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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

Abstract

Objectives

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.

Methods

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.

Results

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).

Conclusion

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.

Keywords

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

Zusammenfassung

Ziel

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.

Methoden

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.

Ergebnisse

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).

Schlussfolgerung

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.

Schlüsselwörter

Bildverarbeitung Strahlenbiologie Maschinenlernen Toxizität Biomarker 

Notes

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

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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

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