Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer
Objective Checkpoint inhibitor pneumonitis (CIP) is a potentially life-threatening immune-related adverse event. Efficient strategies to select patients at risk are still required. The aim of our study was to assess the utility of a machine learning model, integrating pre-treatment CT lung radiomics...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-05-01
|
| Series: | Technology in Cancer Research & Treatment |
| Online Access: | https://doi.org/10.1177/15330338251344004 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850152140599721984 |
|---|---|
| author | François Cousin MD, PhD Thomas Louis MS, PhD Pierre Frères MD, PhD Julien Guiot MD, PhD Mariaelena Occhipinti MD Fabio Bottari MS Wim Vos MS Roland Hustinx MD, PhD |
| author_facet | François Cousin MD, PhD Thomas Louis MS, PhD Pierre Frères MD, PhD Julien Guiot MD, PhD Mariaelena Occhipinti MD Fabio Bottari MS Wim Vos MS Roland Hustinx MD, PhD |
| author_sort | François Cousin MD, PhD |
| collection | DOAJ |
| description | Objective Checkpoint inhibitor pneumonitis (CIP) is a potentially life-threatening immune-related adverse event. Efficient strategies to select patients at risk are still required. The aim of our study was to assess the utility of a machine learning model, integrating pre-treatment CT lung radiomics features with clinical data, to predict patients at risk of developing CIP. Methods In this retrospective study, 116 patients with varied malignancies treated with immune checkpoint inhibitors (ICIs) were included. In this cohort, 35 patients presented with CIP and 81 patients did not. Each lung and its lobes were segmented on pre-treatment CT scans to perform a handcrafted radiomic analysis. Radiomic features were associated with clinical parameters to build generalized linear (GLM) and random forest (RF) models, to predict occurrence of CIP. The models were fine-tuned, validated and tested using a nested 5-fold cross-validation method. Results The RF models combining radiomic and clinical features showed the best performances with an area under the ROC curve (AUC) of 0.75 (95%CI:0.62-0.88) on the test set. The most accurate clinical model was a RF model and achieved an AUC of 0.72 (95%CI:0.51-0.92). The best radiomic model was a GLM model and achieved an AUC of 0.71 (95%CI:0.58-0.84). Conclusions Our CT-based lung radiomic models showed moderate to good performance at predicting CIP. We demonstrated the potential role of machine learning models associating clinical parameters and lung CT radiomic features to better identify patients treated with ICIs at risk of developing CIP. Advances in knowledge: Radiomics analysis of the lung parenchyma could be used as a non-invasive tool to select patients at risk of developing immune-checkpoint pneumonitis. |
| format | Article |
| id | doaj-art-7976a38d4f964545848c7ed3ce22d039 |
| institution | OA Journals |
| issn | 1533-0338 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Technology in Cancer Research & Treatment |
| spelling | doaj-art-7976a38d4f964545848c7ed3ce22d0392025-08-20T02:26:03ZengSAGE PublishingTechnology in Cancer Research & Treatment1533-03382025-05-012410.1177/15330338251344004Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced CancerFrançois Cousin MD, PhD0Thomas Louis MS, PhD1Pierre Frères MD, PhD2Julien Guiot MD, PhD3Mariaelena Occhipinti MD4Fabio Bottari MS5Wim Vos MS6Roland Hustinx MD, PhD7 Department of Nuclear Medicine and Oncological Imaging, University Hospital (CHU) of Liège, Liège, Belgium Radiomics (Oncoradiomics SA), Liège, Belgium Department of Medical Oncology, University Hospital (CHU) of Liège, Liège, Belgium Department of Respiratory Medicine, University Hospital (CHU) of Liège, Liège, Belgium Radiomics (Oncoradiomics SA), Liège, Belgium Radiomics (Oncoradiomics SA), Liège, Belgium Radiomics (Oncoradiomics SA), Liège, Belgium GIGA-CRC in vivo imaging, University of Liège, Liège, BelgiumObjective Checkpoint inhibitor pneumonitis (CIP) is a potentially life-threatening immune-related adverse event. Efficient strategies to select patients at risk are still required. The aim of our study was to assess the utility of a machine learning model, integrating pre-treatment CT lung radiomics features with clinical data, to predict patients at risk of developing CIP. Methods In this retrospective study, 116 patients with varied malignancies treated with immune checkpoint inhibitors (ICIs) were included. In this cohort, 35 patients presented with CIP and 81 patients did not. Each lung and its lobes were segmented on pre-treatment CT scans to perform a handcrafted radiomic analysis. Radiomic features were associated with clinical parameters to build generalized linear (GLM) and random forest (RF) models, to predict occurrence of CIP. The models were fine-tuned, validated and tested using a nested 5-fold cross-validation method. Results The RF models combining radiomic and clinical features showed the best performances with an area under the ROC curve (AUC) of 0.75 (95%CI:0.62-0.88) on the test set. The most accurate clinical model was a RF model and achieved an AUC of 0.72 (95%CI:0.51-0.92). The best radiomic model was a GLM model and achieved an AUC of 0.71 (95%CI:0.58-0.84). Conclusions Our CT-based lung radiomic models showed moderate to good performance at predicting CIP. We demonstrated the potential role of machine learning models associating clinical parameters and lung CT radiomic features to better identify patients treated with ICIs at risk of developing CIP. Advances in knowledge: Radiomics analysis of the lung parenchyma could be used as a non-invasive tool to select patients at risk of developing immune-checkpoint pneumonitis.https://doi.org/10.1177/15330338251344004 |
| spellingShingle | François Cousin MD, PhD Thomas Louis MS, PhD Pierre Frères MD, PhD Julien Guiot MD, PhD Mariaelena Occhipinti MD Fabio Bottari MS Wim Vos MS Roland Hustinx MD, PhD Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer Technology in Cancer Research & Treatment |
| title | Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer |
| title_full | Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer |
| title_fullStr | Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer |
| title_full_unstemmed | Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer |
| title_short | Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer |
| title_sort | machine learning model integrating ct radiomics of the lung to predict checkpoint inhibitor pneumonitis in patients with advanced cancer |
| url | https://doi.org/10.1177/15330338251344004 |
| work_keys_str_mv | AT francoiscousinmdphd machinelearningmodelintegratingctradiomicsofthelungtopredictcheckpointinhibitorpneumonitisinpatientswithadvancedcancer AT thomaslouismsphd machinelearningmodelintegratingctradiomicsofthelungtopredictcheckpointinhibitorpneumonitisinpatientswithadvancedcancer AT pierrefreresmdphd machinelearningmodelintegratingctradiomicsofthelungtopredictcheckpointinhibitorpneumonitisinpatientswithadvancedcancer AT julienguiotmdphd machinelearningmodelintegratingctradiomicsofthelungtopredictcheckpointinhibitorpneumonitisinpatientswithadvancedcancer AT mariaelenaocchipintimd machinelearningmodelintegratingctradiomicsofthelungtopredictcheckpointinhibitorpneumonitisinpatientswithadvancedcancer AT fabiobottarims machinelearningmodelintegratingctradiomicsofthelungtopredictcheckpointinhibitorpneumonitisinpatientswithadvancedcancer AT wimvosms machinelearningmodelintegratingctradiomicsofthelungtopredictcheckpointinhibitorpneumonitisinpatientswithadvancedcancer AT rolandhustinxmdphd machinelearningmodelintegratingctradiomicsofthelungtopredictcheckpointinhibitorpneumonitisinpatientswithadvancedcancer |