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Extracting multiscale patterns for classification of non-small cell lung cancer in CT images

The non-small cell lung cancer (NSCLC) is the most frequent with about 80% of new cases and it is subdivided into adenocarcinoma, squamous cell and large cell carcinomas. Several studies have demonstrated the relevance of identifying NSCLC cancer subtype for prognosis and treatment. This work presents a classification approach for NSCLC subtypes in computed tomography images based on a multi-scale texture analysis. For doing so, gradients over the difference between multi-scale homogeneity textures was computed to build feature descriptors. Binary classifications were performed for the three NSCLC cancer subtypes under a 10-fold cross-validation scheme, and the best results were obtained for adenocarcinoma vs. squamous cell carcinoma, with an area under the curve of 80% and an accuracy of 77; 4%. The results demonstrate that CT is an useful source of information for extracting patterns that allow to identify tissue changes and correlate them with histological outcome.