Skip to main content
SpringerLink
Log in

A Model-Strengthened Imaging Biomarker for Survival Prediction in EGFR-Mutated Non-small-cell Lung Carcinoma Patients Treated with Tyrosine Kinase Inhibitors

  • Original Article
  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript

Abstract

Non-small-cell lung carcinoma is a frequent type of lung cancer with a bad prognosis. Depending on the stage and genomics, several therapeutical approaches are used. Tyrosine Kinase Inhibitors (TKI) may be successful for a time in the treatment of EGFR-mutated non-small cells lung carcinoma. Our objective is here to introduce a survival assessment as their efficacy in the long run is challenging to evaluate. The study includes 17 patients diagnosed with EGFR-mutated non-small cell lung cancer and exposed to an EGFR-targeting TKI with 3 computed tomography (CT) scans of the primary tumor (one before the TKI introduction and two after). An imaging biomarker based on evolution of texture heterogeneity between the first and the third exams is derived and computed from a mathematical model and patient data. Defining the overall survival as the time between the introduction of the TKI treatment and the patient death, we obtain a statistically significant correlation between the overall survival and our imaging marker (\(p=0.006\)). Using the ROC curve, the patients are separated into two populations and the comparison of the survival curves is statistically significant (\(p=0.025\)). The baseline exam seems to have a significant role in the prediction of response to TKI treatment. More precisely, our imaging biomarker defined using only the CT scan before the TKI introduction allows to determine a first classification of the population which is improved over time using the imaging marker as soon as more CT scans are available. This exploratory study leads us to think that it is possible to obtain a survival assessment using only few CT scans of the primary tumor.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, Cavalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P (2014) Decoding Tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006

    Article  Google Scholar 

  • Billy F, Ribba B, Saut O, Morre-Trouilhet H, Colin T, Bresch D, Boissel JP, Grenier E, Flandrois JP (2009) A pharmacologically based multiscale mathematical model of angiogenesis and its use in investigating the efficacy of a new cancer treatment strategy. J Theor Biol 260(4):545–562

    Article  MathSciNet  Google Scholar 

  • Cadranel J, Ruppert AM, Beau-Faller M, Wislez M (2013) Therapeutic strategy for advanced EGFR mutant non-small-cell lung carcinoma. Crit Rev Oncol/Hematol 88(3):477–493

    Article  Google Scholar 

  • Coroller TP, Agrawal V, Narayan V, Hou Y, Grossmann P, Lee SW, Mak RH, Aerts HJWL (2016) Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol 119(3):480–486. https://doi.org/10.1016/j.radonc.2016.04.004

    Article  Google Scholar 

  • Dong Y, Zhou Z, Wang J, Ma L, Liu Z, Wang Y, Song J, Zhang S, Che N (2019) Origin of the T790m mutation and its impact on the clinical outcomes of patients with lung adenocarcinoma receiving EGFR-TKIs. Pathol Res Pract 215:946–951

    Article  Google Scholar 

  • Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M (2009) New response evaluation criteria in solid Tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247

    Article  Google Scholar 

  • Fave X, Zhang L, Yang J, Mackin D, Balter P, Gomez D, Followill D, Jones AK, Stingo F, Liao Z (2017) Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep 7(1):588

    Article  Google Scholar 

  • 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(4):796–802

    Article  Google Scholar 

  • Greenhalgh J, Dwan K, Boland A, Bates V, Vecchio F, Dundar Y, Jain P, Green JA (2016) First-line treatment of advanced epidermal growth factor receptor (EGFR) mutation positive non-squamous non-small cell lung cancer. The Cochrane Library, Hoboken

    Book  Google Scholar 

  • Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C, Leijenaar RT, Haibe-Kains B, Lambin P, Gillies RJ, Aerts HJ (2017) Defining the biological basis of radiomic phenotypes in lung cancer. Elife 6:e23421. https://doi.org/10.7554/eLife.23421

  • Helena AY, Sima CS, Huang J, Solomon SB, Rimner A, Paik P, Pietanza MC, Azzoli CG, Rizvi NA, Krug LM (2013) Local therapy with continued EGFR tyrosine kinase inhibitor therapy as a treatment strategy in EGFR-mutant advanced lung cancers that have developed acquired resistance to EGFR tyrosine kinase inhibitors. J Thorac Oncol 8(3):346–351

    Article  Google Scholar 

  • Hwang KE, Kim HR (2017) Response evaluation of chemotherapy for lung cancer. Tuberc Respir Dis 80(2):136–142

    Article  Google Scholar 

  • Jiang B, Zhou D, Sun Y, Wang J (2017) Systematic analysis of measurement variability in lung cancer with multidetector computed tomography. Ann Thorac Med 12(2):95

    Article  Google Scholar 

  • Kim H, Chae KJ, Yoon SH, Kim M, Keam B, Kim TM, Kim DW, Goo JM, Park CM (2018) Repeat biopsy of patients with acquired resistance to EGFR TKIs: implications of biopsy-related factors on T790m mutation detection. Eur Radiol 28(2):861–868. https://doi.org/10.1007/s00330-017-5006-6

    Article  Google Scholar 

  • Lachaud JO, Taton B (2005) Deformable model with a complexity independent from image resolution. Comput Vis Image Underst 99(3):453–475

    Article  Google Scholar 

  • Lee JH, Lee HY, Ahn MJ, Park K, Ahn JS, Sun JM, Lee KS (2016) Volume-based growth tumor kinetics as a prognostic biomarker for patients with EGFR mutant lung adenocarcinoma undergoing EGFR tyrosine kinase inhibitor therapy: a case control study. Cancer Imaging 16(1):5

    Article  Google Scholar 

  • Lee CK, Lord S, Marschner I, Wu YL, Sequist L, Rosell R, Fukuoka M, Mitsudomi T, Asher R, Davies L (2018) others: The value of early depth of response in predicting long-term outcome in EGFR-mutant lung cancer. J Thorac Oncol 13(6):792–800

    Article  Google Scholar 

  • Nishino M, Guo M, Jackman DM, DiPiro PJ, Yap JT, Ho TK, Hatabu H, Jänne PA, Van den Abbeele AD, Johnson BE (2011) CT tumor volume measurement in advanced non-small-cell lung cancer: performance characteristics of an emerging clinical tool. Acad Radiol 18(1):54–62

    Article  Google Scholar 

  • Nishino M, Jagannathan JP, Krajewski KM, O’Regan K, Hatabu H, Shapiro G, Ramaiya NH (2012) Personalized tumor response assessment in the era of molecular medicine: cancer-specific and therapy-specific response criteria to complement pitfalls of RECIST. Am J Roentgenol 198(4):737–745

    Article  Google Scholar 

  • Nishino M, Dahlberg SE, Fulton LE, Digumarthy SR, Hatabu H, Johnson BE, Sequist LV (2016) Volumetric tumor response and progression in EGFR-mutant NSCLC patients treated with erlotinib or gefitinib. Acad Radiol 23(3):329–336

    Article  Google Scholar 

  • Park S, Ha S, Lee SH, Paeng JC, Keam B, Kim TM, Kim DW, Heo DS (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(1):e0189766. https://doi.org/10.1371/journal.pone.0189766

    Article  Google Scholar 

  • Ribba B, Colin T, Schnell S (2006a) A multiscale mathematical model of cancer, and its use in analyzing irradiation therapies. Theor Biol Med Model 3:7. https://doi.org/10.1186/1742-4682-3-7

  • Ribba B, Saut O, Colin T, Bresch D, Grenier E, Boissel JP (2006b) A multiscale mathematical model of avascular tumor growth to investigate the therapeutic benefit of anti-invasive agents. J Theor Biol 243(4):532–541

    Article  MathSciNet  Google Scholar 

  • 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(1):358. https://doi.org/10.1038/s41598-017-00426-y

    Article  Google Scholar 

  • van Meerten ELVP, Gelderblom H, Bloem JL (2010) RECIST revised: implications for the radiologist: a review article on the modified RECIST guideline. Eur Radiol 20(6):1456–1467

    Article  Google Scholar 

  • Zhang L, Chen B, Liu X, Song J, Fang M, Hu C, Dong D, Li W, Tian J (2018) Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer. Transl Oncol 11(1):94–101. https://doi.org/10.1016/j.tranon.2017.10.012

    Article  Google Scholar 

  • Zhou H, Dong D, Chen B, Fang M, Cheng Y, Gan Y, Zhang R, Zhang L, Zang Y, Liu Z, Zheng H, Li W, Tian J (2018) Diagnosis of distant metastasis of lung cancer: based on clinical and radiomic features. Transl Oncol 11(1):31–36. https://doi.org/10.1016/j.tranon.2017.10.010

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the French Laboratory of Excellence TRAIL ANR-10-LABX-57.

Author information

Authors and Affiliations

  1. Univ. Bordeaux, CNRS, Bordeaux INP, IMB, UMR 5251, INRIA Monc, 33400, Talence, France

    Annabelle Collin, Vladimir Groza & Olivier Saut

  2. Institut Bergonié, 33076, Bordeaux, France

    Louise Missenard, François Chomy & Jean Palussière

  3. Sophia Genetics, Cité de la Photonique, 33600, Pessac, France

    Thierry Colin

Authors
  1. Annabelle Collin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vladimir Groza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Louise Missenard
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. François Chomy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thierry Colin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jean Palussière
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Olivier Saut
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olivier Saut.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Collin, A., Groza, V., Missenard, L. et al. A Model-Strengthened Imaging Biomarker for Survival Prediction in EGFR-Mutated Non-small-cell Lung Carcinoma Patients Treated with Tyrosine Kinase Inhibitors. Bull Math Biol 83, 68 (2021). https://doi.org/10.1007/s11538-021-00902-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11538-021-00902-7

Keywords

Search

Navigation