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
{"title":"Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC","authors":"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","doi":"10.1038/s41698-024-00790-9","DOIUrl":null,"url":null,"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.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":" ","pages":"1-12"},"PeriodicalIF":6.8000,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00790-9.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Precision Oncology","FirstCategoryId":"3","ListUrlMain":"https://www.nature.com/articles/s41698-024-00790-9","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Online-only and open access, npj Precision Oncology is an international, peer-reviewed journal dedicated to showcasing cutting-edge scientific research in all facets of precision oncology, spanning from fundamental science to translational applications and clinical medicine.