Development of a predictive model for lymph node metastasis in esophageal cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) semi-quantitative parameters and tumor biomarkers
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: F-FDG PET/CT semi-quantitative parameters combined with tumor markers in predicting EC-related LNM. Methods: F-FDG PET/CT. Inclusion criteria: no prior anticancer treatment, complete clinical/imaging/tumor marker data. LNM was confirmed by postoperative pathological examination. PET/CT parameters such as the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of primary lesions and common EC-related tumor markers were tested. Univariate/multivariate analyses identified independent predictors, and three prediction models with different parameter combinations were constructed. Predictive accuracy was assessed via receiver operating characteristic (ROC) curves. Results: Patients were mostly male (75%) with median age 62 years and squamous cell carcinoma accounted for 90%. Univariate analysis showed significant differences in tumor diameter, tumor (T) stage, and all PET/CT parameters between LNM and non-LNM groups (all P<0.05). Multivariate analysis confirmed carcinoembryonic antigen (CEA) [odds ratio (OR) =1.326], SUVmax (OR =1.351), mean standardized uptake value (SUVmean) (OR =22.391), and MTV (OR =1.198) as independent predictors (all P<0.05). MTV had the best single-parameter predictive performance [area under the curve (AUC) =0.878, optimal cutoff 11.88]. The combined model [carbohydrate antigen 724 (CA724) + SUVmean + SUVmax + MTV + TLG] showed the highest efficacy (AUC =0.965, sensitivity 94.90%, specificity 86.05%). Conclusions: F-FDG PET/CT metabolic parameters (especially MTV) combined with CA724 significantly improve the accuracy of preoperative LNM prediction in EC, helping clinicians optimize surgical scope and adjuvant therapy, thereby improving patient prognosis.
Authors: Nan Li, Min Huang, Wenjun Bao, Chenmin Ding, Xibao Mao
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7117452628
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