European journal of nuclear medicine and molecular imaging · 2025
Multi-Organ metabolic profiling with [18F]F-FDG PET/CT predicts pathological response to neoadjuvant immunochemotherapy in resectable NSCLC.
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Ma Qiaoke, Yang Jinhui, Guo Xuan, Mu Wenna, Tang Yongxiang, Li Jian, Hu Shuo
Аннотация
PURPOSE: 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.
METHODS: This retrospective cohort included rNSCLC patients who underwent baseline [18F]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 (SUVmean, SUVmax, SUVpeak, 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.
RESULTS: Among 115 patients (MPR rate: 63.5%, n = 73), five metabolic parameters emerged as predictive biomarkers for MPR: Spleen_SUVmean, Colon_SUVpeak, Spine_TLG, Lesion_TLG, and Spleen-to-Liver SUVmax 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.
CONCLUSIONS: 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.
METHODS: This retrospective cohort included rNSCLC patients who underwent baseline [18F]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 (SUVmean, SUVmax, SUVpeak, 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.
RESULTS: Among 115 patients (MPR rate: 63.5%, n = 73), five metabolic parameters emerged as predictive biomarkers for MPR: Spleen_SUVmean, Colon_SUVpeak, Spine_TLG, Lesion_TLG, and Spleen-to-Liver SUVmax 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.
CONCLUSIONS: 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.