Molecular Imaging and Biology

, Volume 21, Issue 6, pp 1200–1209 | Cite as

Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types

  • Francesco BianconiEmail author
  • Isabella Palumbo
  • Mario Luca Fravolini
  • Rita Chiari
  • Matteo Minestrini
  • Luca Brunese
  • Barbara Palumbo
Research Article



The study aims to investigate the correlations between positron emission tomography (PET) texture features, X-ray computed tomography (CT) texture features, and histological subtypes in non-small-cell lung cancer evaluated with 2-deoxy-2-[18F]fluoro-D-glucose PET/CT.


We retrospectively evaluated the baseline PET/CT scans of 81 patients with histologically proven non-small-cell lung cancer. Feature extraction and statistical analysis were carried out on the Matlab platform (MathWorks, Natick, USA).


Intra-CT correlation analysis revealed a strong positive correlation between volume of the lesion (CTvol) and maximum density (CTmax), and between kurtosis (CTkrt) and maximum density (CTmax). A moderate positive correlation was found between volume (CTvol) and average density (CTmean), and between kurtosis (CTkrt) and average density (CTmean). Intra-PET analysis identified a strong positive correlation between the radiotracer uptake (SUVmax, SUVmean) and its degree of variability/disorder throughout the lesion (SUVstd, SUVent). Conversely, there was a strong negative correlation between the uptake (SUVmax, SUVmean) and its degree of uniformity (SUVuni). There was a positive moderate correlation between the metabolic tumor volume (MTV) and radiotracer uptake (SUVmax, SUVmean). Inter (PET-CT) correlation analysis identified a very strong positive correlation between the volume of the lesion at CT (CTvol) and the metabolic volume (MTV), a moderate positive correlation between average tissue density (CTmean) and radiotracer uptake (SUVmax, SUVmean), and between kurtosis at CT (CTkrt) and metabolic tumor volume (MTV). Squamous cell carcinomas had larger volume higher uptake, stronger PET variability and lower uniformity than the other subtypes. By contrast, adenocarcinomas exhibited significantly lower uptake, lower variability and higher uniformity than the other subtypes.


Significant associations emerged between PET features, CT features, and histological type in NSCLC. Texture analysis on PET/CT shows potential to differentiate between histological types in patients with non-small-cell lung cancer.

Key words

[18F] FDG PET/CT Radiomics Non-small-cell lung cancer Texture analysis 


Funding Information

This work was partially supported by the Department of Engineering at the Università degli Studi di Perugia, Italy, under the Fundamental Research Scheme 2018 (M.L. Fravolini and F. Bianconi); by the Italian Ministry of Education, University and Research (MIUR) within the Individual Annual Funding for Fundamental Research “FFABR” 2018 (F. Bianconi and I. Palumbo); and by the Fondazione Cassa di Risparmio di Perugia (Perugia, Italy) with the project Application of Artificial Intelligence methods to PET/CT for computer-assisted diagnosis (grant no. 2015.0389 013).

Compliance with Ethical Standards

Ethical Approval

All the procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Formal ethical approval was not required due to the retrospective nature of the study and the analysis of anonymous clinical data.

Informed Consent

All patients gave written informed consent to undergo PET/CT for clinical purposes and to accept that their data could be used in anonymous form for scientific studies.

Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© World Molecular Imaging Society 2019

Authors and Affiliations

  1. 1.Department of EngineeringUniversità degli Studi di PerugiaPerugiaItaly
  2. 2.Section of Radiation Oncology, Department of Surgical and Biomedical SciencesUniversità degli Studi di PerugiaPerugiaItaly
  3. 3.Department of Medical OncologyOspedale Santa Maria della MisericordiaPerugiaItaly
  4. 4.Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical SciencesUniversità degli Studi di PerugiaPerugiaItaly
  5. 5.Department of Medicine and Health Sciences “Vincenzo Tiberio”Università degli Studi del MoliseCampobassoItaly

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