Radiomics-based recognition of metastatic and histopathological patterns of lung cancer

Abstract

Lung cancer is the leading cause of cancer-related deaths in the world and its poor prognosis varies markedly according to the tumor staging. Tumor histopathology and computed tomography (CT) features have been used as prognostic factors, but they still present challenges. This work addressess the problem of lung cancer pattern recognition in terms of histopathology and nodal and distant metastasis, using radiomic CT image features and machine learning classifiers. We retrospectively analyzed 52 tumors and semiautomaticaly segmented the CT images. Tumors were characterized by clinical factors and quantitative image attributes of gray level, histogram, texture, shape, and volume. Three classifiers used relevant selected features to perform the analysis. An artificial neural network presented stabled performances on pattern recognition, obtaining areas under the receiver operating characteristic curve of 0.90 for histopathology, 0.88 for nodal metastasis, and 0.98 for distant metastasis. The radiomic pattern recognition presented high performance and great potential to aid the lung cancer diagnosis and prognosis.