School of Optoelectronic Engineering, Xidian University, Xi'an 710071, China.
CAS Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China.
Biosensors (Basel). 2022 Sep 25;12(10):790. doi: 10.3390/bios12100790.
Skin cancer, a common type of cancer, is generally divided into basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM). The incidence of skin cancer has continued to increase worldwide in recent years. Early detection can greatly reduce its morbidity and mortality. Hyperspectral microscopic imaging (HMI) technology can be used as a powerful tool for skin cancer diagnosis by reflecting the changes in the physical structure and microenvironment of the sample through the differences in the HMI data cube. Based on spectral data, this work studied the staging identification of SCC and the influence of the selected region of interest (ROI) on the staging results. In the SCC staging identification process, the optimal result corresponded to the standard normal variate transformation (SNV) for spectra preprocessing, the partial least squares (PLS) for dimensionality reduction, the hold-out method for dataset partition and the random forest (RF) model for staging identification, with the highest staging accuracy of 0.952 ± 0.014, and a kappa value of 0.928 ± 0.022. By comparing the staging results based on spectral characteristics from the nuclear compartments and peripheral regions, the spectral data of the nuclear compartments were found to contribute more to the accurate staging of SCC.
皮肤癌是一种常见的癌症，一般分为基底细胞癌（BCC）、鳞状细胞癌（SCC）和恶性黑色素瘤（MM）。近年来，全球皮肤癌的发病率持续上升。早期发现可以大大降低其发病率和死亡率。高光谱显微成像（HMI）技术可以通过反映样本物理结构和微环境的差异，通过 HMI 数据立方体的差异，作为皮肤癌诊断的有力工具。基于光谱数据，本工作研究了 SCC 的分期识别以及感兴趣区域（ROI）的选择对分期结果的影响。在 SCC 分期识别过程中，最优结果对应于光谱预处理的标准正态变量变换（SNV）、降维的偏最小二乘（PLS）、数据集分区的保留方法和用于分期识别的随机森林（RF）模型，具有最高的分期准确性为 0.952 ± 0.014，kappa 值为 0.928 ± 0.022。通过比较基于核区和外周区的光谱特征的分期结果，发现核区的光谱数据对 SCC 的准确分期有更大的贡献。
