Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography
Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography
复制标题
DOI：
10.1111/j.1600-0846.2008.00304.x
复制
发表时间：
2008-08-01
影响因子：
2.2
通讯作者：
Jemec, Gregor B. E.
中科院分区：
文献类型：
--
作者：
Jorgensen, Thomas Martini;Tycho, Andreas;Jemec, Gregor B. E.
背景/目的：许多出版物表明，光学相干断层扫描（OCT）具有非侵入性诊断皮肤癌的潜力。目前，个别诊断特征似乎没有足够的歧视性。然而，几个功能的组合使用可能是useful.Methods：OCT是基于红外光，光子学和纤维光学。所用系统的轴向分辨率为10 μ m，横向分辨率为20 μ m。我们研究了基底细胞癌（BCC）和光化性角化病（AK）的几种OCT特征的联合使用。我们研究了连续34例患者的BCC（41例）和AK（37例）病变。的组合功能的诊断准确性进行了评估，使用机器学习tool.Results：正常皮肤的OCT图像通常表现出分层结构，不存在于病变成像。基底细胞癌表现为与基底细胞样岛相对应的暗球，而角化细胞癌表现为与角化过度相对应的白色斑点和条纹。OCT形态的差异不足以通过肉眼区分BCC和AK。机器学习分析表明，当使用多种功能时，正确的分类准确率为73%（AK）和81%（BCC）achieved.Conclusion：从个人OCT扫描提取的数据包括定量和定性的措施，并在目前的分辨率水平，这些单一因素似乎不足以诊断。我们的方法表明，当组合使用时，可以从OCT图像的整体结构中提取诊断数据，并具有合理的诊断准确性。
Background/purpose: A number of publications have suggested that optical coherence tomography (OCT) has the potential for non-invasive diagnosis of skin cancer. Currently, individual diagnostic features do not appear sufficiently discriminatory. The combined use of several features may however be useful.Methods: OCT is based on infrared light, photonics and fibre optics. The system used has an axial resolution of 10 mu m, lateral 20 mu m. We investigated the combined use of several OCT features from basal cell carcinomas (BCC) and actinic keratosis (AK). We studied BCC (41) and AK (37) lesions in 34 consecutive patients. The diagnostic accuracy of the combined features was assessed using a machine-learning tool.Results: OCT images of normal skin typically exhibit a layered structure, not present in the lesions imaged. BCCs showed dark globules corresponding to basaloid islands and AKs showed white dots and streaks corresponding to hyperkeratosis. Differences in OCT morphology were not sufficient to differentiate BCC from AK by the naked eye. Machine-learning analysis suggests that when a multiplicity of features is used, correct classification accuracies of 73% (AK) and 81% (BCC) are achieved.Conclusion: The data extracted from individual OCT scans included both quantitative and qualitative measures, and at the current level of resolution, these single factors appear insufficient for diagnosis. Our approach suggests that it may be possible to extract diagnostic data from the overall architecture of the OCT images with a reasonable diagnostic accuracy when used in combination.