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首页> 外文期刊>Machine Vision and Applications >Automated morphological classification of lung cancer subtypes using H&E tissue images
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Automated morphological classification of lung cancer subtypes using H&E tissue images

机译：使用H＆E组织图像对肺癌亚型进行自动形态分类

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Patient-targeted therapies have recently been highlighted as important. An important development in the treatment of metastatic non-small cell lung cancer (NSCLC) has been the tailoring of therapy on the basis of histology. A pathology diagnosis of "non-specified NSCLC" is no longer routinely acceptable; an effective approach for classification of adenocarcinoma (AC) and squamous carcinoma (SC) his-totypes is needed for optimizing therapy. In this study, we present a robust and objective automatic computer vision system for real-time classification of AC and SC based on the morphological tissue patterns of hematoxylin and eosin (H&E) staining images to assist medical experts in the diagnosis of lung cancer. Various original and extended densit-ometric and Haralick's texture features are used to extract image features, and a boosting algorithm is utilized to train the classifier, together with alternative decision tree as the base learner. For evaluation, two types of data with 653 tissue samples were tested, including 369 samples from tissue microarray data set and 284 samples from full-face tissue sections. Regarding the data distribution, 45 % are AC samples (288) and 55 % are SC samples (365), which is considerably well balanced for each class. Using tenfold cross-validation, the technique achieved high accuracy of 92.41 % on tissue microarray cores and 95.42 % on full tissue sections. We also found that the two boosting algorithms (cw-Boost and AdaBoost.M l) perform consistently well in comparison with other popularly adopted machine learning methods, including support vector machine, neural network and decision tree. This approach offers a robust, objective and rapid procedure for optimized patient-targeted therapy.
机译：以患者为目标的疗法最近已被强调为重要。转移性非小细胞肺癌（NSCLC）治疗的一个重要进展是根据组织学调整治疗方案。不再常规接受“非特定NSCLC”的病理诊断。需要一种有效的分类腺癌（AC）和鳞癌（SC）组织型的方法，以优化治疗方法。在这项研究中，我们基于苏木精和曙红（H＆E）染色图像的形态组织模式，提出了一种功能强大且客观的自动计算机视觉系统，用于AC和SC的实时分类，以协助医学专家诊断肺癌。各种原始的和扩展的光密度法以及Haralick的纹理特征都用于提取图像特征，并且使用了增强算法来训练分类器，同时将替代决策树作为基础学习者。为了评估，测试了653种组织样本的两种类型的数据，包括来自组织微阵列数据集的369个样本和来自全脸组织切片的284个样本。关于数据分布，AC样本（288）占45％，SC样本​​（365）占55％，这对于每个类别而言都相当均衡。使用十倍交叉验证，该技术在组织微阵列核心上的准确度达到92.41％，在完整组织切片上的准确度达到95.42％。我们还发现，与其他广泛采用的机器学习方法（包括支持向量机，神经网络和决策树）相比，这两种增强算法（cw-Boost和AdaBoost.M l）的性能始终很好。这种方法为优化以患者为目标的治疗方法提供了可靠，客观和快速的程序。

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