Advances in Imaging and Automated Quantification of Malignant Pulmonary Diseases: A State-of-the-Art Review

Research output: Contribution to journalArticle

  • Authors:
  • Bruno Hochhegger
  • Matheus Zanon
  • Stephan Altmayer
  • Gabriel S. Pacini
  • Fernanda Balbinot
  • And 8 others
  • External authors:
  • Martina Z. Francisco
  • Ruhana Dalla Costa
  • Guilherme Watte
  • Marcel Koenigkam Santos
  • Marcelo C. Barros
  • Diana Penha
  • Klaus Irion
  • Edson Marchiori


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.

Bibliographical metadata

Original languageEnglish
Pages (from-to)633-642
Issue number6
Early online date9 Oct 2018
Publication statusPublished - 1 Dec 2018