Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis.
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ICR Authors
Authors
Hindocha, S
Hunter, B
Linton-Reid, K
George Charlton, T
Chen, M
Logan, A
Ahmed, M
Locke, I
Sharma, B
Doran, S
Orton, M
Bunce, C
Power, D
Ahmad, S
Chan, K
Ng, P
Toshner, R
Yasar, B
Conibear, J
Murphy, R
Newsom-Davis, T
Goodley, P
Evison, M
Yousaf, N
Bitar, G
McDonald, F
Blackledge, M
Aboagye, E
Lee, R
Hunter, B
Linton-Reid, K
George Charlton, T
Chen, M
Logan, A
Ahmed, M
Locke, I
Sharma, B
Doran, S
Orton, M
Bunce, C
Power, D
Ahmad, S
Chan, K
Ng, P
Toshner, R
Yasar, B
Conibear, J
Murphy, R
Newsom-Davis, T
Goodley, P
Evison, M
Yousaf, N
Bitar, G
McDonald, F
Blackledge, M
Aboagye, E
Lee, R
Document Type
Journal Article
Date
2024-06-01
Date Accepted
2024-03-31
Abstract
BACKGROUND: Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns. METHODS: In this multi-centre study of 455 patients, we used machine learning with radiomic features extracted from chest CT imaging to develop and validate five models to distinguish CIP and RP from COVID-19, non-COVID-19 infective pneumonitis, and each other. Model performance was compared to that of two radiologists. RESULTS: Models to distinguish RP from COVID-19, CIP from COVID-19 and CIP from non-COVID-19 IP out-performed radiologists (test set AUCs of 0.92 vs 0.8 and 0.8; 0.68 vs 0.43 and 0.4; 0.71 vs 0.55 and 0.63 respectively). Models to distinguish RP from non-COVID-19 IP and CIP from RP were not superior to radiologists but demonstrated modest performance, with test set AUCs of 0.81 and 0.8 respectively. The CIP vs RP model performed less well on patients with prior exposure to both ICI and radiotherapy (AUC 0.54), though the radiologists also had difficulty distinguishing this test cohort (AUC values 0.6 and 0.6). CONCLUSION: Our results demonstrate the potential utility of such tools as a second or concurrent reader to support oncologists, radiologists, and chest physicians in cases of diagnostic uncertainty. Further research is required for patients with exposure to both ICI and thoracic radiotherapy.
Citation
Radiotherapy and Oncology, 2024, 195 pp. 110266 -
Source Title
Radiotherapy and Oncology
Publisher
ELSEVIER IRELAND LTD
ISSN
0167-8140
eISSN
1879-0887
1879-0887
1879-0887
Collections
Research Team
Magnetic Resonance