Published in:
01-12-2018 | LUNG CANCER
Advances in Imaging and Automated Quantification of Malignant Pulmonary Diseases: A State-of-the-Art Review
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
Published in:
Lung
|
Issue 6/2018
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Abstract
Quantitative imaging in lung cancer is a rapidly evolving modality in radiology that is changing clinical practice from a qualitative analysis of imaging features to a more dynamic, spatial, and phenotypical characterization of suspected lesions. Some quantitative parameters, such as the use of 18F-FDG PET/CT-derived standard uptake values (SUV), have already been incorporated into current practice as it provides important information for diagnosis, staging, and treatment response of patients with lung cancer. A growing body of evidence is emerging to support the use of quantitative parameters from other modalities. CT-derived volumetric assessment, CT and MRI lung perfusion scans, and diffusion-weighted MRI are some of the examples. Software-assisted technologies are the future of quantitative analyses in order to decrease intra- and inter-observer variability. In the era of “big data”, widespread incorporation of radiomics (extracting quantitative information from medical images by converting them into minable high-dimensional data) will allow medical imaging to surpass its current status quo and provide more accurate histological correlations and prognostic value in lung cancer. This is a comprehensive review of some of the quantitative image methods and computer-aided systems to the diagnosis and follow-up of patients with lung cancer.