利用扩散MRI衍生血管密度对子宫内膜癌淋巴血管间隙浸润的术前预测。

摘要(中文译文)

评估扩散衍生血管密度（DDVD）用于子宫内膜癌（EC）术前预测淋巴血管间隙浸润（LVSI）的可行性，并探讨将DDVD与体素内不相干运动（IVIM）衍生参数及临床病理（CP）变量相结合是否能提高预测效能。这项探索性回顾性研究纳入194例经组织病理学确诊的EC患者（126例LVSI阴性，68例LVSI阳性），患者术前均接受多b值扩散加权磁共振成像。计算表观扩散系数、真实扩散系数（）、伪扩散系数（*）、灌注分数（）以及DDVD（由b = 0和10 s/mm²图像衍生）。进行单变量和多变量logistic回归分析，以确定LVSI的独立预测因素并建立预测模型。采用受试者工作特征（ROC）曲线分析评估预测性能。采用DeLong检验评估各模型间ROC曲线下面积（AUC）的差异，并应用Bonferroni校正调整多重比较。、DDVD以及三个CP因素（包括肌层浸润深度、组织学分级和肿瘤分期）为LVSI的独立预测因素。DDVD的AUC高于（0.772 vs. 0.692），但经Bonferroni校正后差异无统计学意义（校正 = 1.000）。当DDVD与保留的CP因素相结合时，预测性能显著提高（AUC，0.895 vs. 0.772；< 0.001）。结合、DDVD和CP预测因素的模型获得最高AUC（0.904），但经Bonferroni校正后与DDVD + CP模型的差异无统计学意义（校正 = 1.000）。DDVD可能是术前预测EC中LVSI的可行影像生物标志物。将DDVD与CP特征及IVIM衍生的相结合的多变量方法可能增强EC的术前风险分层。尚需进一步的前瞻性多中心验证。

Abstract

To evaluate the feasibility of diffusion-derived vessel density (DDVD) for the preoperative prediction of lymphovascular space invasion (LVSI) in endometrial cancer (EC) and to explore whether the integration of DDVD with intravoxel incoherent motion (IVIM)-derived parameters and clinicopathologic (CP) variables could improve the prediction. This exploratory retrospective study included 194 patients with histopathologically confirmed EC (126 LVSI-negative and 68 LVSI-positive) who underwent preoperative multi-b-value diffusion-weighted magnetic resonance imaging. The apparent diffusion coefficient, true diffusion coefficient (), pseudodiffusion coefficient (*), perfusion fraction (), and DDVD (derived from b = 0 and 10 s/mm² images) were calculated. Univariable and multivariable logistic regression analyses were performed to identify independent predictors of LVSI and to develop predictive models. The predictive performance was assessed using receiver operating characteristic (ROC) curve analysis. Differences in areas under the ROC curves (AUCs) among models were evaluated using DeLong's test, with the Bonferroni correction applied to adjust for multiple comparisons. The , DDVD, and three CP factors, including the depth of myometrial invasion, histologic grade, and tumor stage, emerged as independent predictors of LVSI. DDVD yielded a higher AUC than (0.772 vs. 0.692), although the difference was not statistically significant after Bonferroni correction (adjusted = 1.000). Predictive performance improved significantly when DDVD was combined with the retained CP factors (AUC, 0.895 vs. 0.772; < 0.001). The model combining the , DDVD, and CP predictors achieved the highest AUC of 0.904, although it was not significantly different from that of the DDVD + CP model after Bonferroni correction (adjusted = 1.000). DDVD may be a feasible imaging biomarker for the preoperative prediction of LVSI in EC. Multivariable approaches integrating DDVD with CP features and IVIM-derived may enhance preoperative risk stratification in EC. Further prospective multicenter validation is warranted.

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