RESUMO
BACKGROUND: The intravoxel incoherent motion (IVIM) method of magnetic resonance imaging (MRI) analysis can provide information regarding many physiological and pathological processes. This study aimed to investigate whether IVIM-derived parameters and the apparent diffusion coefficient (ADC) can act as imaging biomarkers for predicting non-small cell lung cancer (NSCLC) response to anti-tumor therapy and compare their performances. METHODS: This prospective study included 45 patients with NSCLC treated with chemotherapy (29 men and 16 women, mean age 57.9±9.7 years). Diffusion-weighted imaging was performed with 13 b-values before and 2-4 weeks after treatment. The IVIM parameter pseudo-diffusion coefficient (D*), perfusion fraction (f), diffusion coefficient (D), and ADC from a mono-exponential model were obtained. Responses 2 months after chemotherapy were assessed. The diagnostic performance was evaluated, and optimal cut-off values were determined by receiver operating characteristic (ROC) curve analysis, and the differences of progression-free survival (PFS) in groups of responders and non-responders were tested by Cox regression and Kaplan-Meier survival analyses. RESULTS: Of 45 patients, 30 (66.7%) were categorized as responders, and 15 as non-responders. Differences in the diffusion coefficient D and ADC between responders and non-responders were statistically significant (all P<0.05). Conversely, differences in f and D* between responders and non-responders were both not statistically significance (all P>0.05). The ROC analyses showed the change in D value (ΔD) was the best predictor of early response to anti-tumor therapy [area under the ROC curve (AUC), 0.764]. The Cox-regression model showed that all ADC and D parameters were independent predictors of PFS, with a range of reduction in risk from 56.2% to 82.7%, and ΔD criteria responders had the highest reduction (82.7%). CONCLUSIONS: ADC and D derived from IVIM are potentially useful for the prediction of NSCLC treatment response to anti-tumor therapy. Although ΔD is best at predicting response to treatment, ΔADC measurement may simplify manual efforts and reduce the workload.