Igarashi Takao, Shiraishi Megumi, Watanabe Ken, Ohki Kazuyoshi, Takenaga Shinsuke, Ashida Hirokazu, Ojiri Hiroya
Department of Radiology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan.
Pol J Radiol. 2021 May 19;86:e298-e308. doi: 10.5114/pjr.2021.106427. eCollection 2021.
To investigate the predictors of intraductal papillary mucinous neoplasms of the pancreas (IPMNs) with high-grade dysplasia, using 2-dimensional (2D) analysis and 3-dimensional (3D) volume-of-interest-based apparent diffusion coefficient (ADC) histogram analysis.
The data of 45 patients with histopathologically confirmed IPMNs with high-grade or low-grade dysplasia were retrospectively assessed. The 2D analysis included lesion-to-spinal cord signal intensity ratio (LSR), minimum ADC value (ADC), and mean ADC value (ADC). The 3D analysis included the overall mean (ADC), mean of the bottom 10 percentile (ADC), mean of the bottom 10-25 percentile (ADC), mean of the bottom 25-50 percentile (ADC), skewness (ADC), kurtosis (ADC), and entropy (ADC). Diagnostic performance was compared by analysing the area under the receiver operating characteristic curve (AUC). Inter-rater reliability was assessed by blinded evaluation using the intraclass correlation coefficient.
There were 16 and 29 IPMNs with high- and low-grade dysplasia, respectively. The LSR, ADC, ADC, ADC, ADC, and ADC showed significant between-group differences (AUC = 72-93%; < 0.05). Inter-rater reliability assessment showed almost perfect agreement for LSR and substantial agreement for ADC and ADC. Multivariate logistic regression showed that ADCoverall mean and ADCentropy were significant independent predictors of malignancy ( < 0.05), with diagnostic accuracies of 80% and 73%, respectively.
ADC and ADC from 3D analysis may assist in predicting IPMNs with high-grade dysplasia.
采用二维(2D)分析和基于三维(3D)感兴趣区的表观扩散系数(ADC)直方图分析,研究胰腺导管内乳头状黏液性肿瘤(IPMNs)伴高级别异型增生的预测因素。
回顾性评估45例经组织病理学证实为高级别或低级别异型增生的IPMNs患者的数据。二维分析包括病变与脊髓信号强度比(LSR)、最小ADC值(ADC)和平均ADC值(ADC)。三维分析包括总体均值(ADC)、最低10%百分位数均值(ADC)、最低10%-25%百分位数均值(ADC)、最低25%-50%百分位数均值(ADC)、偏度(ADC)、峰度(ADC)和熵(ADC)。通过分析受试者操作特征曲线(AUC)下的面积比较诊断性能。使用组内相关系数通过盲法评估评估评分者间信度。
分别有16例和29例高级别和低级别异型增生的IPMNs。LSR、ADC、ADC、ADC、ADC和ADC显示出显著的组间差异(AUC = 72%-93%;P < 0.05)。评分者间信度评估显示LSR几乎完全一致,ADC和ADC基本一致。多因素逻辑回归显示,ADC总体均值和ADC熵是恶性肿瘤的显著独立预测因素(P < 0.05),诊断准确率分别为80%和73%。
三维分析中的ADC和ADC可能有助于预测伴高级别异型增生的IPMNs。