不同数学模型多b值DWI在预测子宫内膜癌淋巴血管侵犯中的能力

目的 评估不同数学模型多b值扩散加权成像（diffusion-weighted imaging,DWI）在预测子宫内膜癌（endometrial cancer,EC）淋巴血管侵犯（lymphovascular space invasion,LVSI）中的能力。方法 收集2019年9月至2021年5月期间接受多b值DWI盆腔磁共振成像（magnetic resonance imaging,MRI）检查的患者61例。测量并比较表观扩散系数（apparent-diffusion-coefficient,ADC）、双指数模型参数（D、D*和f）和拉伸指数模型参数（DDC和α）,分析经病理证实的预后相关危险因素：组织学分级和淋巴血管侵犯（lymphovascular space invasion,LVSI）。进行受试者工作特征曲线评估不同模型参数在预测EC肿瘤分级和LVSI中的诊断性能。通过多元Logistic回归模型确定EC肿瘤分级和LVSI相关DWI模型参数。使用组内相关系数（ICC）评估观察者间一致性。结果 高级别组的ADC、D、f和DDC显著低于低级别组（AUC:0.699～0.882,...

Exploring the Efficacy of Different Mathematical Models with Multi-b Value DWI in Predicting Lymphatic Vascular Invasion of Endometrial Cancer

  Objective  To evaluate the efficacy of different mathematical models of multi-b-value DWI in predicting lymphatic vascular invasion （LVSI） of endometrial cancer （EC）.   Methods  The study population comprised 61 women who underwent multi-b-value DWI pelvic MRI between September 2019 and May 2021. The apparent-diffusion-coefficient （ADC）, bi-exponential model parameters （D, D* and f） and stretched-exponential model parameters （DDC and α） were measured and compared to analyze the following prognosis-related risk factors confirmed by pathology: histological grade and LVSI. The receiver operating characteristic （ROC） curve was performed to evaluate the diagnostic performance of these parameters in predicting EC histological grade and LVSI. The EC histological grade and LVSI related DWI model parameters were determined by multiple logistic regression model. The intra-group correlation coefficient （ICC） was used to assess the inter-observer agreement.   Results  The ADC, D, f, and DDC of the high histological grade group were significantly lower than those of the low histological grade group （P < 0.05, AUC: 0.699-0.882）. Compared with tumors without LVSI, tumors with LVSI had significantly lower ADC, D*, f and DDC values （ P < 0.05, AUC: 0.671-0.759）. The combination of f and DDC showed higher AUC （0.895, 0.797） than a single parameter in distinguishing high histological grade and LVSI tumors. Multivariate logistic regression analysis showed that both f and DDC can be used as independent predictors of histological grade and LVSI. ICC analysis showed that the inter-observer agreement for the D （ICC = 0.973; 95%CI = 0.956-0.985）, D* （ICC = 0.911; 95%CI = 0.851-0.946）, ADC （ICC = 0.968; 95%CI = 0.947-0.980）, f （ICC = 0.957; 95%CI = 0.922-0.974）, DDC （ICC = 0.947; 95%CI = 0.912?0.968） and α value （ICC = 0.931; 95%CI = 0.884-0.958） were very good.   Conclusions  Multi-b value DWI with different mathematical models is a useful and noninvasive approach for prediction of prognosis-related risk factors in EC with more comprehensive biological information.