Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions

BackgroundWhole-lesion histogram analysis can provide comprehensive assessment of tissues by calculating additional quantitative metrics such as skewness and kurtosis; however, few studies have evaluated its value in the differential diagnosis of lung lesions. PurposeTo compare the diagnostic performance of conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and diffusion kurtosis imaging (DKI) in differentiating lung cancer from focal inflammatory lesions, based on whole-lesion volume histogram analysis. MethodsFifty-nine patients with solitary pulmonary lesions underwent multiple b-values DWIs, which were then postprocessed using mono-exponential, bi-exponential and DKI models. Histogram parameters of the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), apparent diffusional kurtosis (K-app) and kurtosis-corrected diffusion coefficient (D-app) were calculated and compared between the lung cancer and inflammatory lesion groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance. ResultsThe ADC(mean), ADC(median), D-mean and D-median values of lung cancer were significantly lower than those of inflammatory lesions, while the ADC(skewness), K-app(mean), K-app(median), K-app(SD), K-app(kurtosis) and D-app(skewness) values of lung cancer were significantly higher than those of inflammatory lesions (all p < 0.05). ADC(skewness) (p = 0.019) and D-median (p = 0.031) were identified as independent predictors of lung cancer. D-median showed the best performance for differentiating lung cancer from inflammatory lesions, with an area under the ROC curve of 0.777. Using a D-median of 1.091 x 10(-3) mm(2)/s as the optimal cut-off value, the sensitivity, specificity, positive predictive value and negative predictive value were 69.23%, 85.00%, 90.00% and 58.62%, respectively. ConclusionsWhole-lesion histogram analysis of DWI, IVIM and DKI parameters is a promising approach for differentiating lung cancer from inflammatory lesions, and D-median shows the best performance in the differential diagnosis of solitary pulmonary lesions.