基于显微多光谱成像的临床皮肤鳞癌病理分析

Pathological Analysis of Cutaneous Squamous Cell Carcinoma Based on Multispectral Microscopic Imaging

皮肤鳞状细胞癌(cSCC)是我国三大皮肤恶性肿瘤之一,病理学诊断目前仍是其临床诊断的金标准。为了提升诊断效率以及降低人工劳动强度,本课题组自主研发了一套自动多光谱显微成像系统。利用该系统获取人体正常皮肤组织与鳞癌组织全切片的多光谱病理图像,运用随机森林算法与自适应阈值分割法对不同波段的图像进行精确分割并作伪彩色处理,有效地提取出细胞核、大脂滴和角化珠等重要的诊断信息。此外,利用滑动窗口技术设置200×200像素的窗口,计算窗口内的核质比。结果表明,多光谱病理图像经伪彩色处理后显著增强了病理结构的可视化。经量化分析得到正常皮肤组织与cSCC组织的核质比存在显著的统计学差异(P<0.001)。核质比量化的ROC曲线分析也证明了该系统的敏感性与特异性。实验结果表明,该自动多光谱显微成像系统结合图像处理技术,增强了临床病理信息的可视化和量化能力,为提升病理分析的效率和准确率、降低病理诊断的复杂性提供了坚实的技术基础。

Objective 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.Methods 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.Results and Discussions 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.Conclusions 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.