Delasos Lukas, Khorrami Mohammadhadi, Viswanathan Vidya S, Jazieh Khalid, Ding Yifu, Mutha Pushkar, Stephans Kevin, Gupta Amit, Pennell Nathan A, Patil Pradnya D, Higgins Kristin, Madabhushi Anant
Cleveland Clinic Taussig Cancer Center, Cleveland, USA.
Emory University and Georgia Institute of Technology, Atlanta, USA.
NPJ Precis Oncol. 2024 Dec 24;8(1):290. doi: 10.1038/s41698-024-00790-9.
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.
不可切除的 III 期非小细胞肺癌（NSCLC）目前采用放化疗（CRT）联合免疫检查点抑制剂（ICI）进行治疗。肺炎是 CRT 的一种常见并发症，ICI 会增加其风险，可能导致严重后果。目前，尚无生物标志物可预测肺炎风险或区分放射性肺炎（RTP）和免疫检查点抑制剂诱导的肺炎（IIP）。本研究分析了来自两家机构的 293 例患者，其中 140 例发生了肺炎（RTP：84 例，IIP：56 例）。开发了两个模型：M1 使用七个放射组学特征预测肺炎风险，在内部和外部数据集上均具有较高的准确性（AUC：0.76 和 0.85）。M2 区分 RTP 和 IIP，性能良好（AUC：0.86 和 0.81）。基因集富集分析将高肺炎风险与细胞外基质-受体相互作用和 T 细胞信号传导等通路联系起来，而高 IIP 风险与 MAPK 和 JAK-STAT 通路相关。放射组学模型在早期肺炎风险分层和区分肺炎类型方面显示出前景，可能指导 NSCLC 的个性化治疗。
