Multi-Organ metabolic profiling with [18F]F-FDG PET/CT predicts pathological response to neoadjuvant immunochemotherapy in resectable NSCLC.
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 patie...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 53; no. 1; pp. 128 - 142 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189634235&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189634235 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Dec2025 vid: 53 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189634235 185584014 10.1007/s00259-025-07350-8 189634235 ppf: 128 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-Organ metabolic profiling with [18F]F-FDG PET/CT predicts pathological response to neoadjuvant immunochemotherapy in resectable NSCLC. aug: au: Ma, Qiaoke Yang, Jinhui Guo, Xuan Mu, Wenna Tang, Yongxiang Li, Jian Hu, Shuo affil: https://ror.org/05c1yfj14 Department of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, 410008, Changsha City, Hunan Province, P.R. China sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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