Development of a multimodal predictive model using PET/CT radiomics and clinical data for preoperative assessment of lymphovascular invasion in gastric cancer.

OBJECTIVES

To develop and validate a multimodal predictive model combining positron emission tomography/computed tomography (PET/CT) radiomic features with clinical data for the preoperative assessment of lymphovascular invasion (LVI) in patients with gastric cancer (GC).

METHODS

Between December 2017 and December 2022, 325 GC patients with pathologically confirmed LVI status were retrospectively enrolled. PET/CT scans were performed according to standard protocols, and 1,057 radiomic features were extracted from both imaging modalities following appropriate preprocessing. LASSO regression was used to select informative features for separate CT, PET, and combined PET/CT models. Key clinical variables - including age, maximum standardized uptake value, total lesion glycolysis, lymph node metastasis, and tumor grade - were integrated using multivariate logistic regression to construct a comprehensive predictive model. Model performance was assessed using ROC curve analysis. Diagnostic metrics - including AUC, sensitivity, specificity, accuracy, and Net Reclassification Improvement (NRI) - were calculated for each model.

RESULTS

The CT, PET, and combined PET/CT models achieved AUCs of 0.823, 0.761, and 0.861, respectively. The final multimodal model integrating PET/CT radiomics with clinical data demonstrated superior performance, with an AUC of 0.904, specificity of 91.91% and sensitivity of 74.07%. Independent predictors of LVI included age, SUVmax, TLG, and lymph node metastasis. NRI analysis showed a 10.35% improvement in risk classification compared to the PET/CT radiomic model alone.

CONCLUSIONS

The multimodal predictive model demonstrated excellent diagnostic accuracy for preoperative assessment of LVI in GC patients and may support individualized treatment planning and risk stratification. Prospective multicenter studies are needed to further validate its clinical utility.