Monti Serena, Palma Giuseppe, Xu Ting, Mohan Radhe, Liao Zhongxing, Cella Laura
National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy.
National Research Council, Institute of Nanotechnology, Lecce, Italy.
Phys Imaging Radiat Oncol. 2025 May 19;34:100782. doi: 10.1016/j.phro.2025.100782. eCollection 2025 Apr.
Radiation-induced lymphopenia (RIL) is a significant side effect associated with radiation therapy (RT) with important prognostic implications. We developed and tested a normal tissue complication probability (NTCP) model for Grade 4 (G4) RIL in patients with locally advanced Non-Small-Cell Lung Cancer (NSCLC) who underwent concurrent chemotherapy and RT, analyzing data from patients enrolled in two clinical trials.
We retrospectively analyzed the data from NCT00915005 (MDA-cohort) and NCT00533949 (RTOG0617-cohort) trials. After finding the candidate predictors of G4-RIL, defined as absolute lymphocyte count (ALC) at nadir < 0.2*10 cells/l during RT, we trained an NTCP model on the MDA-cohort and tested it on the RTOG-cohort, based on common available variables in the two cohorts. Model performance was assessed in terms of discrimination and calibration.
In the MDA-cohort, 55 out of 161 (34%) patients developed G4-RIL, while in the RTOG-cohort 16 out of 227 (7%) developed this condition. The relative volume of healthy lungs receiving at least 5 Gy (V) and baseline ALC were selected as predictors in an NTCP model, with good discriminative performances (cross validated ROC-AUC: 0.68). The predictive value of V was confirmed in the RTOG0917-cohort (ROC-AUC: 0.67), although its validation was limited with suboptimal calibration, potentially due to discrepancies between cohorts.
Baseline ALC and lung V were identified as predictors for G4-RIL, consistent with findings from previous studies. Treatment plan optimization aiming at reducing low-dose bath in the lungs could be an effective strategy for severe RIL mitigation.
放射性淋巴细胞减少症（RIL）是放射治疗（RT）的一种显著副作用，具有重要的预后意义。我们开发并测试了一种正常组织并发症概率（NTCP）模型，用于预测局部晚期非小细胞肺癌（NSCLC）患者在同步放化疗时发生4级（G4）RIL的情况，分析了两项临床试验中入组患者的数据。
我们回顾性分析了NCT00915005（MDA队列）和NCT00533949（RTOG0617队列）试验的数据。在确定了G4-RIL的候选预测因素（定义为放疗期间最低点绝对淋巴细胞计数（ALC）<0.2×10⁹细胞/升）后，我们基于两个队列中常见的可用变量，在MDA队列上训练了一个NTCP模型，并在RTOG队列上进行了测试。从区分度和校准度方面评估模型性能。
在MDA队列中，161例患者中有55例（34%）发生了G4-RIL，而在RTOG队列中，227例患者中有16例（7%）发生了这种情况。在一个NTCP模型中，选择接受至少5 Gy照射的健康肺组织的相对体积（V）和基线ALC作为预测因素，具有良好的区分性能（交叉验证的ROC-AUC：0.68）。V的预测价值在RTOG0917队列中得到了证实（ROC-AUC：0.67），尽管其验证因校准欠佳而受到限制，这可能是由于队列之间的差异所致。
基线ALC和肺V被确定为G4-RIL的预测因素，与先前研究结果一致。旨在减少肺部低剂量照射范围的治疗计划优化可能是减轻严重RIL的有效策略。
