Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.).
Department of Radiology, Lishui People's Hospital, Dazhong Road, Zhejiang, People's Republic of China (X.P.).
Acad Radiol. 2024 Jun;31(6):2324-2333. doi: 10.1016/j.acra.2023.11.016. Epub 2023 Nov 27.
To explore the potential value of the apparent diffusion coefficient (ADC)-based nomogram models in preoperatively assessing the depth of myometrial invasion of endometrial endometrioid adenocarcinoma (EEA).
Preoperative magnetic resonance imaging (MRI) of 210 EEA patients were retrospectively analyzed. ADC histogram metrics derive from the whole-tumor regions of interest. Univariate and multivariate analyses were used to screen the ADC histogram metrics and clinical characteristics for nomogram model building. The diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of two radiologists without and with the assistance of models were calculated and compared.
Two nomogram models were developed for predicting no myometrial invasion (NMI) and deep myometrial invasion (DMI) with area under the curves of 0.85 and 0.82, respectively. With the assistance of models, the overall accuracies were significantly improved [radiologist_1, 73.3% vs 86.2% (p = 0.001); radiologist_2, 80.0% vs 91.0% (p = 0.002)]. In determining NMI, the sensitivity and PPV were greatly improved but not significant for radiologist_1 (51.9% vs 77.8% and 46.7% vs 75.0%, p = 0.229 and 0.511), and under/near the significance level for radiologist_2 (59.3% vs 88.9% and 57.1% vs 82.8%, p = 0.041 and 0.065), while the specificity, accuracy, and NPV were significantly improved (all p < 0.001). In determining DMI, all sensitivity, specificity, accuracy, PPV, and NPV were significantly improved (all p < 0.001).
The ADC-based nomogram models can improve the diagnostic performance of radiologist in preoperatively assessing the depth of myometrial invasion and facilitate optimizing clinical individualized treatment decisions.
探讨基于表观扩散系数（ADC）的列线图模型在术前评估子宫内膜样腺癌（EEA）肌层浸润深度中的潜在价值。
回顾性分析 210 例 EEA 患者的术前磁共振成像（MRI）资料。通过感兴趣区（ROI）获得全肿瘤 ADC 直方图指标。采用单因素和多因素分析筛选 ADC 直方图指标和临床特征，以建立列线图模型。计算并比较两位放射科医生在有无模型辅助下的诊断灵敏度、特异度、准确率、阳性预测值（PPV）和阴性预测值（NPV）。
建立了两个预测无肌层浸润（NMI）和深肌层浸润（DMI）的列线图模型，曲线下面积分别为 0.85 和 0.82。有模型辅助时，整体准确率显著提高[放射科医生 1：73.3%比 86.2%（p=0.001）；放射科医生 2：80.0%比 91.0%（p=0.002）]。在判断 NMI 时，放射科医生 1 的灵敏度和 PPV 显著提高（51.9%比 77.8%和 46.7%比 75.0%，p=0.229 和 0.511），但差异无统计学意义，放射科医生 2 接近有统计学意义（59.3%比 88.9%和 57.1%比 82.8%，p=0.041 和 0.065），而特异性、准确率和 NPV 均显著提高（均 p<0.001）。在判断 DMI 时，所有灵敏度、特异度、准确率、PPV 和 NPV 均显著提高（均 p<0.001）。
ADC 列线图模型可提高放射科医生术前评估子宫内膜样腺癌肌层浸润深度的诊断效能，有助于优化临床个体化治疗决策。
