Li Jiaxin, Wu Baolin, Huang Zhun, Zhao Yixiang, Zhao Sen, Guo Shuaikang, Xu Shufei, Wang Xiaolei, Tian Tiantian, Wang Zhixue, Zhou Jun
Department of Radiology, The First Affiliated Hospital of Henan University, Kaifeng, China.
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
Front Oncol. 2023 Jan 18;12:1082454. doi: 10.3389/fonc.2022.1082454. eCollection 2022.
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 -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.
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.
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.
全病灶直方图分析可通过计算诸如偏度和峰度等额外的定量指标来对组织进行全面评估；然而，很少有研究评估其在肺病变鉴别诊断中的价值。
基于全病灶体积直方图分析，比较传统扩散加权成像（DWI）、体素内不相干运动（IVIM）磁共振成像（MRI）和扩散峰度成像（DKI）在鉴别肺癌与局灶性炎性病变中的诊断性能。
59例孤立性肺病变患者接受了多值DWI检查，随后使用单指数、双指数和DKI模型进行后处理。计算并比较肺癌组和炎性病变组的表观扩散系数（ADC）、真扩散率（）、伪扩散系数（）和灌注分数（）、表观扩散峰度（K）和峰度校正扩散系数（D）的直方图参数。构建受试者操作特征（ROC）曲线以评估诊断性能。
肺癌的ADC、ADC、和值显著低于炎性病变，而肺癌的ADC、K、K、K、K和D值显著高于炎性病变（均P<0.05）。ADC（P=0.019）和（P=0.031）被确定为肺癌的独立预测因子。在鉴别肺癌与炎性病变方面表现最佳，ROC曲线下面积为0.777。以1.091×10mm/s作为最佳截断值，灵敏度、特异度、阳性预测值和阴性预测值分别为69.23%、85.00%、90.00%和58.62%。
对DWI、IVIM和DKI参数进行全病灶直方图分析是鉴别肺癌与炎性病变的一种有前景的方法，并且在孤立性肺病变的鉴别诊断中表现最佳。
