Ma Qiaoke, Yang Jinhui, Guo Xuan, Mu Wenna, Tang Yongxiang, Li Jian, Hu Shuo
Department of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, Hunan Province, 410008, P.R. China.
National Clinical Research Center for Geriatric Disorders (Xiangya), Changsha, Hunan Province, P.R. China.
Eur J Nucl Med Mol Imaging. 2025 Jun 2. doi: 10.1007/s00259-025-07350-8.
To develop and validate a novel nomogram combining multi-organ PET metabolic metrics for major pathological response (MPR) prediction in resectable non-small cell lung cancer (rNSCLC) patients receiving neoadjuvant immunochemotherapy.
This retrospective cohort included rNSCLC patients who underwent baseline [F]F-FDG PET/CT prior to neoadjuvant immunochemotherapy at Xiangya Hospital from April 2020 to April 2024. Patients were randomly stratified into training (70%) and validation (30%) cohorts. Using deep learning-based automated segmentation, we quantified metabolic parameters (SUV, SUV, SUV, MTV, TLG) and their ratio to liver metabolic parameters for primary tumors and nine key organs. Feature selection employed a tripartite approach: univariate analysis, LASSO regression, and random forest optimization. The final multivariable model was translated into a clinically interpretable nomogram, with validation assessing discrimination, calibration, and clinical utility.
Among 115 patients (MPR rate: 63.5%, n = 73), five metabolic parameters emerged as predictive biomarkers for MPR: Spleen_SUV, Colon_SUV, Spine_TLG, Lesion_TLG, and Spleen-to-Liver SUV ratio. The nomogram demonstrated consistent performance across cohorts (training AUC = 0.78 [95%CI 0.67-0.88]; validation AUC = 0.78 [95%CI 0.62-0.94]), with robust calibration and enhanced clinical net benefit on decision curve analysis. Compared to tumor-only parameters, the multi-organ model showed higher specificity (100% vs. 92%) and positive predictive value (100% vs. 90%) in the validation set, maintaining 76% overall accuracy.
This first-reported multi-organ metabolic nomogram noninvasively predicts MPR in rNSCLC patients receiving neoadjuvant immunochemotherapy, outperforming conventional tumor-centric approaches. By quantifying systemic host-tumor metabolic crosstalk, this tool could help guide personalized therapeutic decisions while mitigating treatment-related risks, representing a paradigm shift towards precision immuno-oncology management.
开发并验证一种新型列线图，该列线图结合多器官PET代谢指标，用于预测接受新辅助免疫化疗的可切除非小细胞肺癌（rNSCLC）患者的主要病理缓解（MPR）。
该回顾性队列研究纳入了2020年4月至2024年4月在湘雅医院接受新辅助免疫化疗前进行基线[F]F-FDG PET/CT检查的rNSCLC患者。患者被随机分层为训练队列（70%）和验证队列（30%）。使用基于深度学习的自动分割技术，我们对原发性肿瘤和九个关键器官的代谢参数（SUV、SUV、SUV、MTV、TLG）及其与肝脏代谢参数的比值进行了量化。特征选择采用了三方方法：单变量分析、LASSO回归和随机森林优化。最终的多变量模型被转化为具有临床可解释性的列线图，并通过验证评估其区分度、校准度和临床实用性。
在115例患者中（MPR率：63.5%，n = 73），五个代谢参数成为MPR的预测生物标志物：脾脏SUV、结肠SUV、脊柱TLG、病灶TLG和脾脏与肝脏SUV比值。列线图在各队列中表现一致（训练集AUC = 0.78 [95%CI 0.67 - 0.88]；验证集AUC = 0.78 [95%CI 0.62 - 0.94]），在决策曲线分析中具有稳健的校准度和更高的临床净效益。与仅基于肿瘤的参数相比，多器官模型在验证集中显示出更高的特异性（100%对92%）和阳性预测值（100%对90%），总体准确率保持在76%。
这种首次报道的多器官代谢列线图可无创预测接受新辅助免疫化疗的rNSCLC患者的MPR，优于传统的以肿瘤为中心的方法。通过量化全身宿主-肿瘤代谢相互作用，该工具可帮助指导个性化治疗决策，同时降低治疗相关风险，代表了向精准免疫肿瘤学管理的范式转变。
