Skin cancer detection by spectroscopic oblique-incidence reflectometry: classification and physiological origins
Skin cancer detection by spectroscopic oblique-incidence reflectometry: classification and physiological origins
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DOI：
10.1364/ao.43.002643
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发表时间：
2004-05-01
期刊：
影响因子：
1.9
通讯作者：
Wang, LHV
中科院分区：
文献类型：
--
作者：
Garcia-Uribe, A;Kehtarnavaz, N;Wang, LHV
对通过光谱斜入射反射法在体内从102个皮肤病变部位获取的数据进行了分析。参与的医生最初根据病变的黑素细胞状况将皮肤病变分为两个视觉上可区分的组。第1组由以下两个癌性和良性亚组组成：（1）基底细胞癌和鳞状细胞癌以及（2）良性光化性角化病、脂溢性角化病和疣。第2组由（1）发育不良痣和（2）良性普通痣组成。针对每组，设计了一种基于自助法的贝叶斯分类器，以区分良性组织与发育不良或癌性组织。然后使用遗传算法为每个分类器获取空间光谱特征的最有效组合。通过前瞻性盲法研究测试，这些分类器对第1组和第2组分别达到了100%和95%的统计准确率。定义了与细胞核大小、氧合血红蛋白浓度、脱氧血红蛋白浓度以及总血红蛋白浓度和氧饱和度相关的特性，以解释分类结果的成因。（C）2004美国光学学会。
Data obtained from 102 skin lesions in vivo by spectroscopic oblique-incidence reflectometry were analyzed. The participating physicians initially divided the skin lesions into two visually distinguishable groups based on the lesions' melanocytic conditions. Group 1 consisted of the following two cancerous and benign subgroups: (1) basal cell carcinomas and squamous cell carcinomas and (2) benign actinic keratoses, seborrheic keratoses, and warts. Group 2 consisted of (1) dysplastic nevi and (2) benign common nevi. For each group, a bootstrap-based Bayes classifier was designed to separate the benign from the dysplastic or cancerous tissues. A genetic algorithm was then used to obtain the most effective combination of spatiospectral features for each classifier. The classifiers, tested with prospective blind studies, reached statistical accuracies of 100% and 95% for groups 1 and 2, respectively. Properties that related to cell-nuclear size, to the concentration of oxyhemoglobin, and to the concentration of deoxyhemoglobin as well as the derived concentration of total hemoglobin and oxygen saturation were defined to explain the origins of the classification outcomes. (C) 2004 Optical Society of America.