Automatic Morphological Classification of Lung Cancer Subtypes with Boosting Algorithms for Optimizing Therapy
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.M1) 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.
Keywordsmorphological classification computer vision adenocarcinoma squamous carcinoma boosting tissue microarray
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