Skip to main content

Advertisement

SpringerLink
Log in

Development and validation of a computed tomography–based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To date, there are no data on the noninvasive surrogate of intratumoural immune status that could be prognostic of survival outcomes in non-small cell lung cancer (NSCLC). We aimed to develop and validate the immune ecosystem diversity index (iEDI), an imaging biomarker, to indicate the intratumoural immune status in NSCLC. We further investigated the clinical relevance of the biomarker for survival prediction.

Methods

In this retrospective study, two independent NSCLC cohorts (Resec1, n = 149; Resec2, n = 97) were included to develop and validate the iEDI to classify the intratumoural immune status. Paraffin-embedded resected specimens in Resec1 and Resec2 were stained by immunohistochemistry, and the density percentiles of CD3+, CD4+, and CD8+ T cells to all cells were quantified to estimate intratumoural immune status. Then, EDI features were extracted using preoperative computed tomography to develop an imaging biomarker, called iEDI, to determine the immune status. The prognostic value of iEDI was investigated on NSCLC patients receiving surgical resection (Resec1; Resec2; internal cohort Resec3, n = 419; external cohort Resec4, n = 96; and TCIA cohort Resec5, n = 55).

Results

iEDI successfully classified immune status in Resec1 (AUC 0.771, 95% confidence interval [CI] 0.759–0.783; and 0.770 through internal validation) and Resec2 (0.669, 0.647–0.691). Patients with higher iEDI-score had longer overall survival (OS) in Resec3 (unadjusted hazard ratio 0.335, 95%CI 0.206–0.546, p < 0.001), Resec4 (0.199, 0.040–1.000, p < 0.001), and TCIA (0.303, 0.098–0.944, p = 0.001).

Conclusions

iEDI is a non-invasive surrogate of intratumoural immune status and prognostic of OS for NSCLC patients receiving surgical resection.

Key Points

• Decoding tumour immune microenvironment enables advanced biomarkers identification.

• Immune ecosystem diversity index characterises intratumoural immune status noninvasively.

• Immune ecosystem diversity index is prognostic for NSCLC patients.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

AJCC:

American Joint Committee Cancer

AUC:

Area under the ROC curve

CI:

Confidence interval

iAUC:

Integrated area under the ROC curve

iBS:

Integrated Brier score

iEDI:

Immune ecosystem diversity index

LASSO:

The least absolute shrinkage and selection operator

NCCN:

National Comprehensive Cancer Network

NNE:

Nearest Neighbor Estimation

NSCLC:

Non-small cell lung cancer

OS:

Overall survival

ROC:

Receiver operating characteristic

TIME:

Tumour immune microenvironment

TNM:

Tumour-node-metastasis

References

  1. Siegel RL, Miller KD, Jemal A (2018) Cancer statistics. CA Cancer J Clin 68(1):7–30

    Article  PubMed  Google Scholar 

  2. Edge S, Byrd D, Compton C, Fritz A, Greene F, Trotti A (2010) AJCC cancer staging manual, 7th edn. Springer, Berlin Heidelberg New York

    Google Scholar 

  3. Donnem T, Hald SM, Paulsen EE et al (2015) Stromal CD8+ T-cell density-a promising supplement to TNM staging in non-small cell lung cancer. Clin Cancer Res 21(11):2635–2643

    Article  PubMed  Google Scholar 

  4. Donnem T, Kilvaer TK, Andersen S et al (2016) Strategies for clinical implementation of TNM-Immunoscore in resected nonsmall-cell lung cancer. Ann Oncol 27(2):225–232

    Article  CAS  PubMed  Google Scholar 

  5. Junttila MR, de Sauvage FJ (2013) Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 501(7467):346–354

    Article  CAS  PubMed  Google Scholar 

  6. Natrajan R, Sailem H, Mardakheh FK et al (2016) Microenvironmental heterogeneity parallels breast cancer progression: a histology-genomic integration analysis. PLoS Med 13(2):e1001961

    Article  PubMed  PubMed Central  Google Scholar 

  7. Binnewies M, Roberts EW, Kersten K et al (2018) Understanding the tumour immune microenvironment (TIME) for effective therapy. Nat Med 24(5):541–350

  8. Fridman WH, Pages F, Sautes-Fridman C, Galon J (2012) The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cance 12(4):298–306

    Article  CAS  Google Scholar 

  9. Bremnes RM, Busund LT, Kilvaer TL et al (2016) The role of tumour-infiltrating lymphocytes in development, progression, and prognosis of non-small cell lung cancer. J Thorac Oncol 11(6):789–800

  10. Galon J, Mlecnik B, Bindea G et al (2014) Towards the introduction of the 'Immunoscore' in the classification of malignant tumours. J Pathol 232(2):199–209

    Article  CAS  PubMed  Google Scholar 

  11. Sun R, Limkin EJ, Vakalopoulou M et al (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(9):1180–1191

    Article  CAS  PubMed  Google Scholar 

  12. Rizvi NA, Hellmann MD, Snyder A et al (2015) Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348(6230):124–128

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Tumeh PC, Harview CL, Yearley JH et al (2014) PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515(7528):568–571

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Herbst RS, Soria JC, Kowanetz M et al (2014) Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515(7528):563–567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Li B, Cui Y, Diehn M, Li R (2017) Development and validation of an individualized immune prognostic signature in early-stage nonsquamous non-small cell lung cancer. JAMA Oncol 3(11):1529–1537

    Article  PubMed  PubMed Central  Google Scholar 

  16. Steele KE, Tan TH, Korn R et al (2018) Measuring multiple parameters of CD8+ tumour-infiltrating lymphocytes in human cancers by image analysis. J Immunother Cancer 6(1):20

  17. Yan X, Jiao SC, Zhang GQ, Guan Y, Wang JL (2017) Tumor-associated immune factors are associated with recurrence and metastasis in non-small cell lung cancer. Cancer Gene Ther 24(2):57–63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Domagala-Kulawik J, Raniszewska A (2017) How to evaluate the immune status of lung cancer patients before immunotherapy. Breathe 13(4):291–296

    Article  PubMed  PubMed Central  Google Scholar 

  19. Grossmann P, Stringfield O, El-Hachem N et al (2017) Defining the biological basis of radiomic phenotypes in lung cancer. Elife 6:e23421

    Article  PubMed  PubMed Central  Google Scholar 

  20. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    Article  CAS  PubMed  Google Scholar 

  21. National Comprehensive Cancer Network (NCCN) (2014) NCCN clinical practice guidelines in oncology: non-small cell lung cancer, V4. 2014. http://www.nccn.org/professionals/physician_gls/pdf/nscl.pdf. Accessed August 15, 2014.

  22. Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56(2):337–344

    Article  CAS  PubMed  Google Scholar 

  23. Fraley C, Raftery AE (2002) Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97(458):611–631

    Article  Google Scholar 

  24. El Naqa I, Ten Haken RK (2018) Can radiomics personalise immunotherapy? Lancet Oncol 19(9):1138–1139

    Article  PubMed  Google Scholar 

  25. Maley CC, Koelble K, Natrajan R, Aktipis A, Yuan Y (2015) An ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer. Breast Cancer Res 17(1):131

    Article  PubMed  PubMed Central  Google Scholar 

  26. Maley CC, Aktipis A, Graham TA et al (2017) Classifying the evolutionary and ecological features of neoplasms. Nat Rev Cancer 17(10):605–619

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Pienta KJ, McGregor N, Axelrod R, Axelrod DE (2008) Ecological therapy for cancer: defining tumours using an ecosystem paradigm suggests new opportunities for novel cancer treatments. Transl Oncol 1(4):158–164

  28. Merlo LM, Pepper JW, Reid BJ, Maley CC (2016) Cancer as an evolutionary and ecological process. Nat Rev Cancer 6(12):924–935

    Article  Google Scholar 

  29. Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328–338

  30. Tang C, Hobbs B, Amer A et al (2018) Development of an immune-pathology informed radiomics model for non-small cell lung cancer. Sci Rep 8(1):1922

    Article  PubMed  PubMed Central  Google Scholar 

  31. Aerts HJ (2016) The Potential of Radiomic-Based Phenotyping in Precision Medicine: a Review. JAMA Oncol 2(12):1636–1642

    Article  PubMed  Google Scholar 

  32. Jiang Y, Wang H, Wu J et al (2020) Noninvasive imaging evaluation of tumour immune microenvironment to predict outcomes in gastric cancer. Ann Oncol 31(6):760–768

  33. Rakaee M, Kilvaer TK, Dalen SM et al (2018) Evaluation of tumor-infiltrating lymphocytes using routine H&E slides predicts patient survival in resected non-small cell lung cancer. Hum Pathol 79:188–198

  34. Ogino S, Giannakis M (2018) Immunoscore for (colorectal) cancer precision medicine. Lancet 391(10135):2084–2086

    Article  PubMed  PubMed Central  Google Scholar 

  35. Salgado R, Denkert C, Demaria S et al (2015) The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann Oncol 26(2):259–271

    Article  CAS  PubMed  Google Scholar 

  36. Ferris RL, Galon J (2016) Additional support for the introduction of immune cell quantification in colorectal cancer classification. J Natl Cancer Inst 108(8):djw033.

  37. Ascierto PA, Capone M, Urba WJ et al (2013) The additional facet of immunoscore: immunoprofiling as a possible predictive tool for cancer treatment. J Transl Med 11:54

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank The Cancer Imaging Archive.

Funding

This study was supported by the National Key Research and Development Program of China (grant number 2021YFF1201003), the Key R&D Program of Guangdong Province of China (grant number 2021B0101420006), the National Science Fund for Distinguished Young Scholars (grant number 81925023), and the National Natural Scientific Foundation of China (grant number 81771912, 81901910, 82071892, and 82001986), the High-level Hospital Construction Project (DFJHBF202105). The funding sources had no involvement in study design, in the collection, analysis, and interpretation of data, in the writing of the report, and in the decision to submit the article for publication.

Author information

Author notes

  1. Lan He and Zhen-Hui Li contributed equally as co-first authors.

  2. Yan-Qi Huang and Zai-Yi Liu contributed equally as co-senior authors.

Authors and Affiliations

  1. Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China

    Lan He, Zhen-Hui Li, Chang-Hong Liang, Yan-Qi Huang & Zai-Yi Liu

  2. Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China

    Zhen-Hui Li

  3. Department of Pathology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Li-Xu Yan

  4. Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China

    Xin Chen

  5. The D-lab and the M-lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands

    Sebastian Sanduleanu & Phillippe Lambin

  6. Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Wen-Zhao Zhong

  7. Department of Radiology and Nuclear Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands

    Phillippe Lambin

  8. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China

    Zhao-Xiang Ye

  9. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China

    Ying-Shi Sun

  10. Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Yu-Lin Liu & Qian Yang

  11. Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China

    Jin-Rong Qu

  12. Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China

    Lin Wu

  13. Department of Cadres Medical Oncology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China

    Chang-Ling Tu

  14. Department of Internal Medicine, Cliniques Universitaires St-Luc, Université Catholique de Louvain, Brussels, Belgium

    Madeleine Scrivener

  15. Departement of Pneumology, Cliniques Universitaires St-Luc, Université Catholique de Louvain, Brussels, Belgium

    Thierry Pieters

  16. Department of Radiology, Cliniques Universitaires St-Luc, Université Catholique de Louvain, Brussels, Belgium

    Emmanuel Coche

  17. Department of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Mei Yang

  18. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China

    Yan-Qi Huang

  19. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Chang-Hong Liang & Zai-Yi Liu

Authors
  1. Lan He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen-Hui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li-Xu Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sebastian Sanduleanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wen-Zhao Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Phillippe Lambin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhao-Xiang Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ying-Shi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yu-Lin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jin-Rong Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Lin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Chang-Ling Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Madeleine Scrivener
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Thierry Pieters
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Emmanuel Coche
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Qian Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Mei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Chang-Hong Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Yan-Qi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Zai-Yi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yan-Qi Huang or Zai-Yi Liu.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Zaiyi Liu.

Conflict of interest

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.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• prognostic study

• multicentre study

Additional information

Publisher’s note

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

Supplementary information

ESM 1

(DOCX 1648 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, L., Li, ZH., Yan, LX. et al. Development and validation of a computed tomography–based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer. Eur Radiol 32, 8726–8736 (2022). https://doi.org/10.1007/s00330-022-08873-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-022-08873-6

Keywords

Search

Navigation