Balbi Maurizio, Mazzaschi Giulia, Leo Ludovica, Moron Dalla Tor Lucas, Milanese Gianluca, Marrocchio Cristina, Silva Mario, Mura Rebecca, Favia Pasquale, Bocchialini Giovanni, Trentini Francesca, Minari Roberta, Ampollini Luca, Quaini Federico, Roti Giovanni, Tiseo Marcello, Sverzellati Nicola
Unit of Scienze Radiologiche.
Department of Medicine and Surgery, University of Parma, Parma, Italy.
J Thorac Imaging. 2025 Jan 1;40(1):e0801. doi: 10.1097/RTI.0000000000000801.
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.
本研究旨在确定CT影像组学特征（RFs）和全身炎症指标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）患者的生存方面是否优于单时间点评估。
我们回顾性收集了来自一个单中心队列的IV期NSCLC患者的治疗前（T0）和首次疾病评估（T1）的RFs和全身炎症指标，并计算其变化量（Δ）为[(T1 - T0)/T0]。选择原发肿瘤的RFs，使用LASSO Cox回归模型检测的标准化预测因子的线性组合构建基线影像组学（RAD）和Δ - RAD评分。仅使用临床特征或结合基线和Δ血液参数生成Cox模型，并与基线 - RAD和Δ - RAD整合。所有模型均进行3折交叉验证。通过Kaplan - Meier分析测试每个模型的预后指数（PI）以分层总生存期（OS）。
我们纳入了90例接受ICI治疗的NSCLC患者（中位年龄70岁[四分位间距 = 42至85岁]，63例男性）。在预测OS方面，Δ - RAD优于基线 - RAD [测试分割中c指数：0.632（95%置信区间：0.628至0.636）对0.605（95%置信区间：0.601至0.608）]。将全身炎症指标的纵向变化和Δ - RAD与临床数据相结合导致最佳模型性能[综合Δ模型，训练中c指数：0.750（95%置信区间：0.749至0.751），测试分割中为0.718（95%置信区间：0.715至0.721）]。PI在所有模型中均实现了显著的OS分层（P值<0.01），在Δ模型中具有最大的判别能力（高风险组HR高达7.37，95%置信区间：3.9至13.94，P<0.01）。
与单时间点影像组学相比，Δ - RAD改善了晚期接受ICI治疗的NSCLC患者的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提高了预后性能。
