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

BACKGROUND: Whole-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. PURPOSE: To 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. METHODS: Fifty-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. RESULTS: The 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 × 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. CONCLUSIONS: Whole-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.