Tsuchihashi Saki, Nagawa Keita, Shimizu Hirokazu, Inoue Kaiji, Okada Yoshitaka, Baba Yasutaka, Hasegawa Kosei, Yasuda Masanori, Kozawa Eito
Department of Radiology, Saitama Medical University Hospital, Saitama, JPN.
Department of Radiology, Japanese Red Cross Ogawa Hospital, Saitama, JPN.
Cureus. 2024 Mar 10;16(3):e55916. doi: 10.7759/cureus.55916. eCollection 2024 Mar.
Aim This study aimed to evaluate the diagnostic feasibility of magnetic resonance imaging (MRI) findings and texture features (TFs) for differentiating uterine endometrial carcinoma from uterine carcinosarcoma. Methods This retrospective study included 102 patients who were histopathologically diagnosed after surgery with uterine endometrial carcinoma (n=68) or uterine carcinosarcoma (n=34) between January 2008 and December 2021. We assessed conventional MRI findings and measurements (cMRFMs) and TFs on T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) map, as well as their combinations, in differentiating between uterine endometrial carcinoma and uterine carcinosarcoma. The least absolute shrinkage and selection operator (LASSO) was used to select three features with the highest absolute value of the LASSO regression coefficient for each model and construct a discriminative model. Binary logistic regression analysis was used to analyze the disease models and conduct receiver operating characteristic analyses on the cMRFMs, T2WI-TFs, ADC-TFs, and their combined model to compare the two diseases. Results A total of four models were constructed from each of the three selected features. The area under the curve (AUC) of the discriminative model using these features was 0.772, 0.878, 0.748, and 0.915 for the cMRFMs, T2WI-TFs, ADC-TFs, and a combined model of cMRFMs and TFs, respectively. The combined model showed a higher AUC than the other models, with a high diagnostic performance (AUC=0.915). Conclusion A combined model using cMRFMs and TFs might be helpful for the differential diagnosis of uterine endometrial carcinoma and uterine carcinosarcoma.
目的 本研究旨在评估磁共振成像（MRI）表现及纹理特征（TFs）在鉴别子宫内膜癌与子宫癌肉瘤中的诊断可行性。方法 本回顾性研究纳入了2008年1月至2021年12月期间102例术后经组织病理学确诊为子宫内膜癌（n = 68）或子宫癌肉瘤（n = 34）的患者。我们在T2加权图像（T2WI）和表观扩散系数（ADC）图上评估了传统MRI表现及测量值（cMRFMs）和TFs，以及它们的组合，以鉴别子宫内膜癌和子宫癌肉瘤。使用最小绝对收缩和选择算子（LASSO）为每个模型选择LASSO回归系数绝对值最高的三个特征，并构建判别模型。采用二元逻辑回归分析疾病模型，并对cMRFMs、T2WI-TFs、ADC-TFs及其联合模型进行受试者工作特征分析，以比较这两种疾病。结果 从所选的三个特征中分别构建了四个模型。使用这些特征的判别模型的曲线下面积（AUC）对于cMRFMs、T2WI-TFs、ADC-TFs以及cMRFMs和TFs的联合模型分别为0.772、0.878、0.748和0.915。联合模型的AUC高于其他模型，具有较高的诊断性能（AUC = 0.915）。结论 使用cMRFMs和TFs的联合模型可能有助于子宫内膜癌和子宫癌肉瘤的鉴别诊断。
