目的:评估基于弥散加权成像(DWI)的定量参数在术前磁共振成像(MRI)上区分子宫肉瘤和非典型平滑肌瘤的附加值。
方法:共138例患者(年龄,从四个机构回顾性收集了43.7±10.3年)子宫肉瘤(n=44)和非典型平滑肌瘤(n=94)。队列随机分为训练组(84/138,60.0%)和验证组(54/138,40.0%)。两名独立读者评估了每个指标肿瘤的六个定性MRI特征和两个基于DWI的定量参数。使用多变量逻辑回归来识别相关的MRI定性特征。使用逻辑回归算法开发了仅基于定性MRI特征并结合基于DWI的定量参数的诊断分类器。使用交叉表分析和受试者工作特征曲线下面积(AUC)的计算来评估分类器的诊断性能。
结果:子宫肉瘤的平均表观扩散系数值低于非典型平滑肌瘤(平均值±标准偏差,0.94±0.3010-3mm²/svs.1.23±0.2510-3mm²/s;P<0.001),子宫肉瘤的相对对比度较高(8.16±2.94vs.4.19±2.66;P<0.001)。选定的MRI定性特征包括界限不清(调整后的比值比[aOR],17.9;95%置信区间[CI],1.41-503,P=0.040),肿瘤内出血(aOR,27.3;95%CI,3.74-596,P=0.006),并且没有T2暗区(aOR,83.5;95%CI,12.4-1916,P<0.001)。结合定性MRI特征和基于DWI的定量参数的分类器在验证集中显示出比没有基于DWI的参数(AUC,0.92vs.0.78;P<0.001)。
结论:将基于DWI的定量参数添加到定性MRI特征中,提高了逻辑回归分类器在术前MRI上区分子宫肉瘤和非典型平滑肌瘤的诊断性能。
OBJECTIVE: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI).
METHODS: A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC).
RESULTS: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10-3 mm²/s vs. 1.23 ± 0.25 10-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001).
CONCLUSIONS: The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.