Abstract：
BACKGROUND: Endometrial carcinoma (EC) is one of the most common gynecological malignancies with an increasing incidence, and an accurate preoperative diagnosis of deep myometrial invasion (DMI) is crucial for personalized treatment.
OBJECTIVE: To determine the predictive value of a magnetic resonance imaging (MRI)-based radiomics nomogram for the presence of DMI in the International Federation of Gynecology and Obstetrics (FIGO) stage I EC.
METHODS: We retrospectively collected 163 patients with pathologically confirmed stage I EC from two centers and divided all samples into a training group (Center 1) and a validation group (Center 2). Clinical and routine imaging indicators were analyzed by logistical regression to construct a conventional diagnostic model (M1). Radiomics features extracted from the axial T2-weighted and axial contrast-enhanced T1-weighted (CE-T1W) images were treated with the intraclass correlation coefficient, Mann-Whitney U test, least absolute shrinkage and selection operator, and logistic regression analysis with Akaike information criterion to build a combined radiomics signature (M2). A nomogram (M3) was constructed by M1 and M2. Calibration and decision curves were drawn to evaluate the nomogram in the training and validation cohorts. The diagnostic performance of each indicator and model was evaluated by the area under the receiver operating characteristic curve (AUC).
RESULTS: The four most significant radiomics features were finally selected from the CE-T1W MRI. For the diagnosis of DMI, the AUCT /AUCV of M1 was 0.798/0.738, the AUCT /AUCV of M2 was 0.880/0.852, and the AUCT /AUCV of M3 was 0.936/0.871 in the training and validation groups, respectively. The calibration curves showed that M3 was in good agreement with the ideal values. The decision curve analysis suggested potential clinical application values of the nomogram.
CONCLUSIONS: A nomogram based on MRI radiomics and clinical imaging indicators can improve the diagnosis of DMI in patients with FIGO stage I EC.
OBJECTIVE: To determine the predictive value of a magnetic resonance imaging (MRI)-based radiomics nomogram for the presence of DMI in the International Federation of Gynecology and Obstetrics (FIGO) stage I EC.
METHODS: We retrospectively collected 163 patients with pathologically confirmed stage I EC from two centers and divided all samples into a training group (Center 1) and a validation group (Center 2). Clinical and routine imaging indicators were analyzed by logistical regression to construct a conventional diagnostic model (M1). Radiomics features extracted from the axial T2-weighted and axial contrast-enhanced T1-weighted (CE-T1W) images were treated with the intraclass correlation coefficient, Mann-Whitney U test, least absolute shrinkage and selection operator, and logistic regression analysis with Akaike information criterion to build a combined radiomics signature (M2). A nomogram (M3) was constructed by M1 and M2. Calibration and decision curves were drawn to evaluate the nomogram in the training and validation cohorts. The diagnostic performance of each indicator and model was evaluated by the area under the receiver operating characteristic curve (AUC).
RESULTS: The four most significant radiomics features were finally selected from the CE-T1W MRI. For the diagnosis of DMI, the AUCT /AUCV of M1 was 0.798/0.738, the AUCT /AUCV of M2 was 0.880/0.852, and the AUCT /AUCV of M3 was 0.936/0.871 in the training and validation groups, respectively. The calibration curves showed that M3 was in good agreement with the ideal values. The decision curve analysis suggested potential clinical application values of the nomogram.
CONCLUSIONS: A nomogram based on MRI radiomics and clinical imaging indicators can improve the diagnosis of DMI in patients with FIGO stage I EC.
摘要：
背景：子宫内膜癌（EC）是最常见的妇科恶性肿瘤之一，发病率越来越高，术前准确诊断深肌层侵犯(depometrialinvention,简称dmi)是个性化治疗的关键.
目的：确定基于磁共振成像（MRI）的放射组学列线图对国际妇产科联合会（FIGO）I期EC中的MI存在的预测价值。
方法：我们回顾性地从两个中心收集了163例经病理证实的I期EC患者，并将所有样本分为训练组（中心1）和验证组（中心2）。采用logistic回归分析临床和常规影像学指标,构建常规诊断模型(M1)。从轴向T2加权和轴向对比增强T1加权（CE-T1W）图像中提取的影像组学特征采用组内相关系数处理，Mann-WhitneyU测试,最小绝对收缩和选择运算符，并与Akaike信息标准进行逻辑回归分析，以构建联合的影像组学签名（M2）。列线图(M3)由M1和M2构成。绘制校准和决策曲线以评估训练和验证队列中的列线图。通过受试者工作特征曲线下面积（AUC）评估每个指标和模型的诊断性能。
结果：最终从CE-T1WMRI中选择了四个最重要的影像组学特征。对于MI的诊断，训练组和验证组M1的AUCT/AUCV为0.798/0.738,M2的AUCT/AUCV为0.880/0.852,M3的AUCT/AUCV为0.936/0.871,分别。校准曲线表明M3与理想值吻合良好。决策曲线分析提示了列线图的潜在临床应用价值。
结论：基于MRI影像组学和临床影像学指标的列线图可以改善FIGOI期EC患者的MI诊断。
目的：确定基于磁共振成像（MRI）的放射组学列线图对国际妇产科联合会（FIGO）I期EC中的MI存在的预测价值。
方法：我们回顾性地从两个中心收集了163例经病理证实的I期EC患者，并将所有样本分为训练组（中心1）和验证组（中心2）。采用logistic回归分析临床和常规影像学指标,构建常规诊断模型(M1)。从轴向T2加权和轴向对比增强T1加权（CE-T1W）图像中提取的影像组学特征采用组内相关系数处理，Mann-WhitneyU测试,最小绝对收缩和选择运算符，并与Akaike信息标准进行逻辑回归分析，以构建联合的影像组学签名（M2）。列线图(M3)由M1和M2构成。绘制校准和决策曲线以评估训练和验证队列中的列线图。通过受试者工作特征曲线下面积（AUC）评估每个指标和模型的诊断性能。
结果：最终从CE-T1WMRI中选择了四个最重要的影像组学特征。对于MI的诊断，训练组和验证组M1的AUCT/AUCV为0.798/0.738,M2的AUCT/AUCV为0.880/0.852,M3的AUCT/AUCV为0.936/0.871,分别。校准曲线表明M3与理想值吻合良好。决策曲线分析提示了列线图的潜在临床应用价值。
结论：基于MRI影像组学和临床影像学指标的列线图可以改善FIGOI期EC患者的MI诊断。
