AUTHOR=Li Jiaxin , Wu Baolin , Huang Zhun , Zhao Yixiang , Zhao Sen , Guo Shuaikang , Xu Shufei , Wang Xiaolei , Tian Tiantian , Wang Zhixue , Zhou Jun 

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

JOURNAL=Frontiers in Oncology

VOLUME=12

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.1082454

DOI=10.3389/fonc.2022.1082454

ISSN=2234-943X

ABSTRACT=<sec><title>Background</title><p>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.</p></sec><sec><title>Purpose</title><p>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.</p></sec><sec><title>Methods</title><p>Fifty-nine patients with solitary pulmonary lesions underwent multiple <italic>b</italic>-values DWIs, which were then postprocessed using mono-exponential, bi-exponential and DKI models. Histogram parameters of the apparent diffusion coefficient (ADC), true diffusivity (<italic>D</italic>), pseudo-diffusion coefficient (<italic>D*</italic>), and perfusion fraction (<italic>f</italic>), apparent diffusional kurtosis (K<sub>app</sub>) and kurtosis-corrected diffusion coefficient (D<sub>app</sub>) were calculated and compared between the lung cancer and inflammatory lesion groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance.</p></sec><sec><title>Results</title><p>The ADC<sup>mean</sup>, ADC<sup>median</sup>, <italic>D</italic><sup>mean</sup> and <italic>D</italic><sup>median</sup> values of lung cancer were significantly lower than those of inflammatory lesions, while the ADC<sup>skewness</sup>, K<sub>app</sub><sup>mean</sup>, K<sub>app</sub><sup>median</sup>, K<sub>app</sub><sup>SD</sup>, K<sub>app</sub><sup>kurtosis</sup> and D<sub>app</sub><sup>skewness</sup> values of lung cancer were significantly higher than those of inflammatory lesions (all <italic>p</italic> &lt; 0.05). ADC<sup>skewness</sup> (<italic>p</italic> = 0.019) and <italic>D</italic><sup>median</sup> (<italic>p</italic> = 0.031) were identified as independent predictors of lung cancer. <italic>D</italic><sup>median</sup> showed the best performance for differentiating lung cancer from inflammatory lesions, with an area under the ROC curve of 0.777. Using a <italic>D</italic><sup>median</sup> of 1.091 × 10<sup>-3</sup> mm<sup>2</sup>/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.</p></sec><sec><title>Conclusions</title><p>Whole-lesion histogram analysis of DWI, IVIM and DKI parameters is a promising approach for differentiating lung cancer from inflammatory lesions, and <italic>D</italic><sup>median</sup> shows the best performance in the differential diagnosis of solitary pulmonary lesions.</p></sec>