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

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.

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.

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.

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 mm²/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.

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.