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Automatic Morphological Classification of Lung Cancer Subtypes with Boosting Algorithms for Optimizing Therapy

机译：使用优化算法的Boosting算法对肺癌亚型进行自动形态分类

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摘要

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) histotypes is needed for optimizing therapy. In this study, we present a robust and objective automatic classification system for real time classification of AC and SC based on morphological tissue pattern of H&E images alone to assist medical experts in diagnosis of lung cancer. Various original and extended Densitometric 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, 369 tissue samples were collected in tissue microarray format, including 97 adenocarcinoma and 272 squamous carcinoma samples. Using 10-fold cross validation, the technique achieved high accuracy of 92.41%, and we also found that the two Boosting algorithms (cw-Boost and AdaBoost.Ml) perform consistently well in comparison with other popularly adopted machine learning methods, including support vector machine, neural network, single decision tree and alternative decision tree. This approach offers a robust, objective and rapid procedure for optimized patient-targeted therapies.
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）和鳞状癌（SC）组织型的方法来优化治疗。在这项研究中，我们提出了一个强大而客观的自动分类系统，用于仅基于H＆E图像的形态组织模式对AC和SC进行实时分类，以协助医学专家诊断肺癌。使用各种原始的和扩展的光密度测量和Haralick的纹理特征来提取图像特征，并使用Boosting算法训练分类器，并将替代决策树作为基础学习者。为了进行评估，以组织微阵列形式收集了369个组织样品，包括97个腺癌和272个鳞状癌样品。使用10倍交叉验证，该技术实现了92.41％的高精度，并且我们还发现，与其他支持的向量学习方法（包括支持向量）相比，这两种Boosting算法（cw-Boost和AdaBoost.Ml）的性能始终很好。机器，神经网络，单一决策树和替代决策树。这种方法为优化以患者为目标的疗法提供了强大，客观和快速的程序。

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