Zhang Yao-Ren, Lu Yueh-Hsun, Lin Che-Ming, Ku Jan-Wen
Department of Radiology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan, ROC.
Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, New Taipei City, Taiwan, ROC.
Thorac Cancer. 2025 Aug;16(15):e70144. doi: 10.1111/1759-7714.70144.
While established biomarkers predict immunotherapy response in advanced nonsmall cell lung cancer (NSCLC), additional noninvasive imaging biomarkers may enhance treatment selection. Pretreatment computed tomography (CT) texture analysis may provide tumor characterization to predict survival outcomes.
We conducted a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Cochrane Library databases were searched. Study quality was assessed using the quality in prognosis studies (QUIPS) tool. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using random-effects models.
Ten retrospective studies involving 2400 patients were included. Patients stratified as low-risk based on CT texture features demonstrated significantly improved survival outcomes compared to high-risk patients. The included studies used diverse radiomic features for risk stratification, including texture features from gray-level co-occurrence matrix (GLCM) such as entropy and dissimilarity, first-order statistical parameters including skewness and kurtosis, gray-level run-length matrix (GLRLM) features, and deep learning-derived features. Meta-analysis of five studies (n = 1102) revealed that patients stratified as low-risk based on these quantitative CT texture signatures had substantially better overall survival (OS) (p < 0.0001) with minimal heterogeneity (I = 0.0%). Similarly, progression-free survival (PFS) analysis of five studies (n = 1799) showed significant benefit for low-risk patients (p < 0.0001), though with moderate heterogeneity (I = 71.7%).
Pretreatment quantitative CT texture analysis effectively predicts survival outcomes in advanced NSCLC patients receiving immunotherapy, providing clinically meaningful risk stratification. This noninvasive imaging approach may serve as an additional tool to complement established pathological and molecular biomarkers, including liquid biopsy, for enhanced personalized treatment selection.
虽然已有的生物标志物可预测晚期非小细胞肺癌（NSCLC）的免疫治疗反应，但其他非侵入性成像生物标志物可能会改善治疗选择。治疗前计算机断层扫描（CT）纹理分析可提供肿瘤特征以预测生存结果。
我们按照系统评价和Meta分析的首选报告项目（PRISMA）指南进行了系统评价和Meta分析。检索了PubMed和Cochrane图书馆数据库。使用预后研究质量（QUIPS）工具评估研究质量。采用随机效应模型汇总95%置信区间（CI）的风险比（HR）。
纳入了10项涉及2400例患者的回顾性研究。与高风险患者相比，根据CT纹理特征分层为低风险的患者生存结果显著改善。纳入的研究使用了多种用于风险分层的放射组学特征，包括来自灰度共生矩阵（GLCM）的纹理特征，如熵和差异度，一阶统计参数，如偏度和峰度，灰度游程长度矩阵（GLRLM）特征，以及深度学习衍生特征。对5项研究（n = 1102）的Meta分析显示，根据这些定量CT纹理特征分层为低风险的患者总生存期（OS）显著更好（p < 0.0001），异质性最小（I = 0.0%）。同样，对5项研究（n = 1799）的无进展生存期（PFS）分析显示低风险患者有显著获益（p < 0.0001），尽管异质性中等（I = 71.7%）。
治疗前定量CT纹理分析可有效预测接受免疫治疗的晚期NSCLC患者的生存结果，提供具有临床意义的风险分层。这种非侵入性成像方法可作为一种补充已有的病理和分子生物标志物（包括液体活检）的额外工具，以加强个性化治疗选择。
