Abstract
OBJECTIVES: To evaluate the potential value of (18)F-FDG positron emission tomography (PET) and multiparametric MRI (intravoxel incoherent motion, IVIM, and diffusion kurtosis imaging, DKI) in the prediction of lymphovascular invasion (LVI) in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: A total of 73 patients with NSCLC who underwent integrated (18)F-FDG PET/MRI were included. IVIM, DKI, and PET parameters with or without LVI of NSCLC were measured and compared, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic efficacy of each parameter. Univariate and multivariate logistic regression models were used to study the optimal combination of PET/MRI parameters for predicting LVI. RESULTS: PET-derived parameters (SUVmax, MTV, TLG) and IVIM, DKI MRI-derived parameters (ADCstand, D, MK, MD) were significantly different between patients with and without LVI (p < 0.05). Multivariate logistic regression analysis showed that MTV and D were independent predictors of LVI, and the combined prediction model of the two parameters had the highest predictive value for the diagnosis of LVI (AUC = 0.841; sensitivity = 63.83%; specificity = 92.31%). CONCLUSION: The present study demonstrates that IVIM, DKI, and PET can be utilized to evaluate LVI status in NSCLC, with the combined diagnostic approach of MTV and D showing the highest diagnostic performance, which may provide a novel reference for clinical management. CRITICAL RELEVANCE STATEMENT: The performance of metabolic parameters and diffusion parameters in the identification of lymphovascular invasion (LVI) in non-small cell lung cancer (NSCLC) is similar, but the combination of metabolic tumor volume (MTV) and true diffusion coefficient (D) may improve the diagnostic efficacy. KEY POINTS: A multimodal PET-MRI model evaluates lymphovascular invasion (LVI) in patients with non-small cell lung cancer (NSCLC). Metabolic and diffusion parameters have similar efficacy in predicting LVI in NSCLC. The combined metabolic tumor volume and true diffusion coefficient prediction model is the most valuable.