A pretreatment 18F-FDG PET/CT-based nomogram for the differential diagnosis of primary gastric cancer and primary gastric diffuse large B-cell lymphoma.

BACKGROUND

It is challenging to differentiate between primary gastric cancer (GC) and primary gastric diffuse large B-cell lymphoma (PG-DLBCL) with traditional imaging modalities. This study aimed to develop a predictive nomogram model for differential diagnosis of primary GC and PG-DLBCL by integrating semiquantitative metabolic parameters from pre-treatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) with morphological features derived from the concurrent CT imaging.

METHODS

A total of 111 patients with histologically confirmed gastric malignancies who underwent pre-treatment 18F-FDG PET/CT were retrospectively analyzed, including 86 cases of GC and 25 cases of PG-DLBCL. A nomogram was constructed based on imaging features from the CT scan (primary lesion type and involvement of infra-renal lymph nodes) and PET-derived semiquantitative parameters, specifically the tumor-to-liver standardized uptake value ratio (TLR-SUV). The diagnostic performance and clinical utility of the model were evaluated using the area under the curve (AUC), calibration plots, and decision curve analysis (DCA).

RESULTS

Primary lesiontype, infra-renal lymph node involvement, and TLR-SUV were identified as independent discriminators between GC and PG-DLBCL. The integrated nomogram model combining CT-based morphological characteristics and PET-derived metabolic metrics achieved an AUC of 0.877 [95% confidence interval (CI): 0.796-0.957] for distinguishing GC from PG-DLBCL, demonstrating robust discrimination and excellent calibration. DCA further confirmed the model's potential to provide valuable clinical decision support across a range of risk thresholds.

CONCLUSIONS

The nomogram model constructed by integrating concurrent CT morphological features with PET metabolic parameters exhibited high diagnostic efficacy in distinguishing GC from PG-DLBCL, and serves as a promising non-invasive imaging tool.