, Volume 196, Issue 6, pp 633–642 | Cite as

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

  • Bruno HochheggerEmail author
  • 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


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.


Lung cancer Computed tomography Magnetic resonance imaging Positron emission tomography 



Fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography


Apparent diffusion coefficient


Computer-aided diagnosis


Computed tomography


Dynamic contrast-enhanced


Dual time point imaging technique


Diffusion-weighted imaging


Lesion-to-spinal cord ratio


Magnetic resonance imaging


Magnetic resonance imaging signal intensity


Microvessel density


Non-small cell lung cancer


Response evaluation criteria in solid tumors


Regions of interest


Signal intensity


Standardized uptake value


Volume doubling time



The authors thank Prof. Hans Ulrich Kauczor for his scientific contribution to improve this manuscript.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Bruno Hochhegger
    • 1
    • 2
    Email author
  • Matheus Zanon
    • 3
  • Stephan Altmayer
    • 3
  • Gabriel S. Pacini
    • 3
  • Fernanda Balbinot
    • 3
  • Martina Z. Francisco
    • 4
  • Ruhana Dalla Costa
    • 4
  • Guilherme Watte
    • 2
    • 3
  • Marcel Koenigkam Santos
    • 5
  • Marcelo C. Barros
    • 4
  • Diana Penha
    • 6
  • Klaus Irion
    • 7
  • Edson Marchiori
    • 8
  1. 1.LABIMED – Medical Imaging Research Lab, Department of Radiology, Pavilhão, Pereira Filho HospitalIrmandade Santa Casa de Misericórdia de Porto AlegrePorto AlegreBrazil
  2. 2.Department of ImagingPontifical Catholic University of Rio Grande do SulPorto AlegreBrazil
  3. 3.Medical Imaging Research LaboratoryFederal University of Health Sciences of Porto AlegrePorto AlegreBrazil
  4. 4.Department of RadiologyIrmandade da Santa Casa de Misericordia de Porto AlegrePorto AlegreBrazil
  5. 5.Ribeirao Preto Medical SchoolRibeirão PretoBrazil
  6. 6.RadiologyLiverpool Heart and Chest HospitalLiverpoolUK
  7. 7.Central Manchester University Hospitals NHS Foundation TrustManchesterUK
  8. 8.Federal University of Rio de JaneiroRio De JaneiroBrazil

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