CT-derived functional imaging biomarkers combined with FEV1 for predicting 10-year all-cause mortality in COPDGene cohort.

NPJ biomedical innovations Pub Date : 2025-01-01 Epub Date: 2025-07-04 DOI:10.1038/s44385-025-00027-9
Girish Nair, Yuying Judy Xing, Aaron Luong, Faiza Bashar, Amanda Nowacki, Craig Stevens, Lili Zhao, Edward Castillo
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Abstract

This study evaluates the predictive power of CT-derived functional imaging (CTFI) combined with forced expiratory volume in 1 second (FEV1) for 10-year all-cause mortality in COPD patients. We analyzed 8583 participants from the COPDGene® cohort, focusing on 3550 participants with spirometric obstruction. CTFI metrics, including ventilation (CT-V) and perfusion (PBM), were computed from non-contrast CT scans at lobar resolution. Our findings show that regional and global CTFI scores decline with advancing GOLD stages. A Random Survival Forest model, adjusted for age, BMI, and scanner type, demonstrated significant improvement in mortality prediction when combining FEV1 with CTFI, compared to FEV1 alone, with an AUC increase from 0.71 to 0.76 over 10 years. The Net Reclassification Index further confirmed the added predictive value of CTFI. These results suggest that integrating CTFI with traditional lung function measures enhances mortality prediction in COPD, offering a promising tool for clinical risk assessment.

ct衍生的功能成像生物标志物结合FEV1预测COPDGene队列的10年全因死亡率
本研究评估了ct衍生功能成像(CTFI)结合1秒用力呼气量(FEV1)对COPD患者10年全因死亡率的预测能力。我们分析了来自COPDGene®队列的8583名参与者,重点分析了3550名肺活量测量性梗阻患者。CTFI指标,包括通气(CT- v)和灌注(PBM),是通过局部分辨率的非对比CT扫描计算的。我们的研究结果表明,区域和全球CTFI得分随着GOLD阶段的推进而下降。根据年龄、BMI和扫描仪类型调整的随机生存森林模型显示,与单独使用FEV1相比,结合FEV1和CTFI对死亡率的预测有显著改善,10年内AUC从0.71增加到0.76。净重分类指数进一步证实了CTFI的附加预测价值。这些结果表明,将CTFI与传统肺功能测量相结合可以提高COPD的死亡率预测,为临床风险评估提供了一种有前景的工具。
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