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

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Abstract

Purpose

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.

Procedures

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).

Results

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.

Conclusions

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.

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References

  1. American Cancer Society. Cancer statistics center, 2018. Available online at: https://cancerstatisticscenter.cancer.org/#!/. Last accessed on Jul. 9, 2018

  2. Molina JR, Yang CPSD et al (2008) Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. Mayo Clinic Proc 83:584–594

    Article  Google Scholar 

  3. Palumbo B, Buresta Nuvoli TS et al (2014) SPECT and PET serve as molecular imaging techniques and in vivo biomarkers for brain metastases. Int J Mol Sci 15:9878–9893

    Article  Google Scholar 

  4. Chao F, Zhang H (2012) PET/CT in the staging of the non-small-cell lung cancer. J biomed Biotechnol. Art. 783739

  5. Scrivener M, de Jong EEC, van Timmeren T et al (2016) Radiomics applied to lung cancer: a review. Transl Cancer Res 5:398–409

    Article  Google Scholar 

  6. Dennie C, Thornhill R, Sethi-Virmani V et al (2016) Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules. Quant Imaging Med Surg 6:6–15

    PubMed  PubMed Central  Google Scholar 

  7. Kirienko M, Cozzi L, Rossi A, Voulaz E, Antunovic L, Fogliata A, Chiti A, Sollini M (2018) Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging 45:1649–1660

    Article  Google Scholar 

  8. Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802

    Article  Google Scholar 

  9. Nappi A, Gallicchio R, Simeon V, Nardelli A, Pelagalli A, Zupa A, Vita G, Venetucci A, di Cosola M, Barbato F, Storto G (2015) FDG-PET/CT parameters as predictors of outcome in inoperable NSCLC patients. Radiol Oncol 49:320–326

    Article  CAS  Google Scholar 

  10. Sacconi B, Anzidei M, Leonardi A, Boni F, Saba L, Scipione R, Anile M, Rengo M, Longo F, Bezzi M, Venuta F, Napoli A, Laghi A, Catalano C (2017) Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with EGFR mutations and survival rates. Clin Radiol 72:443–450

    Article  CAS  Google Scholar 

  11. Bianconi F, Fravolini ML, Bello-Cerezo R et al (2018) Evaluation of shape and textural features from CT as prognostic biomarkers in non-small cell lung cancer. Anticancer Res 38:2155–2160

    PubMed  Google Scholar 

  12. Ravanelli M, Farina D, Morassi N et al (2013) Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy. Eur Radiol 23:3450–3455

    Article  Google Scholar 

  13. Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, Verlingue L, Brandao D, Lancia A, Ammari S, Hollebecque A, Scoazec JY, Marabelle A, Massard C, Soria JC, Robert C, Paragios N, Deutsch E, Ferté C (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19:1180–1191

    Article  CAS  Google Scholar 

  14. Cook CJR, Azad G, Owczarczyk K et al (2018) Challenges and promises of PET radiomics. Int J Radiat Oncol Biol Phys 102:1083–1089

    Article  Google Scholar 

  15. Sollini M, Cozzi L, Antunovic L, Chiti A, Kirienko M (2017) PET radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep 7 Art no 7:358

    Article  CAS  Google Scholar 

  16. Bashir U, Siddique MM, McLean E et al (2016) Imaging heterogeneity in lung cancer: techniques, applications, and challenges. Am J Roentgenol 207:534–543

    Article  Google Scholar 

  17. Brunese L, Greco B, Setola FR, Lassandro F, Guarracino MR, de Rimini M, Piccolo S, de Rosa N, Muto R, Bianco A, Muto P, Grassi R, Rotondo A (2013) Non-small cell lung cancer evaluated with quantitative contrast-enhanced CT and PET-CT: net enhancement and standardized uptake values are related to tumour size and histology. Med Sci Monit 19:95–101

    Article  Google Scholar 

  18. Wu W, Parmar C, Grossmann P et al (2016) Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol 6 Art no:71

  19. Giesel FL, Schneider F, Kratochwil C, Rath D, Moltz J, Holland-Letz T, Kauczor HU, Schwartz LH, Haberkorn U, Flechsig P (2017) Correlation between SUVmax and CT radiomic analysis using lymph node density in PET/CT-based lymph node staging. J Nucl Med 58:282–287

    Article  CAS  Google Scholar 

  20. Saad M, Choi T-S (2018) Computer-assisted subtyping and prognosis for non-small cell lung cancer patients with unresectable tumor. Comput Med Imaging Graph 67:1–8

    Article  Google Scholar 

  21. Zhu X, Dong D, Chen Z, Fang M, Zhang L, Song J, Yu D, Zang Y, Liu Z, Shi J, Tian J (2018) Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol 28:2772–2778

    Article  Google Scholar 

  22. Kawase A, Yoshida J, Ishii G, Nakao M, Aokage K, Hishida T, Nishimura M, Nagai K (2012) Differences between squamous cell carcinoma and adenocarcinoma of the lung: are adenocarcinoma and squamous cell carcinoma prognostically equal? Jpn J Clin Oncol 42:189–195

    Article  Google Scholar 

  23. Fukui T, Taniguchi T, Kawaguchi K, Fukumoto K, Nakamura S, Sakao Y, Yokoi K (2015) Comparisons of the clinicopathological features and survival outcomes between lung cancer patients with adenocarcinoma and squamous cell carcinoma. Gen Thorac Cardiovasc Surg 63:507–513

    Article  Google Scholar 

  24. Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, Pellot-Barakat C, Soussan M, Frouin F, Buvat I (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789

    Article  CAS  Google Scholar 

  25. du Prel J-B, Röhrig B, Hommel G, Blettner M (2010) choosing statistical tests. Dtsch Arztebl Int 2010 107: 343–348

  26. Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5 art. No. 4006

  27. Oikonomou A, Khalvati F, Tyrrell PN, Haider MA, Tarique U, Jimenez-Juan L, Tjong MC, Poon I, Eilaghi A, Ehrlich L, Cheung P (2018) Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy. Sci Rep 8:4003

    Article  Google Scholar 

  28. Schober F, Boer C, Schwarte LA (2018) Correlation coefficients: appropriate use and interpretation. Anesth Analg 126:1763–1768

    Article  Google Scholar 

  29. Zar JH (1972) Testing of the spearman rank correlation coefficient. J Am Stat Assoc 67:578–580

    Article  Google Scholar 

  30. Zhang J, Gold KA, Lin HY, Swisher SG, Xing Y, Lee JJ, Kim ES, William WN Jr (2015) Relationship between tumor size and survival in non-small-cell lung cancer (NSCLC): an analysis of the surveillance, epidemiology, and end results (SEER) registry. J Thorac Oncol 10:682–690

    Article  CAS  Google Scholar 

  31. Pyka T, Bundschuh RA, Andratschke N, Mayer B, Specht HM, Papp L, Zsótér N, Essler M (2015) Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy. Radiat Oncol 10:100

    Article  Google Scholar 

  32. de Geus-Oei L-F, van Krieken JHJM, Aliredjo RP et al (2007) Biological correlates of FDG uptake in non-small cell lung cancer. Lung Cancer 55:79–87

    Article  Google Scholar 

  33. Vesselle H, Schmidt RA, Pugsley JM et al (2000) Lung cancer proliferation correlates with [F-18] fluorodeoxyglucose uptake by positron emission tomography. Clin Cancer Res 6:3837–3844

    CAS  PubMed  Google Scholar 

  34. Duhaylongsod FG, Lowe VJ, Patz EF Jr, Patz EF Jr, Vaughn AL, Coleman RE, Wolfe WG (1995) Lung tumor growth correlates with glucose metabolism measured by fluoride-18 fluorodeoxyglucose positron emission tomography. Ann Thorac Surg 60:1348–1352

    Article  CAS  Google Scholar 

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Funding

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).

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Correspondence to Francesco Bianconi.

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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.

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The authors declare that they have no conflict of interest.

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Bianconi, F., Palumbo, I., Fravolini, M.L. et al. Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types. Mol Imaging Biol 21, 1200–1209 (2019). https://doi.org/10.1007/s11307-019-01336-3

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