Radiomic analysis of planning computed tomograms for predicting radiation-induced lung injury and outcome in lung cancer patients treated with robotic stereotactic body radiation therapy

  • Khaled Bousabarah
  • Susanne Temming
  • Mauritius Hoevels
  • Jan Borggrefe
  • Wolfgang W. Baus
  • Daniel Ruess
  • Veerle Visser-Vandewalle
  • Maximilian Ruge
  • Martin KocherEmail author
  • Harald Treuer
Original Article



To predict radiation-induced lung injury and outcome in non-small cell lung cancer (NSCLC) patients treated with robotic stereotactic body radiation therapy (SBRT) from radiomic features of the primary tumor.


In all, 110 patients with primary stage I/IIa NSCLC were analyzed for local control (LC), disease-free survival (DFS), overall survival (OS) and development of local lung injury up to fibrosis (LF). First-order (histogram), second-order (GLCM, Gray Level Co-occurrence Matrix) and shape-related radiomic features were determined from the unprocessed or filtered planning CT images of the gross tumor volume (GTV), subjected to LASSO (Least Absolute Shrinkage and Selection Operator) regularization and used to construct continuous and dichotomous risk scores for each endpoint.


Continuous scores comprising 1–5 histogram or GLCM features had a significant (p = 0.0001–0.032) impact on all endpoints that was preserved in a multifactorial Cox regression analysis comprising additional clinical and dosimetric factors. At 36 months, LC did not differ between the dichotomous risk groups (93% vs. 85%, HR 0.892, 95%CI 0.222–3.590), while DFS (45% vs. 17%, p < 0.05, HR 0.457, 95%CI 0.240–0.868) and OS (80% vs. 37%, p < 0.001, HR 0.190, 95%CI 0.065–0.556) were significantly lower in the high-risk groups. Also, the frequency of LF differed significantly between the two risk groups (63% vs. 20% at 24 months, p < 0.001, HR 0.158, 95%CI 0.054–0.458).


Radiomic analysis of the gross tumor volume may help to predict DFS and OS and the development of local lung fibrosis in early stage NSCLC patients treated with stereotactic radiotherapy.


Image analysis Radiobiology Machine learning Toxicity Biomarker 

Radiomics-Analyse von Planungs-Computertomogrammen zur Vorhersage von strahleninduzierter Lungenschädigung und onkologischem Ergebnis bei Lungenkrebspatienten nach robotischer stereotaktischer Strahlentherapie



Vorhersage von pulmonaler Toxizität und onkologischem Ergebnis aus Radiomics-Merkmalen des Primärtumors bei Patienten mit nicht-kleinzelligem Bronchialkarzinom (NSCLC), die mittels robotischer stereotaktischer Strahlentherapie (SBRT) behandelt wurden.


Insgesamt 110 Patienten mit NSCLC im Stadium I/IIa wurden bzgl. lokaler Kontrolle (LC), krankheitsfreiem Überleben (DFS), Gesamtüberleben (OS) und Entwicklung einer lokalen Lungenfibrose (LF) untersucht. Merkmale erster Ordnung (Histogramm), zweiter Ordnung (GLCM, Gray-Level Co-Occurence Matrix) und formbezogene Merkmale wurden aus den unverarbeiteten oder gefilterten Planungs-CT-Bildern des makroskopischen Tumorvolumens (GTV) bestimmt, mittels LASSO (Least Absolute Shrinkage and Selection Operator) regularisiert und für die Konstruktion von kontinuierlichen und dichotomen Risikoscores für jeden Endpunkt verwendet.


Kontinuierliche Scores aus 1–5 Histogramm- oder GLCM-Merkmalen hatten einen signifikanten Einfluss auf alle Endpunkte (p = 0,0001–0,032), der in einer multifaktoriellen Cox-Regressionsanalyse mit zusätzlichen klinischen und dosimetrischen Faktoren erhalten blieb. Nach 36 Monaten unterschied sich die LC nicht zwischen den dichotomen Risikogruppen (93% vs. 85%; HR 0,892; 95%-KI 0,222–3,590), während das DFS (45% vs. 17%; p < 0,05; HR 0,457; 95%-KI 0,240–0,868) und das OS (80% vs. 37%; p < 0,001; HR 0,190; 95%-KI 0,065–0,556) in den Hochrisikogruppen signifikant schlechter waren. Auch die Häufigkeit von LF unterschied sich signifikant zwischen den beiden Risikogruppen (63% gegenüber 20% nach 24 Monaten, p < 0,001; HR 0,158; 95%-KI 0,054–0,458).


Die Radiomics-Analyse des GTV aus dem Planungs-CT kann zur Vorhersage der Prognose und zur Einschätzung des Risikos der Entwicklung einer lokalen Lungenfibrose nach stereotaktischer Bestrahlung von Bronchialkarzinomen beitragen.


Bildverarbeitung Strahlenbiologie Maschinenlernen Toxizität Biomarker 


Compliance with ethical guidelines

Conflict of interest

K. Bousabarah, S. Temming, M. Hoevels, J. Borggrefe, W.W. Baus, D. Ruess, V. Visser-Vandewalle, M. Ruge, M. Kocher and H. Treuer declare that they have no competing interests.

Ethical standards

This retrospective study was approved by the Ethics Committee of the Medical Faculty, University of Cologne, protocol number 17-009.

Supplementary material

66_2019_1452_MOESM1_ESM.pdf (96 kb)
Radiomic feature definition and processing


  1. 1.
    Peeken JC, Nusslin F, Combs SE (2017) “Radio-oncomics” : The potential of radiomics in radiation oncology. Strahlenther Onkol 193:767–779CrossRefPubMedGoogle Scholar
  2. 2.
    Peeken JC, Hesse J, Haller B, Kessel KA, Nusslin F, Combs SE (2018) Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients. Strahlenther Onkol 194:580–590CrossRefPubMedGoogle Scholar
  3. 3.
    Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMedGoogle Scholar
  5. 5.
    Pinker K, Shitano F, Sala E et al (2017) Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging. PubMedPubMedCentralGoogle Scholar
  6. 6.
    Verma V, Simone CB 2nd, Krishnan S, Lin SH, Yang J, Hahn SM (2017) The rise of Radiomics and implications for oncologic management. J Natl Cancer Inst 109(7). PubMedGoogle Scholar
  7. 7.
    Abrol S, Kotrotsou A, Salem A, Zinn PO, Colen RR (2017) Radiomic Phenotyping in brain cancer to unravel hidden information in medical images. Top Magn Reson Imaging 26:43–53PubMedGoogle Scholar
  8. 8.
    Braman NM, Etesami M, Prasanna P et al (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 19:57CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Katsila T, Matsoukas MT, Patrinos GP, Kardamakis D (2017) Pharmacometabolomics informs quantitative Radiomics for Glioblastoma diagnostic innovation. OMICS 21:429–439CrossRefPubMedGoogle Scholar
  10. 10.
    Lopez CJ, Nagornaya N, Parra NA et al (2017) Association of Radiomics and metabolic tumor volumes in radiation treatment of Glioblastoma Multiforme. Int J Radiat Oncol Biol Phys 97:586–595CrossRefPubMedGoogle Scholar
  11. 11.
    Parekh VS, Jacobs MA (2017) Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI. NPJ Breast Cancer 3:43CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Shiradkar R, Podder TK, Algohary A, Viswanath S, Ellis RJ, Madabhushi A (2016) Radiomics based targeted radiotherapy planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI. Radiat Oncol 11:148CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Vallieres M, Kay-Rivest E, Perrin LJ et al (2017) Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep 7(1). Google Scholar
  14. 14.
    Huang Y, Liu Z, He L et al (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 281:947–957CrossRefPubMedGoogle Scholar
  15. 15.
    Huang YQ, Liang CH, He L et al (2016) Development and validation of a Radiomics nomogram for preoperative prediction of lymph node metastasis in Colorectal cancer. J Clin Oncol 34:2157–2164CrossRefPubMedGoogle Scholar
  16. 16.
    Coroller TP, Agrawal V, Huynh E et al (2017) Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol 12:467–476CrossRefPubMedGoogle Scholar
  17. 17.
    Coroller TP, Agrawal V, Narayan V et al (2016) Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol 119:480–486CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Coroller TP, Grossmann P, Hou Y et al (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114:345–350CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Grove O, Berglund AE, Schabath MB et al (2015) Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS ONE 10(3):e118261. CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Lee G, Lee HY, Park H et al (2017) Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. Eur J Radiol 86:297–307CrossRefPubMedGoogle Scholar
  21. 21.
    Postmus PE, Kerr KM, Oudkerk M et al (2017) Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 28(suppl_4):iv1–iv21. CrossRefPubMedGoogle Scholar
  22. 22.
    Folkert MR, Timmerman RD (2017) Stereotactic ablative body radiosurgery (SABR) or Stereotactic body radiation therapy (SBRT). Adv Drug Deliv Rev 109:3–14CrossRefPubMedGoogle Scholar
  23. 23.
    Ma L, Wang L, Tseng CL, Sahgal A (2017) Emerging technologies in stereotactic body radiotherapy. Chin Clin Oncol 6(S2):S12. CrossRefPubMedGoogle Scholar
  24. 24.
    Maquilan G, Timmerman R (2016) Stereotactic body radiation therapy for early-stage lung cancer. Cancer J 22:274–279CrossRefPubMedGoogle Scholar
  25. 25.
    Videtic GMM, Donington J, Giuliani M et al (2017) Stereotactic body radiation therapy for early-stage non-small cell lung cancer: Executive summary of an ASTRO evidence-based guideline. Pract Radiat Oncol 7:295–301CrossRefPubMedGoogle Scholar
  26. 26.
    Guckenberger M, Andratschke N, Dieckmann K et al (2017) ESTRO ACROP consensus guideline on implementation and practice of stereotactic body radiotherapy for peripherally located early stage non-small cell lung cancer. Radiother Oncol 124:11–17CrossRefPubMedGoogle Scholar
  27. 27.
    Guckenberger M, Klement RJ, Allgauer M et al (2016) Local tumor control probability modeling of primary and secondary lung tumors in stereotactic body radiotherapy. Radiother Oncol 118:485–491CrossRefPubMedGoogle Scholar
  28. 28.
    Grossmann P, Stringfield O, El-Hachem N et al (2017) Defining the biological basis of radiomic phenotypes in lung cancer. Elife 6:e23421. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Temming S, Kocher M, Stoelben E et al (2018) Risk-adapted robotic stereotactic body radiation therapy for inoperable early-stage non-small-cell lung cancer. Strahlenther Onkol 194:91–97CrossRefPubMedGoogle Scholar
  30. 30.
    Shaw E, Kline R, Gillin M et al (1993) Radiation therapy oncology group: Radiosurgery quality assurance guidelines. Int J Radiat Oncol Biol Phys 27:1231–1239CrossRefPubMedGoogle Scholar
  31. 31.
    Paddick I (2000) A simple scoring ratio to index the conformity of radiosurgical treatment plans. Technical note. J Neurosurg 93(Suppl 3):219–222CrossRefPubMedGoogle Scholar
  32. 32.
    Baumann R, Chan MKH, Pyschny F et al (2018) Clinical results of mean GTV dose optimized Robotic-guided Stereotactic body radiation therapy for lung tumors. Front Oncol 8:171CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Stera S, Balermpas P, Chan MKH et al (2018) Breathing-motion-compensated robotic guided stereotactic body radiation therapy : Patterns of failure analysis. Strahlenther Onkol 194:143–155CrossRefPubMedGoogle Scholar
  34. 34.
    Kimura T, Matsuura K, Murakami Y et al (2006) CT appearance of radiation injury of the lung and clinical symptoms after stereotactic body radiation therapy (SBRT) for lung cancers: Are patients with pulmonary emphysema also candidates for SBRT for lung cancers? Int J Radiat Oncol Biol Phys 66:483–491CrossRefPubMedGoogle Scholar
  35. 35.
    Palma DA, Senan S, Haasbeek CJ, Verbakel WF, Vincent A, Lagerwaard F (2011) Radiological and clinical pneumonitis after stereotactic lung radiotherapy: A matched analysis of three-dimensional conformal and volumetric-modulated arc therapy techniques. Int J Radiat Oncol Biol Phys 80:506–513CrossRefPubMedGoogle Scholar
  36. 36.
    Kalman NS, Hugo GD, Kahn JM et al (2018) Interobserver reliability in describing radiographic lung changes after stereotactic body radiation therapy. Adv Radiat Oncol 3(4):655–661. CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Zwanenburg A, Leger S, Vallières M, Loeck S (2017) Image biomarker standardisation initiative. Google Scholar
  38. 38.
    Molina D, Perez-Beteta J, Martinez-Gonzalez A et al (2016) Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images. Comput Biol Med 78:49–57CrossRefPubMedGoogle Scholar
  39. 39.
    He L, Huang Y, Ma Z, Liang C, Liang C, Liu Z (2016) Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep 6(1). Google Scholar
  40. 40.
    Mattonen SA, Palma DA, Johnson C et al (2016) Detection of local cancer recurrence after Stereotactic ablative radiation therapy for lung cancer: Physician performance versus Radiomic assessment. Int J Radiat Oncol Biol Phys 94:1121–1128CrossRefPubMedGoogle Scholar
  41. 41.
    Mattonen SA, Tetar S, Palma DA, Louie AV, Senan S, Ward AD (2015) Imaging texture analysis for automated prediction of lung cancer recurrence after stereotactic radiotherapy. J Med Imaging (bellingham) 2(4). Google Scholar
  42. 42.
    Moran A, Daly ME, Yip SSF, Yamamoto T (2017) Radiomics-based assessment of radiation-induced lung injury after Stereotactic body radiotherapy. Clin Lung Cancer 18:e425–e431CrossRefPubMedGoogle Scholar
  43. 43.
    Huynh E, Coroller TP, Narayan V et al (2016) CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiother Oncol 120:258–266CrossRefPubMedGoogle Scholar
  44. 44.
    Huynh E, Coroller TP, Narayan V et al (2017) Associations of Radiomic data extracted from static and respiratory-gated CT scans with disease recurrence in lung cancer patients treated with SBRT. PLoS ONE 12(1). Google Scholar
  45. 45.
    Jager KJ, van Dijk PC, Zoccali C, Dekker FW (2008) The analysis of survival data: The Kaplan-Meier method. Kidney Int 74:560–565CrossRefPubMedGoogle Scholar
  46. 46.
    Simon N, Friedman J, Hastie T, Tibshirani R (2011) Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw 39:1–13CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16:385–395CrossRefPubMedGoogle Scholar
  48. 48.
    Brentnall AR, Cuzick J (2016) Use of the concordance index for predictors of censored survival data. Stat Methods Med Res. PubMedPubMedCentralGoogle Scholar
  49. 49.
    Deasy JO, Bentzen SM, Jackson A et al (2010) Improving normal tissue complication probability models: The need to adopt a “data-pooling” culture. Int J Radiat Oncol Biol Phys 76(3):S151–S154. CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Baker M (2016) 1,500 scientists lift the lid on reproducibility. Nature 533:452–454CrossRefPubMedGoogle Scholar
  51. 51.
    Choi W, Oh JH, Riyahi S et al (2018) Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys. Google Scholar
  52. 52.
    Constanzo J, Wei L, Tseng HH, El Naqa I (2017) Radiomics in precision medicine for lung cancer. Transl Lung Cancer Res 6:635–647CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Fave X, Mackin D, Yang J et al (2015) Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med Phys 42:6784–6797CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Fave X, Zhang L, Yang J et al (2017) Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep 7:588CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Kalpathy-Cramer J, Mamomov A, Zhao B et al (2016) Radiomics of lung nodules: A multi-institutional study of robustness and agreement of quantitative imaging features. Tomography 2:430–437CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Larue RTHM, Van De Voorde L, van Timmeren JE et al (2017) 4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers. Radiother Oncol 125:147–153CrossRefPubMedGoogle Scholar
  57. 57.
    Li Q, Kim J, Balagurunathan Y et al (2017) Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy. Med Phys 44:4341–4349CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Li Q, Kim J, Balagurunathan Y et al (2017) CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy. Radiat Oncol 12:158CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Takeda K, Takanami K, Shirata Y et al (2017) Clinical utility of texture analysis of 18F-FDG PET/CT in patients with Stage I lung cancer treated with stereotactic body radiotherapy. J Radiat Res 58(6):862–869. CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    van Timmeren JE, Leijenaar RTH, van Elmpt W, Reymen B, Lambin P (2017) Feature selection methodology for longitudinal cone-beam CT radiomics. Acta Oncol 56(11):1537–1543. CrossRefPubMedGoogle Scholar
  61. 61.
    van Timmeren JE, Leijenaar RTH, van Elmpt W et al (2017) Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiother Oncol 123:363–369CrossRefPubMedGoogle Scholar
  62. 62.
    Yu W, Tang C, Hobbs BP et al (2017) 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. Google Scholar
  63. 63.
    Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F (2017) Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep 7(1). Google Scholar
  64. 64.
    Zhu X, Dong D, Chen Z et al (2018) Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol. PubMedCentralGoogle Scholar
  65. 65.
    Wu W, Parmar C, Grossmann P et al (2016) Exploratory study to identify Radiomics classifiers for lung cancer histology. Front Oncol 6:71PubMedPubMedCentralGoogle Scholar
  66. 66.
    Tsoutsou PG, Koukourakis MI (2006) Radiation pneumonitis and fibrosis: Mechanisms underlying its pathogenesis and implications for future research. Int J Radiat Oncol Biol Phys 66:1281–1293CrossRefPubMedGoogle Scholar
  67. 67.
    Wang S, Campbell J, Stenmark MH et al (2017) Plasma levels of IL-8 and TGF-beta1 predict radiation-induced lung toxicity in non-small cell lung cancer: A validation study. Int J Radiat Oncol Biol Phys 98:615–621CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Guckenberger M, Klement RJ, Kestin LL et al (2013) Lack of a dose-effect relationship for pulmonary function changes after stereotactic body radiation therapy for early-stage non-small cell lung cancer. Int J Radiat Oncol Biol Phys 85:1074–1081CrossRefPubMedGoogle Scholar
  69. 69.
    Balagurunathan Y, Gu Y, Wang H et al (2014) Reproducibility and prognosis of quantitative features extracted from CT images. Transl Oncol 7:72–87CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and Reproducibility of Radiomic features: A systematic review. Int J Radiat Oncol Biol Phys 102:1143–1158CrossRefPubMedGoogle Scholar
  71. 71.
    Zhao B, Tan Y, Tsai WY et al (2016) Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 6(1). Google Scholar
  72. 72.
    de Oliveira MS, Balthazar ML, D’Abreu A et al (2011) MR imaging texture analysis of the corpus callosum and thalamus in amnestic mild cognitive impairment and mild Alzheimer disease. Ajnr Am J Neuroradiol 32:60–66CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Khaled Bousabarah
    • 1
  • Susanne Temming
    • 2
  • Mauritius Hoevels
    • 1
  • Jan Borggrefe
    • 3
  • Wolfgang W. Baus
    • 2
  • Daniel Ruess
    • 1
  • Veerle Visser-Vandewalle
    • 1
  • Maximilian Ruge
    • 1
  • Martin Kocher
    • 1
    • 2
    Email author
  • Harald Treuer
    • 1
  1. 1.Department of Stereotactic and Functional NeurosurgeryUniversity Hospital of CologneCologneGermany
  2. 2.Department of Radiation OncologyUniversity Hospital of CologneCologneGermany
  3. 3.Institute of Diagnostic and Interventional RadiologyUniversity Hospital of CologneCologneGermany

Personalised recommendations