Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC
Abstract Unresectable stage III NSCLC is now treated with chemoradiation (CRT) followed by immune checkpoint inhibitors (ICI). Pneumonitis, a common CRT complication, has heightened risk with ICI, potentially causing severe outcomes. Currently, there are no biomarkers to predict pneumonitis risk or...
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| Format: | Article |
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Nature Portfolio
2024-12-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-024-00790-9 |
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| author | Lukas Delasos Mohammadhadi Khorrami Vidya S. Viswanathan Khalid Jazieh Yifu Ding Pushkar Mutha Kevin Stephans Amit Gupta Nathan A. Pennell Pradnya D. Patil Kristin Higgins Anant Madabhushi |
| author_facet | Lukas Delasos Mohammadhadi Khorrami Vidya S. Viswanathan Khalid Jazieh Yifu Ding Pushkar Mutha Kevin Stephans Amit Gupta Nathan A. Pennell Pradnya D. Patil Kristin Higgins Anant Madabhushi |
| author_sort | Lukas Delasos |
| collection | DOAJ |
| description | Abstract Unresectable stage III NSCLC is now treated with chemoradiation (CRT) followed by immune checkpoint inhibitors (ICI). Pneumonitis, a common CRT complication, has heightened risk with ICI, potentially causing severe outcomes. Currently, there are no biomarkers to predict pneumonitis risk or differentiate between radiation-induced pneumonitis (RTP) and ICI-induced pneumonitis (IIP). This study analyzed 293 patients from two institutions, with 140 experiencing pneumonitis (RTP: 84, IIP: 56). Two models were developed: M1 predicted pneumonitis risk using seven radiomic features, achieving high accuracy across internal and external datasets (AUCs: 0.76 and 0.85). M2 differentiated RTP from IIP, with strong performance (AUCs: 0.86 and 0.81). Gene set enrichment analysis linked high pneumonitis risk to pathways such as ECM-receptor interaction and T-cell signaling, while high IIP risk correlated with MAPK and JAK–STAT pathways. Radiomic models show promise in early pneumonitis risk stratification and distinguishing pneumonitis types, potentially guiding personalized NSCLC treatment. |
| format | Article |
| id | doaj-art-83beddb166fe45b38993f22d8aa8df18 |
| institution | DOAJ |
| issn | 2397-768X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-83beddb166fe45b38993f22d8aa8df182025-08-20T02:43:32ZengNature Portfolionpj Precision Oncology2397-768X2024-12-018111210.1038/s41698-024-00790-9Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLCLukas Delasos0Mohammadhadi Khorrami1Vidya S. Viswanathan2Khalid Jazieh3Yifu Ding4Pushkar Mutha5Kevin Stephans6Amit Gupta7Nathan A. Pennell8Pradnya D. Patil9Kristin Higgins10Anant Madabhushi11Cleveland Clinic Taussig Cancer CenterEmory University and Georgia Institute of TechnologyEmory University and Georgia Institute of TechnologyCleveland Clinic Taussig Cancer CenterDepartment of Radiation Oncology, Winship Cancer Institute and Emory UniversityEmory University and Georgia Institute of TechnologyCleveland Clinic Taussig Cancer CenterDepartment of Radiology, University Hospital Cleveland Medical CenterCleveland Clinic Taussig Cancer CenterDepartment of hematology and medical oncology, Nuvance HealthDepartment of Radiation Oncology, Winship Cancer Institute and Emory UniversityEmory University and Georgia Institute of TechnologyAbstract Unresectable stage III NSCLC is now treated with chemoradiation (CRT) followed by immune checkpoint inhibitors (ICI). Pneumonitis, a common CRT complication, has heightened risk with ICI, potentially causing severe outcomes. Currently, there are no biomarkers to predict pneumonitis risk or differentiate between radiation-induced pneumonitis (RTP) and ICI-induced pneumonitis (IIP). This study analyzed 293 patients from two institutions, with 140 experiencing pneumonitis (RTP: 84, IIP: 56). Two models were developed: M1 predicted pneumonitis risk using seven radiomic features, achieving high accuracy across internal and external datasets (AUCs: 0.76 and 0.85). M2 differentiated RTP from IIP, with strong performance (AUCs: 0.86 and 0.81). Gene set enrichment analysis linked high pneumonitis risk to pathways such as ECM-receptor interaction and T-cell signaling, while high IIP risk correlated with MAPK and JAK–STAT pathways. Radiomic models show promise in early pneumonitis risk stratification and distinguishing pneumonitis types, potentially guiding personalized NSCLC treatment.https://doi.org/10.1038/s41698-024-00790-9 |
| spellingShingle | Lukas Delasos Mohammadhadi Khorrami Vidya S. Viswanathan Khalid Jazieh Yifu Ding Pushkar Mutha Kevin Stephans Amit Gupta Nathan A. Pennell Pradnya D. Patil Kristin Higgins Anant Madabhushi Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC npj Precision Oncology |
| title | Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC |
| title_full | Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC |
| title_fullStr | Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC |
| title_full_unstemmed | Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC |
| title_short | Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC |
| title_sort | novel radiogenomics approach to predict and characterize pneumonitis in stage iii nsclc |
| url | https://doi.org/10.1038/s41698-024-00790-9 |
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