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

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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.

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Abbreviations

18F-FDG PET/CT:

Fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography

ADC:

Apparent diffusion coefficient

CAD:

Computer-aided diagnosis

CT:

Computed tomography

DCE:

Dynamic contrast-enhanced

DTP:

Dual time point imaging technique

DWI:

Diffusion-weighted imaging

LSR:

Lesion-to-spinal cord ratio

MRI:

Magnetic resonance imaging

MRI-SI:

Magnetic resonance imaging signal intensity

MVD:

Microvessel density

NSCLC:

Non-small cell lung cancer

RECIST:

Response evaluation criteria in solid tumors

ROI:

Regions of interest

SI:

Signal intensity

SUV:

Standardized uptake value

VDT:

Volume doubling time

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Acknowledgements

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

Author information

Authors and Affiliations

  1. LABIMED – Medical Imaging Research Lab, Department of Radiology, Pavilhão, Pereira Filho Hospital, Irmandade Santa Casa de Misericórdia de Porto Alegre, Av. Independência, 75, Porto Alegre, 90020-160, Brazil

    Bruno Hochhegger

  2. Department of Imaging, Pontifical Catholic University of Rio Grande do Sul, Av. Ipiranga, 6681 - Partenon, Porto Alegre, RS, 90619-900, Brazil

    Bruno Hochhegger & Guilherme Watte

  3. Medical Imaging Research Laboratory, Federal University of Health Sciences of Porto Alegre, R. Sarmento Leite, 245, Porto Alegre, Rio Grande Do Sul, 90050-170, Brazil

    Matheus Zanon, Stephan Altmayer, Gabriel S. Pacini, Fernanda Balbinot & Guilherme Watte

  4. Department of Radiology, Irmandade da Santa Casa de Misericordia de Porto Alegre, Av. Independência, 75, Porto Alegre, 90035-072, Brazil

    Martina Z. Francisco, Ruhana Dalla Costa & Marcelo C. Barros

  5. Ribeirao Preto Medical School, Av. Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, São Paulo, 14049-900, Brazil

    Marcel Koenigkam Santos

  6. Radiology, Liverpool Heart and Chest Hospital, Thomas Dr, Liverpool, L14 3PE, UK

    Diana Penha

  7. Central Manchester University Hospitals NHS Foundation Trust, Manchester Royal Infirmary, Oxford Road, Manchester, M13 9WL, UK

    Klaus Irion

  8. Federal University of Rio de Janeiro, Av. Carlos Chagas Filho, 373, Rio De Janeiro, 21941-902, Brazil

    Edson Marchiori

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  1. Bruno Hochhegger
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  2. Matheus Zanon
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Hochhegger, B., Zanon, M., Altmayer, S. et al. Advances in Imaging and Automated Quantification of Malignant Pulmonary Diseases: A State-of-the-Art Review. Lung 196, 633–642 (2018). https://doi.org/10.1007/s00408-018-0156-0

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