Computed tomography-based radiomics for the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer patients treated with immune checkpoint inhibitors

F. Tohidinezhad, D. Bontempi, Z. Zhang, A.M. Dingemans, J. Aerts, G. Bootsma, J. Vansteenkiste, S. Hashemi, E. Smit, H. Gietema, H.J.W.L. Aerts, A. Dekker, L.E.L. Hendriks, A. Traverso, D. De Ruysscher*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Introduction: Immunotherapy-induced pneumonitis (IIP) is a serious side-effect which requires accurate diagnosis and management with high-dose corticosteroids. The differ-ential diagnosis between IIP and other types of pneumonitis (OTP) remains challenging due to similar radiological patterns. This study was aimed to develop a prediction model to differentiate IIP from OTP in patients with stage IV non-small cell lung cancer (NSCLC) who developed pneumonitis during immunotherapy. Methods: Consecutive patients with metastatic NSCLC treated with immunotherapy in six centres in the Netherlands and Belgium from 2017 to 2020 were reviewed and cause-specific pneumonitis events were identified. Seven regions of interest (segmented lungs and sphe-roidal/cubical regions surrounding the inflammation) were examined to extract the most pre-dictive radiomic features from the chest computed tomography images obtained at pneumonitis manifestation. Models were internally tested regarding discrimination, calibra-tion and decisional benefit. To evaluate the clinical application of the models, predicted labels were compared with the separate clinical and radiological judgements. Results: A total of 556 patients were reviewed; 31 patients (5.6%) developed IIP and 41 pa-tients developed OTP (7.4%). The line of immunotherapy was the only predictive factor in the clinical model (2nd versus 1st odds ratio Z 0.08, 95% confidence interval:0.01-0.77). The best radiomic model was achieved using a 75-mm spheroidal region of interest which showed an optimism-corrected area under the receiver operating characteristic curve of 0.83 (95% confidence interval:0.77-0.95) with negative and positive predictive values of 80% and 79%, respectively. Good calibration and net benefits were achieved for the radiomic model across the entire range of probabilities. A correct diagnosis was provided by the radiomic model in 10 out of 12 cases with non-conclusive radiological judgements. Conclusion: Radiomic biomarkers applied to computed tomography imaging may support cli-nicians making the differential diagnosis of pneumonitis in patients with NSCLC receiving immunotherapy, especially when the radiologic assessment is non-conclusive. 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (
Original languageEnglish
Pages (from-to)142-151
Number of pages10
JournalEuropean Journal of Cancer
Publication statusPublished - 1 Apr 2023


  • Immunotherapy
  • Pneumonitis
  • Image analysis
  • Radiomics
  • Prediction
  • CT

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