He Rong, Song Gesheng, Fu Junyi, Dou Weiqiang, Li Aiyin, Chen Jingbo
Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
MR Research, GE Healthcare, Beijing, China.
Quant Imaging Med Surg. 2024 Aug 1;14(8):5358-5372. doi: 10.21037/qims-23-1614. Epub 2024 Jul 26.
Unfortunately, the morphologic magnetic resonance imaging (MRI) is unable to determine perineural invasion (PNI) status. This study applied histogram analysis of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the assessment of PNI status of rectal cancer (RC).
The retrospective analysis enrolled 175 patients with RC confirmed by postoperative pathology in The First Affiliated Hospital of Shandong First Medical University from January 2019 to December 2021. All patients underwent preoperative rectal MRI. Whole-tumor volume histogram features from IVIM-DWI were extracted using open-source software. Univariate analysis and multivariate logistic regression analysis were used to compare the differences in histogram parameters and clinical features between the PNI-positive group and PNI-negative group. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance, while the Delong test was used to compare the area under the curve of the models.
The interobserver agreement of the histogram features derived from DWI, including apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), water molecular diffusion heterogeneity index (α), and distributed diffusion coefficient (DDC) were good to excellent. A total of eight histogram features including DWI_maximum, DWI_skewness, D_kurtosis, D_minimum, D_skewness, D*_energy, D*_skewness, and f_minimum were significantly different between the PNI-positive and PNI-negative groups in the univariate analysis (P<0.05); among the clinicoradiologic factors, percentage of rectal wall circumference invasion (PCI) was significantly different between the two groups (P<0.05). Multivariate analysis demonstrated that the values of D*_energy, D*_skewness, and f_minimum differed significantly between the PNI-positive patients and PNI-negative patients (P<0.05), with the independent risk factors being D*_skewness [odds ratio (OR) =1.157; 95% confidence interval (CI): 1.050-1.276; P=0.003] and PCI (OR =11.108, 95% CI: 1.767-69.838; P=0.0002). The area under the curve of the model combining the three histogram features and PCI to assess PNI status in RC was 0.807 (95% CI: 0.741-0.863). The results of the Delong test showed that the combined model was significantly different from each single-parameter model (P<0.05).
The combined model constructed on the basis of IVIM-DWI histogram features may help to assess the status of RC PNI.
遗憾的是，形态学磁共振成像（MRI）无法确定神经周围浸润（PNI）状态。本研究应用体素内不相干运动扩散加权成像（IVIM-DWI）的直方图分析来评估直肠癌（RC）的PNI状态。
回顾性分析纳入了2019年1月至2021年12月在山东第一医科大学第一附属医院经术后病理确诊的175例RC患者。所有患者均接受了术前直肠MRI检查。使用开源软件提取IVIM-DWI的全肿瘤体积直方图特征。采用单因素分析和多因素逻辑回归分析比较PNI阳性组和PNI阴性组之间直方图参数和临床特征的差异。采用受试者操作特征曲线分析评估诊断性能，同时使用德龙检验比较模型的曲线下面积。
从扩散加权成像（DWI）得出的直方图特征，包括表观扩散系数（ADC）、真扩散系数（D）、伪扩散系数（D*）、灌注分数（f）、水分子扩散异质性指数（α）和分布扩散系数（DDC）的观察者间一致性良好至优秀。单因素分析显示，PNI阳性组和PNI阴性组之间共有8个直方图特征存在显著差异，包括DWI_最大值、DWI_偏度、D_峰度、D_最小值、D_偏度、D*_能量、D*_偏度和f_最小值（P<0.05）；在临床放射学因素中，两组之间直肠壁周径侵犯百分比（PCI）存在显著差异（P<0.05）。多因素分析表明，PNI阳性患者和PNI阴性患者之间D*_能量、D*_偏度和f_最小值的值存在显著差异（P<0.05），独立危险因素为D*_偏度[比值比（OR）=1.157；95%置信区间（CI）：1.0
