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

Tohidinezhad, F.; Bontempi, D.; Zhang, Z.; Dingemans, A.M.; Aerts, J.; Bootsma, G.; ... ; Ruysscher, D. de (2023)

Article / Letter to editor

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 (http://creativecommons.org/licenses/by/4.0/).

• Immunotherapy
• Pneumonitis
• Image analysis
• Radiomics
• Prediction

• All authors: Tohidinezhad, F.; Bontempi, D.; Zhang, Z.; Dingemans, A.M.; Aerts, J.; Bootsma, G.; Vansteenkiste, J.; Hashemi, S.; Smit, E.; Gietema, H.; Aerts, H.J.W.L.; Dekker, A.; Hendriks, L.E.L.; Traverso, A.; Ruysscher, D. de
• Date: 2023-02-27
• Journal: European Journal of Cancer
• Volume: 183
• Pages: 142 - 151
• DOI: doi:10.1016/j.ejca.2023.01.027
• Link: https://www.ejcancer.com/article/S0959-8049(23)00061-8/fulltext