To evaluate the development stage of skin cancer accurately is very important for prompt treatment and clinical prognosis. In this paper, we used the FLIM system based on time-correlated single-photon counting (TCSPC) to acquire fluorescence lifetime images of skin tissues. In the cases of full sample data, three kinds of sample set partitioning methods, including bootstrapping method, hold-out method and K-fold cross-validation method, were used to divide the samples into calibration set and prediction set, respectively. Then the binary classification models for skin cancer were established based on random forest (RF), K-nearest neighbor (KNN),support vector machine (SVM) and linear discriminant analysis (LDA) respectively. The results showed that FLIM combining with appropriate machine learning algorithms can achieve early and advanced canceration classification of skin cancer, which could provide reference for the multi-classification, clinical staging and diagnosis of skin cancer.
Fluorescence lifetime imaging (FLIM) can reveal information about the spatial distribution of a fluorescent molecule together with information about its microenvironment, which makes FLIM find wide applications in biomedical research. In this paper, we implemented time-correlated single-photon counting-based fluorescence lifetime imaging microscopy (TCSPC-based FLIM) in skin cancer diagnosis, including malignant melanoma (MM), squamous cell carcinoma (SCC) and basal cell carcinoma (BCC). The fluorescence lifetime images and two-dimensional vector analysis of normal and several cancerous skin tissue sections stained with hematoxylin-eosin (H&E) were mapped and the results showed that cancerous skin tissue has a relatively shorter fluorescence lifetime compared with normal skin tissue. Moreover, the fluorescence lifetime of skin cancer tissue sections with different clinical stages was measured, and the quantitative relationship between fluorescence lifetime and pathological stage of skin cancer was explored.
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