BACKGROUND AND OBJECTIVES: lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. METHODS: local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. RESULTS: radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. CONCLUSIONS: the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning.
BACKGROUND AND OBJECTIVES:lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. METHODS: local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. RESULTS: radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. CONCLUSIONS: the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning.
Authors: Bruno Hochhegger; Matheus Zanon; Stephan Altmayer; Gabriel S Pacini; Fernanda Balbinot; Martina Z Francisco; Ruhana Dalla Costa; Guilherme Watte; Marcel Koenigkam Santos; Marcelo C Barros; Diana Penha; Klaus Irion; Edson Marchiori Journal: Lung Date: 2018-10-09 Impact factor: 2.584
Authors: Radouane El Ayachy; Nicolas Giraud; Paul Giraud; Catherine Durdux; Philippe Giraud; Anita Burgun; Jean Emmanuel Bibault Journal: Front Oncol Date: 2021-05-05 Impact factor: 6.244
Authors: Michele Avanzo; Lise Wei; Joseph Stancanello; Martin Vallières; Arvind Rao; Olivier Morin; Sarah A Mattonen; Issam El Naqa Journal: Med Phys Date: 2020-06 Impact factor: 4.071