Year: 2024
Type: article
Study design: Observational Study
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.
Source: npj Precision Oncology
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,790
Funders: U.S. Department of Defense, Foundation for the National Institutes of Health, U.S. Department of Health and Human Services, U.S. Department of Veterans Affairs, Eli Lilly and Company, AstraZeneca, California Breast Cancer Research Program, National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, DOD Prostate Cancer Research Program, DOD Peer Reviewed Cancer Research Program
Awards: G100871905, RadxTools for assessing tumor treatment response on imaging, SYSTEMS BIOLOGY APPROACH TO PREDICTING AND ASSESSING RESPONSE TO CHEMORADIATION FOR BRAIN TUMORS, G1216189450, G1266336578, G1313646905, G1478154455, G1505200503, G1596146291, G1817069843, G1828163000, A Study to Validate and Improve an Automated Image Analysis Algorithm to Detect Tuberculosis in Sputum Smear Slides, G2228475719, G2291306530, BIOMEDICAL ENGINEERING RESEARCH FACILITIES, G2689512122, G278330899, G278880248, G2800195642, MR Fingerprinting and Computerized Decision Support for Prostate Cancer, G2917785245, G2931214990, G3068466812, G3075835325, G3137332008, G338730684, G3564444776, G3716872827, G406353596, G4119534169, Lung cancer in East Africa and the relationship to HIV-1 infection: epidemiology, molecular characterization and imaging, G5026872601, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, G5429562348, FUSED BIOMARKER-BASED PREDICTION OF AGGRESSIVE FROM INDOLENT PROSTATE CANCER, Computerized histologic image predictor of cancer outcome, G5758079931, Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal Carcinoma, G5850682332, NOVEL CT-DERIVED RADIOMIC BIOMARKERS FOR LUNG NODULE CHARACTERIZATION IN THE SCREENING POPULATION, G6123616905, G620757052, G7124118669, Computer-Assisted Histologic Evaluation of Cardiac Allograft Rejection, G7189336044, QUANTITATIVE VESSEL TORTUOSITY AS BIOMARKER FOR PREDICTING AND MONITORING RESPONSE TO IMMUNOTHERAPY FOR NON-SMALL CELL LUNG CANCER FROM ROUTINE CT SCANS, G7349345630, G761790451, G7935030457, G8166743225, G854738250, G86714149, G8715450552, COMPUTATIONAL HISTOMORPHOMETRIC PREDICTOR OF PATHOLOGIC RESPONSE AND DISEASE RECURRENCE FOR TRIPLE-NEGATIVE BREAST CANCER, G9000000780, G9000021175, G9000023716, G9000064711, G9000153031, G9000175579, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, G942918344
OpenAlex ID: W4405734244