Preoperative PET/CT phenotype of gastric-type endocervical adenocarcinoma: integrated morphological, metabolic, serological, and explainable machine-learning analysis

Eur J Nucl Med Mol Imaging. 2026 Jul 22. doi: 10.1007/s00259-026-08088-7. Online ahead of print.

ABSTRACT

PURPOSE: Gastric-type endocervical adenocarcinoma (GAS) is an aggressive, non-HPV-associated cervical adenocarcinoma that is often difficult to recognize preoperatively. This study aimed to characterize the integrated PET/CT phenotype of GAS and evaluate whether morphological, metabolic, serological, and explainable machine-learning features could support its differentiation from squamous cell carcinoma (SCC) and usual-type endocervical adenocarcinoma (UEA).

MATERIALS AND METHODS: This retrospective study included 144 patients with histologically confirmed cervical cancer who underwent pretreatment 18 F-FDG PET/CT, including 22 with GAS, 82 with SCC, and 40 with UEA. Clinical characteristics, serum tumor markers, PET/CT-derived morphological features, metabolic parameters, and dissemination-related variables were collected. Intergroup differences were assessed using appropriate statistical tests. Five machine-learning models were developed for histological differentiation, and model performance was evaluated using ROC analysis, classification metrics, calibration assessment, and decision curve analysis. SHAP analysis was used to interpret feature contributions.

RESULTS: GAS demonstrated a distinctive PET/CT phenotype characterized by diffuse infiltrative growth, cystic morphology, intrauterine fluid accumulation, relatively lower FDG uptake, CA19-9 positivity, and more frequent distant and peritoneal metastasis. The median cervical lesion SUVmax was lower in GAS than in SCC and UEA, and similar trends were observed for liver-normalized and blood pool-normalized SUV ratios. Despite its relatively low metabolic activity, GAS showed more aggressive dissemination-related features. Among the machine-learning models, tree-based ensemble models showed better exploratory discriminative performance than Logistic Regression and multilayer perceptron. SHAP analysis indicated that growth pattern, cystic morphology, intrauterine fluid, cervical lesion SUVmax, liver SUV ratio, blood pool ratio, CA19-9, and SCC antigen were the main contributors to model prediction.

CONCLUSION: GAS exhibits a recognizable PET/CT phenotype characterized by a descriptive metabolic-morphological mismatch, namely relatively low primary-tumor FDG uptake despite aggressive infiltrative morphology and metastatic dissemination. Integrated assessment of PET/CT morphology, metabolic parameters, tumor markers, and dissemination patterns may help raise preoperative suspicion of GAS and guide further pathological work-up. Explainable machine learning may serve as a complementary tool for feature integration, but external validation is required before clinical implementation.

PMID:42484891 | DOI:10.1007/s00259-026-08088-7