A radiomic approach to predicting nodal relapse and disease-specific survival in patients treated with stereotactic body radiation therapy for early-stage non-small cell lung cancer.



To describe the possibility of building a classifier for patients at risk of lymph node relapse and a predictive model for disease-specific survival in patients with early stage non-small cell lung cancer.


A cohort of 102 patients who received stereotactic body radiation treatment was retrospectively investigated. A set of 45 textural features was computed for the tumor volumes on the treatment planning CT images. Patients were split into two independent cohorts (70 patients, 68.9%, for training; and 32 patients, 31.4%, for validation). Three different models were built in the study. A stepwise backward linear discriminant analysis was applied to identify patients at risk of lymph node progression. The performance of the model was assessed by means of standard metrics derived from the confusion matrix. Furthermore, all textural features were correlated to survival data to build two separate predictive models for progression-free survival (PFS) and disease-specific survival (DS-OS). These models were built from the features/predictors found significant in univariate analysis and elastic net regularization by means of a multivarate Cox regression with backward selection. Low- and high-risk groups were identified by maximizing the separation by means of the Youden method.


In the total cohort (77, 75.5%, males; and 25, 24.5%, females; median age 76.6 years), 15 patients presented nodal progression at the time of analysis; 19 patients (18.6%) died because of disease-specific causes, 25 (24.5%) died from other reasons, 28 (27.5%) were alive without disease, and 30 (29.4%) with either local or distant progression. The specificity, sensitivity, and accuracy of the classifier resulted 83.1 ± 24.5, 87.4 ± 1.2, and 85.4 ± 12.5 in the validation group (coherent with the findings in the training). The area under the curve for the classifier resulted in 0.84 ± 0.04 and 0.73 ± 0.05 for training and validation, respectively. The mean time for DS-OS and PFS for the low- and high-risk subgroups of patients (in the validation groups) were 88.2 month ± 9.0 month vs. 84.1 month ± 7.8 month (low risk) and 52.7 month ± 5.9 month vs. 44.6 month ± 9.2 month (high risk), respectively.


Radiomics analysis based on planning CT images allowed a classifier and predictive models capable of identifying patients at risk of nodal relapse and high-risk of bad prognosis to be built. The radiomics signatures identified were mostly related to tumor heterogeneity.

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

Fig. 1
Fig. 2


  1. 1.

    Siegel R, Miller K, Jemal A (2018) Cancer statistics. CA Cancer J Clin 68:7–30

    Google Scholar 

  2. 2.

    Howlader N, Noone AM, Krapcho M et al (2017) SEER Cancer Statistics Review, 1975–2014, National Cancer Institute. Bethesda, MD, April 2017. https://seer.cancer.gov/csr/1975_2014/. Accessed 1st June 2019

    Google Scholar 

  3. 3.

    Aberle D, Adams A, Berg C et al (2011) Reduced lung cancer mortality with low-dose computed tomographic screening. N Engl J Med 365:395–409

    Article  Google Scholar 

  4. 4.

    Padda S, Burt B, Trakul N, Wakelee H (2014) Early-stage non-small cell lung cancer: surgery, stereotactic radiosurgery, and individualized adjuvant therapy. Semin Oncol 41:40–56

    Article  Google Scholar 

  5. 5.

    Abel S, Hasan S, Horne ZD et al (2019) Stereotactic body radiation therapy in early-stage NSCLC: historical review, contemporary evidence and future implications. Lung Cancer Management 8:1

    Article  Google Scholar 

  6. 6.

    Timmerman R, Paulus R, Galvin J et al (2010) Stereotactic body radiation therapy for inoperable early-stage lung cancer. JAMA 303:1070–1076

    CAS  Article  Google Scholar 

  7. 7.

    Timmerman R, Paulus R, Pass H et al (2018) Stereotactic body radiation therapy for operable early-stage lung cancer: findings from the NRG oncology RTOG 0618 trial. JAMA Oncol 4:1263–1266

    Article  Google Scholar 

  8. 8.

    Videtic G, Hu C, Singh A et al (2015) A randomized phase 2 study comparing 2 stereotactic body radiation therapy schedules for medically inoperable patients with stage I peripheral non-small cell lung cancer: NRG oncology RTOG 0915 (NCCTG N0927). Int J Radiat Oncol Biol Phys 93:757–764

    Article  Google Scholar 

  9. 9.

    Robinson C, DeWees T, El Naqa I et al (2013) Patterns of failure after stereotactic body radiation therapy or lobar resection for clinical stage I non-small-cell lung cancer. J Thorac Oncol 8:192–201

    Article  Google Scholar 

  10. 10.

    Chi A, Liao Z, Nguyen NP et al (2010) Systemic review of the patterns of failure following stereotactic body radiation therapy in early-stage non-small-cell lung cancer: clinical implications. Radiother Oncol 94:1–11

    Article  Google Scholar 

  11. 11.

    Foster C, Rusthoven C, Sher D et al (2019) Adjuvant chemotherapy following stereotactic body radiotherapy for early stage non-small-cell lung cancer is associated with lower overall: a national cancer database analysis. Cancer Treat Res 130:162–168

    Google Scholar 

  12. 12.

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

    Article  Google Scholar 

  13. 13.

    Lambin P, van Stiphout R, Starmans M et al (2013) Predicting outcomes in radiation oncology, multifactorial decision support systems. Nat Rev Clin Oncol 10:27–40

    Article  Google Scholar 

  14. 14.

    Aerts H, Velazquez E, Leijenaar R, Parmar C, Grossmann P, Carvalho S et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    van Timmeren J, van Elmpt W, Leijenaar R, Reymen B, Monshouwer R (2019) Bussik J er al. Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer ptients: evaluation of the added prognostic value for overall survival and locoregional recurrence. Radiother Oncol 136:78–85

    Article  Google Scholar 

  16. 16.

    Buizza G, Toma-Dasu I, Lazzeroni M, Paganelli C, Riboldi M, Chang Y et al (2018) Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans. Phys Med 54:21–29

    Article  Google Scholar 

  17. 17.

    de Jong E, van Elmpt W, Rizzo S, Colarieti A, Spitaleri G, Leijenaar R, Jochems A et al (2018) Applicability of a prognostic Ct-based radiomic signature model trained on stage I–III non small cell lung cancer in stage IV non small-cell lung cancer. Cancer Treat Res 124:6–11

    Google Scholar 

  18. 18.

    Ramella S, Fiore M, Greco C, Cordelli E, Sicilia R, Merone M et al (2018) A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients. PLoS ONE 13:e207455

    Article  Google Scholar 

  19. 19.

    Kirienko M, Cozzi L, Antonovic L, Lozza L, Fogliata A, Voulaz E et al (2018) Prediction of diease free survival by PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging 45:207–217

    Article  Google Scholar 

  20. 20.

    Kirienko M, Cozzi L, Rossi A, Voulaz E, Antonovici L, Chiti A, Sollini M (2018) Ability of FDG-PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging 45:1649–1660

    Article  Google Scholar 

  21. 21.

    Starkov P, Aguilera T, Golden D, Sholtz D, Trakul N, Maxim P et al (2019) The use of texture based radiomics CT analysis to predict outcomes in early stage non-small cell lung cancer with stereotactic ablative radiotherapy. Br J Radiol 92:20180228

    Article  Google Scholar 

  22. 22.

    Huynh E, Coroller T, Narayan V, Agrawal V, Hou Y, Romano J et al (2016) CT-based radiomic analysis of stereotactic body radiatin therapy patients with lung cancer. Radiother Oncol 120:258–266

    Article  Google Scholar 

  23. 23.

    Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C et al (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789

    CAS  Article  Google Scholar 

  24. 24.

    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 7:358

    CAS  Article  Google Scholar 

  25. 25.

    Collins G, Reitsma J, Altman D, Moons K (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Br Med J 350:g7594

    Article  Google Scholar 

  26. 26.

    Cozzi L, Franzese C, Fogliata A, Franceschini D, Navarria P, Tomatis S et al (2019) Predicting survival and local control after radiochemotherapy in locally advanced head and neck cancer by means of computed tomography based radiomics. Strahlenther Onkol 195:805–818

    Article  Google Scholar 

  27. 27.

    Youden W (1950) Index for rating diagnostic tests. Cancer 3:32–35

    CAS  Article  Google Scholar 

  28. 28.

    R Core Team A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed 1st June 2019

  29. 29.

    Huang Y, Liu Z, He L, Chen X, Pan D, Ma Z (2016) Radiomics signature: a potential biomarker for the prediction of disease free survival in early stage (I or II) non-small cell lung cancer. Radiology 281:947–957

    Article  Google Scholar 

  30. 30.

    van Timmeren J, Carvalho S, Leijenaar R, Troost E, van Elmpt W, de Ruysscher D et al (2019) Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics. PLoS ONE 14:e217536

    Article  Google Scholar 

  31. 31.

    Yu W, Tang C, Hobbs B, Li X, Koay E, Wistuba I et al (2018) Development and validation of a predictive radiomics model for clinical outcomes in stage I non-small cell lung cancer. Int J Radiat Oncol Biol Phys 102:1090–1097

    Article  Google Scholar 

  32. 32.

    Bogowicz M, Riesterer O, Ikenberg K, Stieb S, Moch H, Studer G 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 99:921–928

    Article  Google Scholar 

  33. 33.

    Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, Madabhoshi A (2018) Radiomics and radiogenomics in lung cancer: a review for the clinician. Cancer Treat Res 115:34–41

    Google Scholar 

  34. 34.

    Larue R, Van De Voorde L, van Timmeren J, Leijenaar R, Berbee M, Sosef M et al (2017) 4DCT imaging to assess radiomics feature stability: an investigation for thoracic cancers. Radiother Oncol 125:146–153

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Luca Cozzi PhD.

Ethics declarations

Conflict of interest

L. Cozzi acts as Scientific Advisor to Varian Medical Systems and is Clinical Research Scientist at Humanitas Cancer Center. D. Franceschini, F. De Rose, P. Navarria, A. Fogliata, C. Franzese, D. Pezzulla, S. Tomatis, G. Reggiori, and M. Scorsetti declare that they have no competing interests.

Additional information

The authors Davide Franceschini and Luca Cozzi contributed equally to the manuscript.

Caption Electronic Supplementary Material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Franceschini, D., Cozzi, L., De Rose, F. et al. A radiomic approach to predicting nodal relapse and disease-specific survival in patients treated with stereotactic body radiation therapy for early-stage non-small cell lung cancer.. Strahlenther Onkol 196, 922–931 (2020). https://doi.org/10.1007/s00066-019-01542-6

Download citation


  • Lung cancer
  • Stereotactic Body Radiation therapy
  • Radiomics
  • Survival