Skip to main content

Advertisement

Log in

Glucose metabolic heterogeneity correlates with pathological features and improves survival stratification of resectable lung adenocarcinoma

  • Original Article
  • Published:
Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

Objective

We investigated whether glycolytic heterogeneity correlated with histopathology, and further stratified the survival outcomes pertaining to resectable lung adenocarcinoma.

Methods

We retrospectively analyzed the 18F-fluorodeoxyglucose positron emission tomography-derived entropy and histopathology from 128 patients who had undergone curative surgery for lung adenocarcinoma. Disease-free survival (DFS) and overall survival (OS) were analyzed using univariate and multivariate Cox regression models. Independent predictors were used to construct survival prediction models.

Results

Entropy significantly correlated with histopathology, including tumor grades, lympho-vascular invasion, and visceral pleural invasion. Furthermore, entropy was an independent predictor of unfavorable DFS (p = 0.031) and OS (p = 0.004), while pathological nodal metastasis independently predicted DFS (p = 0.009). Our entropy-based models outperformed the traditional staging system (c-index = 0.694 versus 0.636, p = 0.010 for DFS; c-index = 0.704 versus 0.630, p = 0.233 for OS). The models provided further survival stratification in subgroups comprising different tumor grades (DFS: HR = 2.065, 1.315, and 1.408 for grade 1–3, p = 0.004, 0.001, and 0.039, respectively; OS: HR = 25.557, 6.484, and 2.570, for grade 1–3, p = 0.006, < 0.001, and = 0.224, respectively).

Conclusion

The glycolytic heterogeneity portrayed by entropy is associated with aggressive histopathological characteristics. The proposed entropy-based models may provide more sophisticated survival stratification in addition to histopathology and may enable personalized treatment strategies for resectable lung cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Data Availability

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.

References

  1. Ruffini E, Rena O, Oliaro A, Filosso PL, Bongiovanni M, Arslanian A, et al. Lung tumors with mixed histologic pattern. Clinico-pathologic characteristics and prognostic significance. Eur J Cardiothorac Surg. 2002;22:701–7.

    Article  Google Scholar 

  2. Barta JA, Powell CA, Wisnivesky JP. Global epidemiology of lung cancer. Ann Glob Health. 2019;85:8.

    Article  Google Scholar 

  3. Chen YH, Wang TF, Chu SC, Lin CB, Wang LY, Lue KH, et al. Incorporating radiomic feature of pretreatment 18F-FDG PET improves survival stratification in patients with EGFR-mutated lung adenocarcinoma. PLoS One. 2020;15: e0244502.

    Article  CAS  Google Scholar 

  4. Kim HC, Ji W, Lee JC, Kim HR, Song SY, Choi CM, et al. Prognostic factor and clinical outcome in stage III non-small cell lung cancer: a study based on real-world clinical data in the korean population. Cancer Res Treat. 2021;53:1033–41.

    Article  Google Scholar 

  5. Tsuta K, Kawago M, Inoue E, Yoshida A, Takahashi F, Sakurai H, et al. The utility of the proposed IASLC/ATS/ERS lung adenocarcinoma subtypes for disease prognosis and correlation of driver gene alterations. Lung Cancer. 2013;81:371–6.

    Article  Google Scholar 

  6. Sakurai H, Asamura H, Miyaoka E, Yoshino I, Fujii Y, Nakanishi Y, et al. Differences in the prognosis of resected lung adenocarcinoma according to the histological subtype: a retrospective analysis of Japanese lung cancer registry data. Eur J Cardiothorac Surg. 2014;45:100–7.

    Article  Google Scholar 

  7. Chen T, Luo J, Gu H, Gu Y, Huang Q, Wang Y, et al. Impact of solid minor histologic subtype in postsurgical prognosis of stage I lung adenocarcinoma. Ann Thorac Surg. 2018;105:302–8.

    Article  Google Scholar 

  8. Moreira AL, Ocampo PSS, Xia Y, Zhong H, Russell PA, Minami Y, et al. A grading system for invasive pulmonary adenocarcinoma: a proposal from the international association for the study of lung cancer pathology committee. J Thorac Oncol. 2020;15:1599–610.

    Article  Google Scholar 

  9. Schuchert MJ, Normolle DP, Awais O, Pennathur A, Wilson DO, Luketich JD, et al. Factors influencing recurrence following anatomic lung resection for clinical stage I non-small cell lung cancer. Lung Cancer. 2019;128:145–51.

    Article  Google Scholar 

  10. Yoshizawa A, Motoi N, Riely GJ, Sima CS, Gerald WL, Kris MG, et al. Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: prognostic subgroups and implications for further revision of staging based on analysis of 514 stage I cases. Mod Pathol. 2011;24:653–64.

    Article  CAS  Google Scholar 

  11. Hou Y, Song W, Chen M, Zhang J, Luo Q, Um SW, et al. The presence of lepidic and micropapillary/solid pathological patterns as minor components has prognostic value in patients with intermediate-grade invasive lung adenocarcinoma. Transl Lung Cancer Res. 2022;11:64–74.

    Article  CAS  Google Scholar 

  12. Chen YH, Chu SC, Wang LY, Wang TF, Lue KH, Lin CB, et al. Prognostic value of combing primary tumor and nodal glycolytic-volumetric parameters of (18)F-FDG PET in patients with non-small cell lung cancer and regional lymph node metastasis. Diagnostics (Basel). 2021;11:1065.

    Article  CAS  Google Scholar 

  13. Aragaki M, Kato T, Fujiwara-Kuroda A, Hida Y, Kaga K, Wakasa S. Preoperative identification of clinicopathological prognostic factors for relapse-free survival in clinical N1 non-small cell lung cancer: a retrospective single center-based study. J Cardiothorac Surg. 2020;15:229.

    Article  Google Scholar 

  14. Pellegrino S, Fonti R, Pulcrano A, Del Vecchio S. PET-based volumetric biomarkers for risk stratification of non-small cell lung cancer patients. Diagnostics (Basel). 2021;11:210.

    Article  CAS  Google Scholar 

  15. Sun XY, Chen TX, Chang C, Teng HH, Xie C, Ruan MM, et al. SUVmax of (18)FDG PET/CT predicts histological grade of lung adenocarcinoma. Acad Radiol. 2021;28:49–57.

    Article  Google Scholar 

  16. Kim KH, Ryu SY, Lee HY, Choi JY, Kwon OJ, Kim HK, et al. Evaluating the tumor biology of lung adenocarcinoma: a multimodal analysis. Medicine. 2019;98: e16313.

    Article  CAS  Google Scholar 

  17. Yu M, Chen S, Hong W, Gu Y, Huang B, Lin Y, et al. Prognostic role of glycolysis for cancer outcome: evidence from 86 studies. J Cancer Res Clin Oncol. 2019;145:967–99.

    Article  CAS  Google Scholar 

  18. Kim G, Kim J, Cha H, Park WY, Ahn JS, Ahn MJ, et al. Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations. Sci Rep. 2020;10:13231.

    Article  CAS  Google Scholar 

  19. Lue KH, Huang CH, Hsieh TC, Liu SH, Wu YF, Chen YH. Systemic inflammation index and tumor glycolytic heterogeneity help risk stratify patients with advanced epidermal growth factor receptor-mutated lung adenocarcinoma treated with tyrosine kinase inhibitor therapy. Cancers. 2022;14:309.

    Article  CAS  Google Scholar 

  20. Ouyang ML, Xia HW, Xu MM, Lin J, Wang LL, Zheng XW, et al. Prediction of occult lymph node metastasis using SUV, volumetric parameters and intratumoral heterogeneity of the primary tumor in T1–2N0M0 lung cancer patients staged by PET/CT. Ann Nucl Med. 2019;33:671–80.

    Article  CAS  Google Scholar 

  21. Pyka T, Bundschuh RA, Andratschke N, Mayer B, Specht HM, Papp L, et al. 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. 2015;10:100.

    Article  Google Scholar 

  22. Nakanishi K, Nakamura S, Sugiyama T, Kadomatsu Y, Ueno H, Goto M, et al. Diagnostic utility of metabolic parameters on FDG PET/CT for lymph node metastasis in patients with cN2 non-small cell lung cancer. BMC Cancer. 2021;21:983.

    Article  CAS  Google Scholar 

  23. Lue KH, Chu SC, Wang LY, Chen YC, Li MH, Chang BS, et al. Tumor glycolytic heterogeneity improves detection of regional nodal metastasis in patients with lung adenocarcinoma. Ann Nucl Med. 2022;36:256–66.

    Article  CAS  Google Scholar 

  24. Sato R, Imamura K, Semba T, Tomita Y, Saeki S, Ikeda K, et al. TGFbeta signaling activated by cancer-associated fibroblasts determines the histological signature of lung adenocarcinoma. Cancer Res. 2021;81:4751–65.

    Article  CAS  Google Scholar 

  25. Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al. The IASLC lung cancer staging project: proposals for revision of the tnm stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol. 2016;11:39–51.

    Article  Google Scholar 

  26. Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.

    Article  CAS  Google Scholar 

  27. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104–7.

    Article  Google Scholar 

  28. Zwanenburg A, Vallieres M, Abdalah MA, Aerts H, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328–38.

    Article  Google Scholar 

  29. Oliver JA, Budzevich M, Zhang GG, Dilling TJ, Latifi K, Moros EG. Variability of image features computed from conventional and respiratory-gated PET/CT images of lung cancer. Transl Oncol. 2015;8:524–34.

    Article  Google Scholar 

  30. Desseroit MC, Tixier F, Weber WA, Siegel BA, Cheze Le Rest C, Visvikis D, et al. Reliability of PET/CT shape and heterogeneity features in functional and morphologic components of non-small cell lung cancer tumors: a repeatability analysis in a prospective multicenter cohort. J Nucl Med. 2017;58:406–11.

    Article  CAS  Google Scholar 

  31. Traverso A, Wee L, Dekker A, Gillies R. Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys. 2018;102:1143–58.

    Article  Google Scholar 

  32. Xu H, Lv W, Zhang H, Ma J, Zhao P, Lu L. Evaluation and optimization of radiomics features stability to respiratory motion in (18) F-FDG 3D PET imaging. Med Phys. 2021;48:5165–78.

    Article  Google Scholar 

  33. Polley MC, Dignam JJ. Statistical considerations in the evaluation of continuous biomarkers. J Nucl Med. 2021;62:605–11.

    Article  Google Scholar 

  34. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10:7252–9.

    Article  CAS  Google Scholar 

  35. Kang L, Chen W, Petrick NA, Gallas BD. Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach. Stat Med. 2015;34:685–703.

    Article  Google Scholar 

  36. Ujiie H, Kadota K, Chaft JE, Buitrago D, Sima CS, Lee MC, et al. Solid predominant histologic subtype in resected stage I lung adenocarcinoma is an independent predictor of early, extrathoracic, multisite recurrence and of poor postrecurrence survival. J Clin Oncol. 2015;33:2877–84.

    Article  Google Scholar 

  37. Travis WD, Brambilla E, Noguchi M, Nicholson AG, Geisinger KR, Yatabe Y, et al. International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol. 2011;6:244–85.

    Article  Google Scholar 

  38. Turajlic S, Swanton C. Metastasis as an evolutionary process. Science. 2016;352:169–75.

    Article  CAS  Google Scholar 

  39. Caswell DR, Swanton C. The role of tumour heterogeneity and clonal cooperativity in metastasis, immune evasion and clinical outcome. BMC Med. 2017;15:133.

    Article  Google Scholar 

  40. Jiangdian S, Di D, Yanqi H, Yali Z, Zaiyi L, Jie T. Association between tumor heterogeneity and progression-free survival in non-small cell lung cancer patients with EGFR mutations undergoing tyrosine kinase inhibitors therapy. Conf Proc IEEE Eng Med Biol Soc. 2016;2016:1268–71.

    Google Scholar 

  41. McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168:613–28.

    Article  CAS  Google Scholar 

  42. Moon SH, Kim J, Joung JG, Cha H, Park WY, Ahn JS, et al. Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer. Eur J Nucl Med Mol Imaging. 2019;46:446–54.

    Article  CAS  Google Scholar 

  43. Sereno M, He Z, Smith CR, Baena J, Das M, Hastings RK, et al. Inclusion of multiple high-risk histopathological criteria improves the prediction of adjuvant chemotherapy efficacy in lung adenocarcinoma. Histopathology. 2021;78:838–48.

    Article  Google Scholar 

  44. Chaft JE, Shyr Y, Sepesi B, Forde PM. Preoperative and postoperative systemic therapy for operable non-small-cell lung cancer. J Clin Oncol. 2022;40:546–55.

    Article  CAS  Google Scholar 

  45. Felip E, Altorki N, Zhou C, Csőszi T, Vynnychenko I, Goloborodko O, et al. Adjuvant atezolizumab after adjuvant chemotherapy in resected stage IB–IIIA non-small-cell lung cancer (IMpower010): a randomised, multicentre, open-label, phase 3 trial. Lancet. 2021;398:1344–57.

    Article  CAS  Google Scholar 

  46. Mamdani H, Matosevic S, Khalid AB, Durm G, Jalal SI. Immunotherapy in lung cancer: current landscape and future directions. Front Immunol. 2022;13: 823618.

    Article  CAS  Google Scholar 

  47. Melosky B, Cheema P, Juergens RA, Leighl NB, Liu G, Wheatley-Price P, et al. The dawn of a new era, adjuvant EGFR inhibition in resected non-small cell lung cancer. Ther Adv Med Oncol. 2021;13:17588359211056306.

    Article  CAS  Google Scholar 

  48. Wu YL, John T, Grohe C, Majem M, Goldman JW, Kim SW, et al. Postoperative chemotherapy use and outcomes from ADAURA: osimertinib as adjuvant therapy for resected EGFR-mutated NSCLC. J Thorac Oncol. 2022;17:423–33.

    Article  CAS  Google Scholar 

  49. Steyerberg EW, Bleeker SE, Moll HA, Grobbee DE, Moons KGM. Internal and external validation of predictive models: a simulation study of bias and precision in small samples. J Clin Epidemiol. 2003;56:441–7.

    Article  Google Scholar 

  50. Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med. 2016;35:214–26.

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank the staff from the Lung Cancer Research Team of Buddhist Tzu Chi General Hospital for their assistance in retrieving the data of patients with lung adenocarcinoma. We also thank the Grant provided from Buddhist Tzu Chi Medical Foundation (TCMF-A 107-01-02(111)).

Funding

This research was funded by The Ministry of Science and Technology in Taiwan, Grant number: 110–2314-B-303–015-; Buddhist Tzu Chi Medical Foundation, Grant number: TCMF-A 107–01-02(111).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Kun-Han Lue or Sung-Chao Chu.

Ethics declarations

Conflict of interest

All authors declare that they have no potential conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants 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. This study was approved by the Institutional Review Board and Ethics Committee of Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation (protocol code IRB110-258-B and date of approval: Dec. 13th, 2021). The requirement of informed consent for this study was waived due to its retrospective nature.

Informed consent

The requirement of informed consent for this study was waived due to its retrospective nature.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 492 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, YH., Chen, YC., Lue, KH. et al. Glucose metabolic heterogeneity correlates with pathological features and improves survival stratification of resectable lung adenocarcinoma. Ann Nucl Med 37, 139–150 (2023). https://doi.org/10.1007/s12149-022-01811-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12149-022-01811-y

Keywords

Navigation