Skip to main content

CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy

European Radiology volume 32, pages 1538–1547 (2022)Cite this article

Abstract

Objectives

The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both.

Materials and methods

This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient’s CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients.

Results

The initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619–0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646–0.846).

Conclusions

Radiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future.

Key Points

  • The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646–0.846).

  • The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

Fig. 1
Fig. 2
Fig. 3

Abbreviations

ALK:

Anaplastic lymphocyte kinase

AUC:

Area under the ROC curve

CEA:

Carcinoembryonic antigen

CT:

Computed tomography

CYFRA21-1:

Cytokeratin 19 fragment antigen 21–1

EGFR:

Epidermal growth factor receptor

GLCM:

Gray Level Co-occurrence Matrix

GLDM:

Gray Level Dependence Matrix

GLRLM:

Gray Level Run Length Matrix

GLSZM:

Gray Level Size Zone Matrix

IP:

Independent predictive

LBP:

Local binary pattern

LoG:

Laplacian of Gaussian

MITK:

Medical Imaging Interaction Toolkit

MRMR:

Minimum Redundancy Maximum Relevance

NCCN:

National Comprehensive Cancer Network

NGTDM:

Neighborhood Gray Tone Difference Matrix

NSCLC:

Non-small cell lung cancer

NSE:

Neuron-specific enolase

OS:

Overall survival

PDL1:

Programmed cell death receptor ligand 1

PFS:

Progression-free survival

RECIST:

Response Evaluation Criteria in Solid Tumors

RF:

Random Forest

ROS1:

C-ros oncogene 1

TKI:

Tyrosine kinase inhibitor

VOI:

Volume of interest

References

  1. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics. CA Cancer J Clin 69(1):7–34

    PubMed  Google Scholar 

  2. Jemal A, Siegel R, Ward E et al (2008) Cancer statistics. CA Cancer J Clin 58(2):71–96

    PubMed  Google Scholar 

  3. Cancer Research UK. Types of lung cancer. www.cancerresearchuk.org/about-cancer/lung-cancer/stages-typesgrades/types. Accessed 25 Sep 2019

  4. American cancer society. Survival rates for non-small cell lung cancer. www.cancer.org. Accessed 21 Mar 2020

  5. Eberhardt WE, De Ruysscher D, Weder W et al (2015) 2nd ESMO Consensus Conference in Lung Cancer: locally advanced stage III non-small-cell lung cancer. Ann Oncol 26(8):1573–1588

    CAS  PubMed  Google Scholar 

  6. Antonia S, Villegas A, Daniel D et al (2017) Durvalumab after chemoradiotherapy in stage III non-small-cell lung cancer. N Engl J Med 377(20):1919–1929

    CAS  Article  PubMed  Google Scholar 

  7. Antonia S, Villegas A, Daniel D et al (2018) Overall survival with durvalumab after chemoradiotherapy in stage III NSCLC. N Engl J Med 379(24):2342–2350

    CAS  Article  PubMed  Google Scholar 

  8. Mok TSK, Wu Y, Kudaba I et al (2019) Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial. Lancet 393(10183):1819–1830

    CAS  PubMed  Google Scholar 

  9. Hellmann MD, Chaft JE, William WN Jr et al (2014) Pathological response after neoadjuvant chemotherapy in resectable non-small-cell lung cancers: proposal for the use of major pathological response as a surrogate endpoint. Lancet Oncol 15(1):e42–e50

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Mouillet G, Monnet E, Milleron B et al (2012) Pathologic complete response to preoperative chemotherapy predicts cure in early-stage non–small-cell lung cancer: combined analysis of two IFCT randomized trials. J Thorac Oncol 7(5):841–849

    CAS  PubMed  Google Scholar 

  11. Isobe K, Hata Y, Sakaguchi S et al (2012) Pathological response and prognosis of stage III non-small cell lung cancer patients treated with induction chemoradiation. Asia Pac J Clin Oncol 8(3):260–266

    PubMed  Google Scholar 

  12. Chetan MR, Gleeson FV (2021) Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol 31(2):1049–1058

    PubMed  Google Scholar 

  13. Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30(9):1234–1248

    PubMed  PubMed Central  Google Scholar 

  14. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446

    PubMed  PubMed Central  Google Scholar 

  15. Meng X, Xia W, Xie P et al (2019) Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol 29(6):3200–3209

    PubMed  Google Scholar 

  16. Wang X, Zhao X, Li Q et al (2019) Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT? Eur Radiol 29(11):6049–6058

    PubMed  Google Scholar 

  17. Shi L, He Y, Yuan Z et al (2018) Radiomics for response and outcome assessment for non-small cell lung cancer. Technol Cancer Res Treat 17:1533033818782788

    PubMed  PubMed Central  Google Scholar 

  18. Ramella S, Fiore M, Greco C et al (2018) A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients. PLoS One 13:e0207455

    PubMed  PubMed Central  Google Scholar 

  19. Zhang P, Yorke E, Mageras G et al (2018) Validating a predictive atlas of tumor shrinkage for adaptive radiotherapy of locally advanced lung cancer. Int J Radiat Oncol Biol Phys 102:978–986

    PubMed  PubMed Central  Google Scholar 

  20. Hunter LA, Chen YP, Zhang L et al (2016) NSCLC tumor shrinkage prediction using quantitative image features. Comput Med Imaging Graph 49:29–36

    PubMed  Google Scholar 

  21. Bera K, Velcheti V, Madabhushi A (2018) Novel quantitative imaging for predicting response to therapy: techniques and clinical applications. Am Soc Clin Oncol Educ Book 38(38):1008–1018

    PubMed  Google Scholar 

  22. Bogowicz M, Riesterer O, Ikenberg K et al (2017) Computed tomography radiomics predicts HPV status and local tumor control after definitive radiochemotherapy in head and neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys 99(4):921–928

    PubMed  Google Scholar 

  23. Avanzo M, Stancanello J, El Naqa I (2017) Beyond imaging: the promise of radiomics. Phys Med 38:122–139

    PubMed  Google Scholar 

  24. Van Griethuysen JJ, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104–e107

    PubMed  PubMed Central  Google Scholar 

  25. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577

    PubMed  Google Scholar 

  26. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ (2015) Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol 5:272

    PubMed  PubMed Central  Google Scholar 

  28. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    PubMed  Google Scholar 

  29. Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15(1):3133–3181

    Google Scholar 

  30. Coroller TP, Agrawal V, Narayan V et al (2016) Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol 119(3):480–486

    PubMed  PubMed Central  Google Scholar 

  31. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36

    CAS  PubMed  Google Scholar 

  32. Wang C, Dong X, Sun X, Zhang R, Xing L (2019) Association of radiomic features with epidermal growth factor receptor mutation status in non-small cell lung cancer and survival treated with tyrosine kinase inhibitors. Nucl Med Commun 40(11):1091–1098

    PubMed  Google Scholar 

  33. Kato H, Kanematsu M, Zhang X et al (2007) Computer-aided diagnosis of hepatic fibrosis: preliminary evaluation of MRI texture analysis using the finite difference method and an artificial neural network. AJR Am J Roentgenol 189(1):117–122

    PubMed  Google Scholar 

  34. Feng D, Zhou Y, Xing Y et al (2018) Selection of glucocorticoid-sensitive patients in interstitial lung disease secondary to connective tissue diseases population by radiomics. Ther Clin Risk Manag 14:1975–1986

    PubMed  PubMed Central  Google Scholar 

  35. Ohno Y, Fujisawa Y, Koyama H et al (2017) Dynamic contrast-enhanced perfusion area-detector CT assessed with various mathematical models: its capability for therapeutic outcome prediction for non-small cell lung cancer patients with chemoradiotherapy as compared with that of FDG-PET/CT. Eur J Radiol 86:83–91

    PubMed  Google Scholar 

  36. Holdenrieder S (2016) Biomarkers along the continuum of care in lung cancer. Scand J Clin Lab Invest Suppl 245:S40–S45

    PubMed  Google Scholar 

  37. Molina R, Marrades RM, Augé JM et al (2016) Assessment of a combined panel of six serum tumor markers for lung cancer. Am J Respir Crit Care Med 193(4):427–437

    CAS  PubMed  Google Scholar 

  38. Wojcik E, Kulpa JK (2017) Pro-gastrin-releasing peptide (ProGRP) as a biomarker in small-cell lung cancer diagnosis, monitoring and evaluation of treatment response. Lung Cancer (Auckl) 8:231–240

    CAS  Google Scholar 

  39. Lee YC, Hsieh C, Lee YL, Li C (2019) Which should be used first for ALK-positive non-small-cell lung cancer: chemotherapy or targeted therapy? A meta-analysis of five randomized trials. Medicina (Kaunas) 55(2):29

    Google Scholar 

  40. Sim EH, Yang IA, Wood-Baker R, Bowman RV, Fong KM (2018) Gefitinib for advanced non-small cell lung cancer. Cochrane Database Syst Rev 1(1):CD006847

    PubMed  Google Scholar 

  41. Noronha V, Patil VM, Joshi A et al (2020) Gefitinib versus gefitinib plus pemetrexed and carboplatin chemotherapy in EGFR-mutated lung cancer. J Clin Oncol 38(2):124–136

    CAS  PubMed  Google Scholar 

  42. Tan PS, Bilger M, de Lima LG, Acharyya S, Haaland B (2017) Meta-analysis of first-line therapies with maintenance regimens for advanced non-small-cell lung cancer (NSCLC) in molecularly and clinically selected populations. Cancer Med 6(8):1847–1860

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Ravanelli M, Farina D, Morassi M et al (2013) Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy. Eur Radiol 23(12):3450–3455

    PubMed  Google Scholar 

  44. Kim H, Park CM, Keam B et al (2017) The prognostic value of CT radiomic features for patients with pulmonary adenocarcinoma treated with EGFR tyrosine kinase inhibitors. PLoS One 12(11):e0187500

    PubMed  PubMed Central  Google Scholar 

  45. Ravanelli M, Agazzi GM, Ganeshan B et al (2018) CT texture analysis as predictive factor in metastatic lung adenocarcinoma treated with tyrosine kinase inhibitors (TKIs). Eur J Radiol 109:130–135

    PubMed  Google Scholar 

  46. Yamamoto S, Korn RL, Oklu R et al (2014) ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization. Radiology 272(2):568–576

    PubMed  Google Scholar 

  47. Song J, Shi J, Dong D et al (2018) A new approach to predict progression-free survival in stage IV EGFR-mutant NSCLC patients with EGFR-TKI therapy. Clin Cancer Res 24(15):3583–3592

    CAS  PubMed  Google Scholar 

  48. Jian J, Xiong F, Xia W et al (2018) Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images. Australas Phys Eng Sci Med 41(2):393–401

    PubMed  Google Scholar 

  49. Huang L, Xia W, Zhang B, Qiu B, Gao X (2017) MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images. Comput Methods Programs Biomed 143:67–74

    PubMed  Google Scholar 

Download references

Acknowledgements

This thesis would not have been possible without the consistent and valuable reference materials that I received from my supervisors, whose insightful guidance and enthusiastic encouragement in the course of my shaping this thesis definitely gained my deepest gratitude.

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers 81871439; 61801474; 81872475; 81372413); Guangdong Provincial Key Research and Development Program (grant number 2019B010152001); Chinese Academy of Sciences-Iranian Vice Presidency for Science and Technology Silk Road Science Fund (grant number GJHZ1857); Science and Technology Plan Projects of Jiangsu (grant number BE2019665); Shandong Key Research and Development Plan (grant numbers 2017CXGC1209; 2017GSF18164); Outstanding Youth Natural Science Foundation of Shandong Province (grant number JQ201423); Jinan Clinical Medicine Science and Technology Innovation Plan (grant number 201704095); National Key Research and Development Program of China (grant number 2016YFC0904700).

Author information

Author notes

  1. Fengchang Yang and Jiayi Zhang contributed equally and are co-first authors.

Authors and Affiliations

  1. Department of Radiology, Shandong Cancer Hospital and Institute, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China

    Fengchang Yang

  2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88 Keling Road, Suzhou New District, Suzhou, 215163, Jiangsu, China

    Jiayi Zhang, Wei Xia, Rui Zhang, Xingyu Zhao, Junming Jian & Xin Gao

  3. School of Mathematics and Information Engineering, Wenzhou University Oujiang College, Wenzhou, 325035, Zhejiang, China

    Liu Zhou

  4. Department of Radiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250012, Shandong, China

    Haifeng Wei

  5. Cheeloo College of Medicine, Shandong University, Jinan, 250000, Shandong, China

    Jinxue Feng

  6. Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Pharmaceutical Valley New Drug Creation Platform, Jinan New District, No. 3 Building, Jinan, 250101, Shandong, China

    Xin Gao

  7. Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong University, No.440 Jiyan Road, Huaiyin District, Jinan, 250100, Shandong, China

    Shuanghu Yuan

  8. Shandong First Medical University and Shandong Academy of Medical Sciences, No. 6699 Qingdao Road, Jinan, 250117, Shandong, China

    Shuanghu Yuan

  9. Zhengzhou University, No. 100 Science Road, Zhengzhou, 450001, Henan, China

    Shuanghu Yuan

Authors
  1. Fengchang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiayi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Haifeng Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jinxue Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xingyu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Junming Jian
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Shuanghu Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xin Gao or Shuanghu Yuan.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Shuanghu Yuan.

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

Jiayi Zhang kindly provided statistical advice for this manuscript.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• randomized controlled trial

• performed at one institution

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 28 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yang, F., Zhang, J., Zhou, L. et al. CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy. Eur Radiol 32, 1538–1547 (2022). https://doi.org/10.1007/s00330-021-08277-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-021-08277-y

Keywords

  • Tomography, X-ray computed
  • Non-small cell lung cancer
  • Radiomics
  • Random forest
  • Biomarkers