本研究旨在确定 CT 放射组学特征 (RF) 和全身炎症指数的纵向变化在预测接受免疫检查点抑制剂 (ICIs) 治疗的晚期非小细胞肺癌 (NSCLC) 的生存方面是否优于单时间点评估。从单中心 IV 期 NSCLC 患者队列中回顾性获取治疗前 (T0) 和首次疾病评估 (T1) RF 和全身炎症指数，并将其 delta (Δ) 变化计算为 [(T1-T0)/T0]。选择来自原发肿瘤的 RF，使用 LASSO Cox 回归模型检测到的标准化预测因子的线性组合来构建基线放射组学 (RAD) 和 Δ-RAD 评分。 Cox 模型是单独使用临床特征或结合基线和 Δ 血液参数生成的，并与基线-RAD 和 Δ-RAD 集成。所有模型均经过 3 倍交叉验证。通过 Kaplan-Meier 分析测试每个模型的预后指数 (PI)，以对总生存期 (OS) 进行分层。我们纳入了 90 名接受 ICI 治疗的 NSCLC 患者（中位年龄 70 岁 [IQR=42 至 85]，63 名男性）。 Δ-RAD 在预测 OS 方面优于基线 RAD [c 指数：0.632（95%CI：0.628 至 0.636）与 0.605（95%CI：0.601 至 0.608）在测试分组中]。将全身炎症指数和 Δ-RAD 的纵向变化与临床数据相结合，获得了最佳模型性能 [Integrated-Δ 模型，c 指数：训练时为 0.750（95% CI：0.749 至 0.751），训练时为 0.718（95% CI：0.715）到 0.721）在测试分裂]。 PI在所有模型中实现了显着的OS分层（P值<0.01），在Δ模型中达到了最大的判别能力（高危组HR高达7.37，95％CI：3.9至13.94，P<0.01）。Δ-与单时间点放射组学相比，RAD 在晚期 ICI 治疗的 NSCLC 中改善了 OS 预测。将 Δ-RAD 与临床和实验室数据的纵向评估相结合，进一步改善了预后表现。版权所有 © 2024 Wolters Kluwer Health, Inc. 保留所有权利。

This study aims to determine whether longitudinal changes in CT radiomic features (RFs) and systemic inflammatory indices outperform single-time-point assessment in predicting survival in advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs).We retrospectively acquired pretreatment (T0) and first disease assessment (T1) RFs and systemic inflammatory indices from a single-center cohort of stage IV NSCLC patients and computed their delta (Δ) variation as [(T1-T0)/T0]. RFs from the primary tumor were selected for building baseline-radiomic (RAD) and Δ-RAD scores using the linear combination of standardized predictors detected by LASSO Cox regression models. Cox models were generated using clinical features alone or combined with baseline and Δ blood parameters and integrated with baseline-RAD and Δ-RAD. All models were 3-fold cross-validated. A prognostic index (PI) of each model was tested to stratify overall survival (OS) through Kaplan-Meier analysis.We included 90 ICI-treated NSCLC patients (median age 70 y [IQR=42 to 85], 63 males). Δ-RAD outperformed baseline-RAD for predicting OS [c-index: 0.632 (95%CI: 0.628 to 0.636) vs. 0.605 (95%CI: 0.601 to 0.608) in the test splits]. Integrating longitudinal changes of systemic inflammatory indices and Δ-RAD with clinical data led to the best model performance [Integrated-Δ model, c-index: 0.750 (95% CI: 0.749 to 0.751) in training and 0.718 (95% CI: 0.715 to 0.721) in testing splits]. PI enabled significant OS stratification within all the models (P-value <0.01), reaching the greatest discriminative ability in Δ models (high-risk group HR up to 7.37, 95% CI: 3.9 to 13.94, P<0.01).Δ-RAD improved OS prediction compared with single-time-point radiomic in advanced ICI-treated NSCLC. Integrating Δ-RAD with a longitudinal assessment of clinical and laboratory data further improved the prognostic performance.Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.