin tissues. Additionally, by establishing a 200×200 pixel window for quantitative analysis, the nuclear-cytoplasmic ratio within the window can be calculated. A statistically significant difference in the nuclear-cytoplasmic ratio between normal skin tissues and cSCC tissues is revealed (P<0.001). The ROC curve for the quantification of the nuclear-cytoplasmic ratio demonstrates the sensitivity and specificity of the system. Using a qualitative analysis and quantitative statistics, a method is developed to reduce the subjective judgment in diagnostics and thereby offering insights into the detection of cSCC.

In this study, an automated multispectral microscopy imaging system is designed and established based on the results of a previous study. The system is equipped with a human-computer interaction interface that was designed using the Qt framework to achieve image acquisition and control functions. Compared with traditional visual inspection methods, this system captures images across multiple spectral bands and provides richer information on tissue states. Based on the results of the qualitative and quantitative analyses, the tremendous potential of multispectral imaging technology for distinguishing between normal and cancerous tissues is demonstrated. The system will not only reduce costs and manpower requirements but also address diagnostic inconsistencies caused by staining differences. This research will bring new perspectives and methods to the fields of skin pathology and cancer diagnosis, as it demonstrates the potential to achieve more efficient and high-quality diagnostics at lower costs. Future studies should focus on collecting more clinical data to validate the broad applicability of this technology.