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07.03.2019 | Research Article

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

verfasst von: Francesco Bianconi, Isabella Palumbo, Mario Luca Fravolini, Rita Chiari, Matteo Minestrini, Luca Brunese, Barbara Palumbo

Erschienen in: Molecular Imaging and Biology | Ausgabe 6/2019

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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 (CT_vol) and maximum density (CT_max), and between kurtosis (CT_krt) and maximum density (CT_max). A moderate positive correlation was found between volume (CT_vol) and average density (CT_mean), and between kurtosis (CT_krt) and average density (CT_mean). Intra-PET analysis identified a strong positive correlation between the radiotracer uptake (SUV_max, SUV_mean) and its degree of variability/disorder throughout the lesion (SUV_std, SUV_ent). Conversely, there was a strong negative correlation between the uptake (SUV_max, SUV_mean) and its degree of uniformity (SUV_uni). There was a positive moderate correlation between the metabolic tumor volume (MTV) and radiotracer uptake (SUV_max, SUV_mean). Inter (PET-CT) correlation analysis identified a very strong positive correlation between the volume of the lesion at CT (CT_vol) and the metabolic volume (MTV), a moderate positive correlation between average tissue density (CT_mean) and radiotracer uptake (SUV_max, SUV_mean), and between kurtosis at CT (CT_krt) 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.

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

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

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 CrossRef

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

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

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 PubMedPubMedCentral

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

28.

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

29.

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

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 CrossRef

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 CrossRef

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 CrossRef

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 PubMed

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 CrossRef

Titel: Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types
verfasst von: Francesco Bianconi
Isabella Palumbo
Mario Luca Fravolini
Rita Chiari
Matteo Minestrini
Luca Brunese
Barbara Palumbo
Publikationsdatum: 07.03.2019
Verlag: Springer International Publishing
Erschienen in: Molecular Imaging and Biology / Ausgabe 6/2019
Print ISSN: 1536-1632
Elektronische ISSN: 1860-2002
DOI: https://doi.org/10.1007/s11307-019-01336-3

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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

Abstract

Purpose

Procedures

Results

Conclusions

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Abstract

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