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Association of Fluorodeoxyglucose Positron Emission Tomography Radiomics Features with Clinicopathological Factors and Prognosis in Lung Squamous Cell Cancer

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Abstract

Aim

To evaluate the role of fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics features (RFs) for predicting clinicopathological factors (CPFs) and prognosis in patients with resected lung squamous cell cancer (LSCC).

Material and Methods

Patients with early-stage (stage I–III) LSCC who underwent 18F-FDG PET/CT before surgical resection between August 2012 and February 2020 were analyzed. Patients who received neoadjuvant chemotherapy or radiotherapy were excluded from the study. The maximum standard uptake value (SUVmax) and RFs were extracted from PET images for primary tumors. The diagnostic performances of PET parameters in groups of tumor differentiation, stage, and mediastinal lymph node metastasis (MLNM) status were evaluated. The study endpoints were overall survival (OS) and progression-free survival (PFS). Univariate and multivariate analyses were performed with RFs, SUVmax, and CPFs to find independent predictors of PFS and OS.

Results

A total of 77 patients (5 female, 72 male) were included in the study. SUVmax and GLCM entropy were independently associated with tumor differentiation. The only parameter with significant diagnostic performance for MLNM was GLZLM-SLZGE. Tumor diameter and NGLDM busyness were independently associated with the stage. MLNM and tumor differentiation were found to be independent predictors of PFS. NGLDM contrast and MLNM were independently associated with OS.

Conclusion

Using radiomic features in addition to CPFs to predict disease recurrence and shorter overall survival can guide precision medicine in patients with LSCC.

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References

  1. Fitzmaurice C, Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, MacIntyre M, et al. The global burden of cancer 2013. JAMA Oncol. 2015;4:505–27.

    Google Scholar 

  2. Perez-Moreno P, Brambilla E, Thomas R, Soria JC, et al. Squamous cell carcinoma of the lung: molecular subtypes and therapeutic opportunities. Clin Cancer Res. 2012;18:2443–51.

    Article  PubMed  CAS  Google Scholar 

  3. Kwon W, Howard BA, Herndon JE, Patz EF Jr, et al. FDG uptake on positron emission tomography correlates with survival and time to recurrence in patients with stage I non-small-cell lung cancer. J Thorac Oncol. 2015;10:897–902.

    Article  PubMed  Google Scholar 

  4. Kurtipek E, Çayc M, Düzgün N, Esme H, Terzi Y, Bakdk S, et al. (18)F-FDG PET/CT mean SUV and metabolic tumor volume for mean survival time in non-small cell lung cancer. Clin Nucl Med. 2015;40:459–63.

    Article  PubMed  Google Scholar 

  5. Liu J, Dong M, Sun X, Li W, Xing L, Yu J, et al. Prognostic value of 18F-FDG PET/CT in surgical non-small cell lung cancer: a meta-analysis. PLoS ONE. 2016;11:e0146195.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fisher R, Pusztai L, Swanton C. Cancer heterogeneity: implications for targeted therapeutics. Br J Cancer. 2013;108:479–85.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Eary JF, Brenner W. Do we need quantitative imaging? Nuklearmedizin. 2020;59:225–6.

    Article  PubMed  Google Scholar 

  8. Lambin P, Leijenaar RT, Deist TM, Peerlings J, De Jong EE, Van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–62.

    Article  PubMed  Google Scholar 

  9. Ha S, Choi H, Paeng JC, Cheon GJ. Radiomics in oncological PET/CT: a methodological overview. Nucl Med Mol Imaging. 2019;53:14–29.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–77.

    Article  PubMed  Google Scholar 

  11. Aydos U, Ünal ER, Özçelik M, Akdemir D, Ekinci Ö, Taştepe Aİ, et al. Texture features of the primary tumor on (18)F-FDG PET images in non-small cell lung cancer: the relationship between imaging and histopathological parameters. Rev Esp Med Nucl Imagen Mol (Engl Ed). 2021;40:343–50.

    Google Scholar 

  12. Mostafa R, Abdelsamie Kandeel A, Abd Elkareem M, Nardo L, Abdelhafez YG. Pretherapy 18F-fluorodeoxyglucose positron emission tomography/computed tomography robust radiomic features predict overall survival in non-small cell lung cancer. Nucl Med Commun. 2022;43:540–8.

    Article  PubMed  CAS  Google Scholar 

  13. Dissaux G, Visvikis D, Da-Ano R, Pradier O, Chajon E, Barillot I, et al. Pretreatment (18)F-FDG PET/CT radiomics predict local recurrence in patients treated with stereotactic body radiotherapy for early-stage non-small cell lung cancer: a multicentric study. J Nucl Med. 2020;61:814–20.

    Article  PubMed  CAS  Google Scholar 

  14. Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78:4786–9.

    Article  PubMed  CAS  Google Scholar 

  15. Hong R, Halama J, Bova D, Sethi A, Emami B, et al. Correlation of PET standard uptake value and CT window-level thresholds for target delineation in CT-based radiation treatment planning. Int J Radiat Oncol Biol Phys. 2007;67:720–6.

    Article  PubMed  Google Scholar 

  16. Hong R, Halama J, Bova D, Sethi A, Emami B, et al. Gene expression-based classification of non-small cell lung carcinomas and survival prediction. PLoS ONE. 2010;5:e10312.

    Article  Google Scholar 

  17. Attenberger UI, Langs G. How does radiomics actually work?–review. Rofo. 2021;193:652–7.

    Article  PubMed  Google Scholar 

  18. Shao D, Du D, Liu H, Lv J, Cheng Y, Zhang H, et al. Identification of stage IIIC/IV EGFR-mutated non-small cell lung cancer populations sensitive to targeted therapy based on a PET/CT radiomics risk model. Front Oncol. 2021;11:e721318.

    Article  Google Scholar 

  19. Önner H, Abdülrezzak Ü, Tutuş A. Could the skewness and kurtosis texture parameters of lesions obtained from pretreatment Ga-68 DOTA-TATE PET/CT images predict receptor radionuclide therapy response in patients with gastroenteropancreatic neuroendocrine tumors? Nucl Med Commun. 2020;41:1034–9.

    Article  PubMed  Google Scholar 

  20. Sun W, Jiang M, Dang J, Chang P, Yin FF. Effect of machine learning methods on predicting NSCLC overall survival time based on radiomics analysis. Radiat Oncol. 2018;13:1–8.

    Article  Google Scholar 

  21. Ahn HK, Lee H, Kim SG, Hyun SH. Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer. Clin Radiol. 2019;74:467–73.

    Article  PubMed  CAS  Google Scholar 

  22. Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F. Radiomics-based prognosis analysis for non-small cell lung cancer. Science. 2017;7:1–8.

    Google Scholar 

  23. Carles M, Fechter T, Radicioni G, Schimek-Jasch T, Adebahr S, Zamboglou C, et al. FDG-PET radiomics for response monitoring in non-small-cell lung cancer treated with radiation therapy. Cancers. 2021;13:814.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Chetan MR, Gleeson FV. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol. 2020;31:1049–58.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Andre F, Grunenwald D, Pignon JP, Dujon A, Pujol JL, Brichon PY, et al. Survival of patients with resected N2 non–small-cell lung cancer: evidence for a subclassification and implications. J Clin Oncol. 2000;18:;2981–89.

  26. Önner H, Coskun N, Erol M, Karanis MİE. Association of (18)F-FDG PET/CT textural features with immunohistochemical characteristics in invasive ductal breast cancer. Rev Esp Med Nucl Imagen Mol (Engl Ed). 2022;41:11–6.

    Google Scholar 

  27. Ouyang ML, Wang YR, Deng QS, Zhu YF, Zhao ZH, Wang L, et al. Development and validation of a 18F-FDG PET-based radiomic model for evaluating hypermetabolic mediastinal–hilar lymph nodes in non-small-cell lung cancer. Front Oncol. 2021;11:e710909.

    Article  Google Scholar 

  28. Zheng K, Wang X, Jiang C, Tang Y, Fang Z, Hou J, et al. Pre-operative prediction of mediastinal node metastasis using radiomics model based on 18F-FDG PET/CT of the primary tumor in non-small cell lung cancer patients. Front Med (Lausanne). 2021;8:e673876.

    Article  Google Scholar 

  29. Papp L, Rausch I, Hacker M, Beyer T. Fuzzy radiomics: a novel approach to minimize the effects of target delineation on radiomic models. Nuklearmedizin. 2019;58:L9.

    Google Scholar 

  30. Im HJ, Zhang Y, Wu H, Wu J, Daw NC, Navid F, et al. Prognostic value of metabolic and volumetric parameters of FDG PET in pediatric osteosarcoma: a hypothesis-generating study. Radiology. 2018;287:303–12.

    Article  PubMed  Google Scholar 

  31. Erdi YE, Mawlawi O, Larson SM, Imbriaco M, Yeung H, Finn R, et al. Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding. Cancer. 1997;80:2505–9.

    Article  PubMed  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Nuclear Medicine, Konya City Hospital, University of Health Sciences Turkey, Konya, Turkey

    Mustafa Erol

  2. Department of Nuclear Medicine, Medical Faculty, Selcuk University, Konya, Turkey

    Hasan Önner

  3. Department of Pathology, Konya City Hospital, University of Health Sciences Turkey, Konya, Turkey

    İlknur Küçükosmanoğlu

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  1. Mustafa Erol
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  2. Hasan Önner
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  3. İlknur Küçükosmanoğlu
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Contributions

The study was designed by Mustafa Erol. Material preparation and data collection were performed by Mustafa Erol, Hasan Önner, and İlknur Küçükosmanoğlu. The data analysis was performed by Mustafa Erol and Hasan Önner. The first draft of the manuscript was written by Mustafa Erol and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hasan Önner.

Ethics declarations

Ethical Approval and Consent to Participate

The study was approved by the institutional review board of KTO Karatay University (2020/11), and the requirement for written consent was waived by the institutional review board. All procedures performed in studies involving human participants were in accordance with the Helsinki Declaration as revised in 2013 and its later amendments.

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Not applicable.

Conflict of Interest

Mustafa Erol, Hasan Önner, and İlknur Küçükosmanoğlu declare no conflict of interest.

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Erol, M., Önner, H. & Küçükosmanoğlu, İ. Association of Fluorodeoxyglucose Positron Emission Tomography Radiomics Features with Clinicopathological Factors and Prognosis in Lung Squamous Cell Cancer. Nucl Med Mol Imaging 56, 306–312 (2022). https://doi.org/10.1007/s13139-022-00774-2

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  • DOI: https://doi.org/10.1007/s13139-022-00774-2

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