Support Vector Machine Classification of Nonmelanoma Skin Lesions Based on Fluorescence Lifetime Imaging Microscopy
Support Vector Machine Classification of Nonmelanoma Skin Lesions Based on Fluorescence Lifetime Imaging Microscopy
复制标题
基于荧光寿命成像显微镜的非黑色素瘤皮肤病变支持向量机分类
DOI：
10.1021/acs.analchem.9b01866
复制
发表时间：
2019-08-20
影响因子：
7.4
通讯作者：
Qu, Junle
中科院分区：
文献类型：
--
作者：
Chen, Bingling;Lu, Yuan;Qu, Junle
恶性皮肤病变的早期诊断对于皮肤癌的及时治疗和临床预后至关重要。然而，很难精确地评估非黑色素瘤皮肤癌的发展阶段，因为它们源自相同的组织，这是皮肤表皮层中异常鳞状角质细胞不受控制生长的结果。在本研究中，我们开发了一个线性核支持向量机（LSVM）模型来区分基底细胞癌（BCC）与光化性角化病（AK）和鲍文病（BD）。LSVM模型的输入参数包括适当的寿命分量和熵值，这是从苏木精和伊红（H&E）染色的活检切片的双光子荧光寿命成像中提取的。不同的功能作为输入的支持向量机训练进行了比较和评估。在构建SVM模型时，发现从第二组分的寿命（tau（2））获得的特征在诊断准确性、灵敏度和特异性方面比平均荧光寿命（tau（m））显著更具有预测性。上述结果证实了诊断模型的受试者工作特征（ROC）曲线的基础上。将Shannon熵作为一个独立的特征加入到SVM模型中，进一步提高了诊断的准确性。因此，荧光寿命分析和熵计算可以为皮肤肿瘤疾病的准确检测提供高度信息化的特征。总之，荧光寿命成像显微镜（FLIM）结合SVM分类显示出巨大的潜力，开发一个有效的计算机辅助诊断标准和准确的癌症检测在皮肤科。
Early diagnosis of malignant skin lesions is critical for prompt treatment and a clinical prognosis of skin cancers. However, it is difficult to precisely evaluate the development stage of nonmelanoma skin cancers because they are derived from the same tissues as a result of the uncontrolled growth of abnormal squamous keratinocytes in the epidermis layer of the skin. In the present study, we developed a linear-kernel support vector machine (LSVM) model to distinguish basal cell carcinoma (BCC) from actinic keratosis (AK) and Bowen's disease (BD). The input parameters of the LSVM model consist of appropriate lifetime components and entropy values, which were extracted from two-photon fluorescence lifetime imaging of hematoxylin and eosin (H&E)-stained biopsy sections. Different features used as inputs for SVM training were compared and evaluated. In constructing the SVM models, features obtained from the lifetime (tau(2)) of the second component were found to be significantly more predictive than the average fluorescence lifetime (tau(m)) in terms of diagnostic accuracy, sensitivity, and specificity. The above findings were confirmed on the basis of the receiver operating characteristic (ROC) curves of diagnostic models. Shannon entropy was added to the SVM models as an independent feature to further improve the diagnostic accuracy. Therefore, fluorescence lifetime analysis and entropy calculations can provide highly informative features for the accurate detection of skin neoplasm disorders. In summary, fluorescence lifetime imaging microscopy (FLIM) combined with the SVM classification exhibited great potential for developing an effective computer-aided diagnostic criterion and accurate cancer detection in dermatology.