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Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer?

  • Molecular Imaging
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A Correction to this article was published on 23 December 2020

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Abstract

Objectives

We aimed at investigating the origin of the correlations between tumor volume and 18F-FDG-PET texture indices in lung cancer.

Methods

Eighty-five consecutive patients with newly diagnosed non-small cell lung cancer (NSCLC) underwent a 18F-FDG-PET/CT scan before treatment. Seven phantom spheres uniformly filled with 18F-FDG, and covering a range of activities and volumes similar to that found in lung tumors, were also scanned. Established texture indices were computed for lung tumors and homogeneous spheres. The dependence between textural indices and volume in homogeneous spheres was modeled and then used to predict texture indices in lung tumors. Correlation analyses were carried out between predicted and texture features measured in lung tumors. Cox proportional hazards regression was used to investigate the associations between overall survival and volume-adjusted textural features.

Results

All textural features showed strong, non-linear correlations with volume, both in tumors and homogeneous spheres. Correlations between predicted versus measured texture features were very high for contrast (r2 = 0.91), dissimilarity (r2 = 0.90), ZP (r2 = 0.90), GLNN (r2 = 0.86), and homogeneity (r2 = 0.82); high for entropy (r2 = 0.50) and HILAE (r2 = 0.53); and low for energy (r2 = 0.30). Cox regressions showed that among volume-adjusted features, only HILAE was associated with overall survival (b = − 0.35, p = 0.008).

Conclusion

We have shown that texture indices previously found to be correlated with a number of clinically relevant outcomes might not provide independent information apart from that driven by their correlation with tumor volume, suggesting that these metrics might not be suitable as intratumor heterogeneity markers.

Key Points

• Associations between texture FDG-PET indices and overall survival have been widely reported in lung cancer, with tumor volume also being associated with overall survival, and therefore, it is still unclear whether the predictive power of textural indices is simply driven by this correlation.

• Our results demonstrated strong non-linear correlations between textural indices and volume, showing an analogous behavior for lung tumors from patients and homogeneous spheres inserted in phantoms.

• Our findings showed that texture FDG-PET indices might not provide independent information apart from that driven by their correlation with tumor volume.

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Abbreviations

CM:

Co-occurrence matrix

GLNN:

Gray Level Non-Uniformity Normalized

HILAE:

High intensity large area emphasis

NSCLC:

Non-small lung cell carcinoma

SZM:

Size zone matrix

ZP:

Zone percentage

References

  1. Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. McGranahan N, Swanton C (2015) Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27:15–26

    Article  CAS  PubMed  Google Scholar 

  3. Tellez-Gabriel M, Ory B, Lamoureux F, Heymann M-F, Heymann D (2016) Tumour heterogeneity: the key advantages of single-cell analysis. Int J Mol Sci 20;17(12):2142

  4. Michor F, Polyak K (2010) The origins and implications of intratumor heterogeneity. Cancer Prev Res (Phila) 3:1361–1364

    Article  Google Scholar 

  5. Visvader JE (2011) Cells of origin in cancer. Nature. 469:314–322

    Article  CAS  PubMed  Google Scholar 

  6. Krause BJ, Schwarzenböck S, Souvatzoglou M (2013) FDG PET and PET/CT. Recent Results Cancer 187:351–369

    Article  CAS  Google Scholar 

  7. Davnall F, Yip CSP, Ljungqvist G, Selmi M, Ng F (2012) Sanghera B, et al Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589

    Article  PubMed  PubMed Central  Google Scholar 

  8. Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJR (2013) Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 40:133–140

    Article  PubMed  Google Scholar 

  9. Fonti R, Conson M, Del Vecchio S (2019) PET/CT in radiation oncology. Semin Oncol 46:202–209

    Article  CAS  PubMed  Google Scholar 

  10. Miller TR, Pinkus E, Dehdashti F, Grigsby PW (2003) Improved prognostic value of 18F-FDG PET using a simple visual analysis of tumor characteristics in patients with cervical cancer. J Nucl Med 44:192–197

    PubMed  Google Scholar 

  11. Bailly C, Bodet-Milin C, Bourgeois M et al (2019) Exploring tumor heterogeneity using PET imaging: the big picture. Cancers (Basel) 11(9):1282

  12. Hatt M, Tixier F, Visvikis D, Cheze Le Rest C (2017) Radiomics in PET/CT: more than meets the eye? J Nucl Med 58:365–366

    Article  PubMed  Google Scholar 

  13. Tixier F, Hatt M, Valla C, et al (2014) Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. J Nucl Med 55(8):1235–1241

  14. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3:610–621

    Article  Google Scholar 

  15. Amadasun M, King R (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 19:1264–1274

    Article  Google Scholar 

  16. Alic L, Niessen WJ, Veenland JF (2014) Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. PLoS One 9:e110300

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Materka A, Strzelecki M (1998). Texture analysis methods – a review. Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels

  18. Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069

    Article  CAS  PubMed  Google Scholar 

  19. Moscoso A, Ruibal Á, Domínguez-Prado I et al (2018) Texture analysis of high-resolution dedicated breast 18 F-FDG PET images correlates with immunohistochemical factors and subtype of breast cancer. Eur J Nucl Med Mol Imaging 45:196–206

    Article  CAS  PubMed  Google Scholar 

  20. Groheux D, Martineau A, Teixeira L et al (2017) 18FDG-PET/CT for predicting the outcome in ER+/HER2- breast cancer patients: comparison of clinicopathological parameters and PET image-derived indices including tumor texture analysis. Breast Cancer Res 19:3

  21. Kamitaka Y, Miwa K, Nishii R et al (2019) Textural analysis of 18F-FDG PET/CT to predict tumor response of carbon-ion radiotherapy in patients with locally advanced pancreas cancer. J Nucl Med 60:3005–3005

    Google Scholar 

  22. Tixier F, Le Rest CC, Hatt M et al (2011) Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52:369–378

    Article  PubMed  Google Scholar 

  23. Wang S, Wang S, Liu E-T (2019) Application value of 18F-FDG PET/CT and texture analysis in evaluating the M staging of esophageal cancer. J Nucl Med 60:219–219

    Google Scholar 

  24. Lue K-H, Chen Y-H, Lin H-H, Liu S-H, Kao C-H, Chuang K-S (2019) Relationship between prostate-specific antigen kinetics and radiomic features of F-18 fluorocholine PET in patients with prostate cancer. J Nucl Med 60:3004–3004

    Google Scholar 

  25. Song J, Dong D, Huang Y, Liu Z, Tian J (2016) Association between tumor heterogeneity and overall survival in patients with non-small cell lung cancer. 2016 IEEE 13th Int Symp Biomed Imaging ISBI

  26. Lovinfosse P, Janvary ZL, Coucke P et al (2016) FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging 43:1453–1460

    Article  CAS  PubMed  Google Scholar 

  27. Cook GJR, Yip C, Siddique M et al (2013) Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med 54:19–26

    Article  PubMed  Google Scholar 

  28. Giesel FL, Schneider F, Kratochwil C et al (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  PubMed  Google Scholar 

  29. Bashir U, Siddique MM, Mclean E, Goh V, Cook GJ (2016) Imaging heterogeneity in lung cancer: techniques, applications, and challenges. AJR Am J Roentgenol 207:534–543

    Article  PubMed  Google Scholar 

  30. Moon SH, Kim J, Joung J-G et al (2019) Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer. Eur J Nucl Med Mol Imaging 46:446–454

    Article  CAS  PubMed  Google Scholar 

  31. Scrivener M, de Jong EEC, van Timmeren JE, Pieters T, Ghaye B, Geets X (2016) Radiomics applied to lung cancer: a review. Transl Cancer Res 5:398–409

    Article  Google Scholar 

  32. Cherezov D, Goldgof D, Hall L et al (2019) Revealing tumor habitats from texture heterogeneity analysis for classification of lung cancer malignancy and aggressiveness. Sci Rep 9:4500

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Han S, Woo S, Suh CH, Kim YJ, Oh JS, Lee JJ (2018) A systematic review of the prognostic value of texture analysis in 18F-FDG PET in lung cancer. Ann Nucl Med 32:602–610

    Article  CAS  PubMed  Google Scholar 

  34. Jensen GL, Yost CM, Mackin DS et al (2018) Prognostic value of combining a quantitative image feature from positron emission tomography with clinical factors in oligometastatic non-small cell lung cancer. Radiother Oncol 126:362–367

  35. Kirienko M, Cozzi L, Antunovic L et al (2018) Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging 45:207–217

    Article  PubMed  Google Scholar 

  36. Dong X, Sun X, Sun L et al (2016) Early change in metabolic tumor heterogeneity during chemoradiotherapy and its prognostic value for patients with locally advanced non-small cell lung cancer. PLoS One 11:e0157836

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Park S, Ha S, Lee S-H et al (2018) Intratumoral heterogeneity characterized by pretreatment PET in non-small cell lung cancer patients predicts progression-free survival on EGFR tyrosine kinase inhibitor. PLoS One 13:e0189766

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D (2017) Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging 44:151–165

    Article  PubMed  Google Scholar 

  39. Depeursinge A, Foncubierta-Rodriguez A, Van De Ville D, Müller H (2014) Three-dimensional solid texture analysis in biomedical imaging: review and opportunities. Med Image Anal 18:176–196

    Article  PubMed  Google Scholar 

  40. Altman DG, Lausen B, Sauerbrei W, Schumacher M (1994) Dangers of using “optimal” cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst 86:829–835

    Article  CAS  PubMed  Google Scholar 

  41. Chalkidou A, O’Doherty MJ, Marsden PK (2015) False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One 10:e0124165

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Yan J, Chu-Shern JL, Loi HY et al (2015) Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med 56:1667–1673

    Article  CAS  PubMed  Google Scholar 

  43. Lemarignier C, Martineau A, Teixeira L et al (2017) Correlation between tumour characteristics, SUV measurements, metabolic tumour volume, TLG and textural features assessed with 18F-FDG PET in a large cohort of oestrogen receptor-positive breast cancer patients. Eur J Nucl Med Mol Imaging 44:1145–1154

    Article  CAS  PubMed  Google Scholar 

  44. Orlhac F, Soussan M, Maisonobe J-A, Garcia CA, Vanderlinden B, Buvat I (2014) Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med 55:414–422

    Article  CAS  PubMed  Google Scholar 

  45. Brooks FJ, Grigsby PW (2014) The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med 55:37–42

    Article  CAS  PubMed  Google Scholar 

  46. Hatt M, Majdoub M, Vallières M et al (2015) 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med 56:38–44

    Article  CAS  PubMed  Google Scholar 

  47. Werner-Wasik M, Swann RS, Bradley J et al (2008) Increasing tumor volume is predictive of poor overall and progression-free survival: secondary analysis of the Radiation Therapy Oncology Group 93-11 phase I-II radiation dose-escalation study in patients with inoperable non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 70:385–390

    Article  PubMed  Google Scholar 

  48. Huang W, Fan M, Liu B et al (2014) Value of metabolic tumor volume on repeated 18F-FDG PET/CT for early prediction of survival in locally advanced non-small cell lung cancer treated with concurrent chemoradiotherapy. J Nucl Med 55:1584–1590

    Article  CAS  PubMed  Google Scholar 

  49. Kurtipek E, Çayci M, Düzgün N et al (2015) (18)F-FDG PET/CT mean SUV and metabolic tumor volume for mean survival time in non-small cell lung cancer. Clin Nucl Med 40:459–463

    Article  PubMed  Google Scholar 

  50. Chaddad A, Desrosiers C, Toews M, Abdulkarim B (2017) Predicting survival time of lung cancer patients using radiomic analysis. Oncotarget. 8:104393–104407

    Article  PubMed  PubMed Central  Google Scholar 

  51. Edge SB, Compton CC (2010) The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 17:1471–1474

    Article  PubMed  Google Scholar 

  52. Performance measurements of positron emission tomographs (PET) [Internet]. [cited 2020 May 27]. Available from: https://www.nema.org

  53. Vereos P. Advanced molecular imaging. 12. https://www.usa.philips.com/healthcare/solutions/advanced-molecular-imaging

  54. Bashir U, Azad G, Siddique MM et al (2017) The effects of segmentation algorithms on the measurement of 18F-FDG PET texture parameters in non-small cell lung cancer. EJNMMI Res 7:60

    Article  PubMed  PubMed Central  Google Scholar 

  55. Texture indexes and gray level size zone matrix application to cell nuclei classification. Pattern Recogn Inf Process. 2009. Available from: https://www.scienceopen.com/document?vid=2c91747d-b5c9-4a39-8751-9e17e9776f22

  56. Hatt M, Tixier F, Cheze Le Rest C, Pradier O, Visvikis D (2013) Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging 40:1662–1671

    Article  PubMed  Google Scholar 

  57. Tixier F, Hatt M, Le Rest CC, Le Pogam A, Corcos L, Visvikis D (2012) Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med 53:693–700

    Article  PubMed  Google Scholar 

  58. van Velden FHP, Cheebsumon P, Yaqub M et al (2011) Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging 38:1636–1647

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Groheux D, Majdoub M, Tixier F et al (2015) Do clinical, histological or immunohistochemical primary tumour characteristics translate into different 18F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer? Eur J Nucl Med Mol Imaging 42:1682–1691

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Takeda K, Takanami K, Shirata Y et al (2017) Clinical utility of texture analysis of 18F-FDG PET/CT in patients with stage I lung cancer treated with stereotactic body radiotherapy. J Radiat Res 58:862–869

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Yang F, Young LA, Johnson PB (2018) Quantitative radiomics: validating image textural features for oncological PET in lung cancer. Radiother Oncol 129:209–217

    Article  PubMed  Google Scholar 

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Acknowledgments

Special thanks to the technical staff of the Nuclear Medicine Department, who provided all the required information on the acquisition and reconstruction protocol and management of patients.

Funding

This study has received funding by the public project DTS17/00138 co-funded by FEDER and Beca SEMNIM 2020 (con el patrocinio de Advanced Accelerator Applications).

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Authors and Affiliations

  1. Molecular Imaging and Medical Physics Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Santiago de Compostela, Galicia, Spain

    Manuel Piñeiro-Fiel, Alexis Moscoso, David Rey-Bretal, Noemí Gómez-Lado, Jesús Silva-Rodríguez, Álvaro Ruibal & Pablo Aguiar

  2. Nuclear Medicine Department and Molecular Imaging Research Group, University Hospital and Health Research Institute of Santiago de Compostela (IDIS), Travesía da Choupana S/N, 15706, Santiago de Compostela, Galicia, Spain

    Manuel Piñeiro-Fiel, Alexis Moscoso, María Pombo-Pasín, David Rey-Bretal, Noemí Gómez-Lado, Jesús Silva-Rodríguez, Virginia Pubul, Álvaro Ruibal & Pablo Aguiar

  3. Radiological Protection and Radiation Physics Department, University Hospital and Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain

    Lucía Lado-Cacheiro & Manuel Sánchez

  4. Pharmacology Group, Health Research Institute of Santiago de Compostela (FIDIS), 15706, Santiago de Compostela, Spain

    Cristina Mondelo-García

  5. Pharmacy Department, University Clinical Hospital of Santiago de Compostela (SERGAS), 15706, Santiago de Compostela, Spain

    Cristina Mondelo-García

  6. Fundación Tejerina, Madrid, Spain

    Álvaro Ruibal

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  1. Manuel Piñeiro-Fiel
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Correspondence to Pablo Aguiar.

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The scientific guarantor of this publication is Pablo Aguiar.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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Written informed consent was obtained from all subjects (patients) in this study.

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• retrospective

• prognostic study/observational/experimental

• performed at one institution

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The original online version of this article was revised: The information that Manuel Piñeiro-Fiel and Alexis Moscoso have contributed equally to this work was missing.

Manuel Piñeiro-Fiel and Alexis Moscoso contributed equally to this work.

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Piñeiro-Fiel, M., Moscoso, A., Lado-Cacheiro, L. et al. Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer?. Eur Radiol 31, 4156–4165 (2021). https://doi.org/10.1007/s00330-020-07507-z

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