Department of Radiology, Peking University First Hospital, Beijing, China.
Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Eur J Radiol. 2019 Jun;115:39-45. doi: 10.1016/j.ejrad.2019.04.004. Epub 2019 Apr 5.
To explore whether MRI and radiomic features can differentiate uterine sarcoma from atypical leiomyoma. And to compare diagnostic performance of radiomic model with radiologists.
78 patients (29 sarcomas, 49 leiomyomas) imaged with pelvic MRI prior to surgery were included in this retrospective study. Certain clinical and MRI features were evaluated for one lesion per patient. Radiological diagnosis was made based on MRI features. A radiomic model using automated texture analysis based on ADC maps was built to predict pathological results. The association between MRI features and pathological results was determined by multivariable logistic regression after controlling for other variables in univariate analyses with P < 0.05. The diagnostic efficacy of radiologists and radiomic model were compared by area under the receiver-operating characteristic curve (AUC), sensitivity, specificity and accuracy.
In univariate analyses, patient's age, menopausal state, intratumor hemorrhage, tumor margin and uterine endometrial cavity were associated with pathological results, P < 0.05. Patient's age, tumor margin and uterine endometrial cavity remained significant in a multivariable model, P < 0.05. Diagnosis efficacy of radiologists based on MRI reached an AUC of 0.752, sensitivity of 58.6%, specificity of 91.8%, and accuracy of 79.5%. The optimal radiomic model reached an AUC of 0.830, sensitivity of 76.0%, average specificity of 73.2%, and accuracy of 73.9%.
Ill-defined tumor margin and interrupted uterine endometrial cavity of older women were predictors of uterine sarcoma. Radiomic analysis was feasible. Optimal radiomic model showed comparable diagnostic efficacy with experienced radiologists.
探讨 MRI 和放射组学特征是否可用于区分子宫肉瘤和非典型平滑肌瘤。并比较放射组学模型与放射科医生的诊断性能。
本回顾性研究纳入了 78 名术前接受盆腔 MRI 检查的患者（29 例肉瘤，49 例平滑肌瘤）。对每位患者的单个病灶评估了某些临床和 MRI 特征。基于 MRI 特征进行放射学诊断。使用基于 ADC 图的自动纹理分析构建放射组学模型，以预测病理结果。在单变量分析中，P 值小于 0.05，将多变量逻辑回归用于控制其他变量后，确定 MRI 特征与病理结果之间的关联。通过接受者操作特征曲线下面积（AUC）、敏感性、特异性和准确性比较放射科医生和放射组学模型的诊断效能。
在单变量分析中，患者年龄、绝经状态、肿瘤内出血、肿瘤边界和子宫子宫内膜腔与病理结果相关，P 值小于 0.05。多变量模型中，患者年龄、肿瘤边界和子宫子宫内膜腔仍然具有统计学意义，P 值小于 0.05。基于 MRI 的放射科医生诊断效能达到 AUC 为 0.752、敏感性为 58.6%、特异性为 91.8%、准确性为 79.5%。最佳放射组学模型达到 AUC 为 0.830、敏感性为 76.0%、平均特异性为 73.2%、准确性为 73.9%。
边界不清的肿瘤和绝经后妇女中断的子宫子宫内膜腔是子宫肉瘤的预测因素。放射组学分析是可行的。最佳放射组学模型与经验丰富的放射科医生具有相当的诊断效能。
