Multi-Organ metabolic profiling with [<sup>18</sup>F]F-FDG PET/CT predicts pathological response to neoadjuvant immunochemotherapy in resectable NSCLC.
Authors
Affiliations (5)
Affiliations (5)
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, Hunan Province, 410008, P.R. China.
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, Hunan Province, 410008, P.R. China. [email protected].
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, Hunan Province, 410008, P.R. China. [email protected].
- National Clinical Research Center for Geriatric Disorders (Xiangya), Changsha, Hunan Province, P.R. China. [email protected].
- Key Laboratory of Biological Nanotechnology of National Health Commission, Changsha, Hunan Province, P.R. China. [email protected].
Abstract
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 [<sup>18</sup>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<sub>mean</sub>, SUV<sub>max</sub>, SUV<sub>peak</sub>, 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<sub>mean</sub>, Colon_SUV<sub>peak</sub>, Spine_TLG, Lesion_TLG, and Spleen-to-Liver SUV<sub>max</sub> 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.