AI Article Synopsis
- Whole-lesion histogram analysis can enhance the evaluation of lung lesions by providing additional metrics like skewness and kurtosis, yet its effectiveness in distinguishing between types of lung lesions remains underexplored.
- This study aimed to assess and compare the diagnostic accuracy of diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM) MRI, and diffusion kurtosis imaging (DKI) for differentiating lung cancer from inflammatory lesions using histogram analysis.
- Results indicated that certain diffusion parameters (ADC and ) were significantly lower in lung cancer versus inflammatory lesions, with identified predictors showing promising diagnostic performance and a specific cut-off value yielding moderate sensitivity and high specificity for lung cancer detection.
Article Abstract
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 -values DWIs, which were then postprocessed using mono-exponential, bi-exponential and DKI models. Histogram parameters of the apparent diffusion coefficient (ADC), true diffusivity (), pseudo-diffusion coefficient (), and perfusion fraction (), apparent diffusional kurtosis (K) and kurtosis-corrected diffusion coefficient (D) 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, ADC, and values of lung cancer were significantly lower than those of inflammatory lesions, while the ADC, K , K , K , K and D values of lung cancer were significantly higher than those of inflammatory lesions (all < 0.05). ADC ( = 0.019) and ( = 0.031) were identified as independent predictors of lung cancer. showed the best performance for differentiating lung cancer from inflammatory lesions, with an area under the ROC curve of 0.777. Using a of 1.091 × 10 mm/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 shows the best performance in the differential diagnosis of solitary pulmonary lesions.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9890049 | PMC |
| http://dx.doi.org/10.3389/fonc.2022.1082454 | DOI Listing |
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