Wang Jin Jun, Zhang Xiao Hong, Guo Xing Hua, Ying Yang, Wang Xiang, Luan Zhong Hua, Lv Wei Qin, Wang Peng Fei
Department of Radiology, Yuncheng Central Hospital of Shanxi Province, Yuncheng Hospital Affiliated to Shanxi Medical University, No. 3690 He Dong East Road, YanHu Distract, Shanxi, P.R. China.
Curr Med Imaging. 2024 Mar 19. doi: 10.2174/0115734056266366231219111246.
Objective: To explore the application value of a combined model based on multi-parameter MRI radiomics and clinical features in preoperative prediction of lymphatic vascular space invasion (LVSI) in endometrial carcinoma (EC).
This retrospective study collected the clinicopathological and imaging data of 218 patients with EC in Yuncheng Central Hospital from March 2018 to May 2022. The patients were randomly divided into training group (n=152) and validation group (n= 66) according to the ratio of 7: 3. Based on the ADC, CE-sag, CE-tra, DWI, T2WI-sag-fs, T2WI-tra sequence images of each patient, the region of interest was manually segmented and the features were extracted. The four-step dimensionality reduction method based on max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and radiomics model construction. Independent predictors of clinicopathological features were screened by multivariate logistic regression analysis. The imaging model based on ADC, CE-sag, CE-tra, DWI, T2WI-sag-fs, T2WI-tra single sequence and combined sequence and the fusion model with clinicopathological features were constructed, and the nomogram was made. ROC curve, correction curve and decision analysis curve were used to evaluate the efficacy and clinical benefits of the nomogram.
There was no significant difference in general clinical data between the training and validation groups (P > 0.05). After screening the extracted features, 16 radiomics features were obtained, which were all related to LVSI in EC patients (P < 0.05). The area under the ROC curve (AUC) of the six independent sequence radiomics models in the training group was 0.807, 0.794, 0.826, 0.794, 0.828, 0.824, respectively. The AUC corresponding to the radiomics model constructed by the combined sequence was 0.884, and the diagnostic efficiency was the best, which was verified in the validation group. The AUC of the nomogram constructed by the combined radiomics model and age maximum tumor diameter(MTD), lymph node enlargement (LNE) in the training group and the validation group were 0.914 and 0.912, respectively. The correction curve shows that the nomogram has good correction performance. The decision curve suggests that taking radiomics nomogram to predict LVSI net benefit when the risk threshold is > 10% is better than considering all patients as LVSI+ or LVSI-.
The combined model based on multi-parametric MRI radiomics features and clinical features has good predictive value for LVSI status in EC patients.
.目的：探讨基于多参数MRI影像组学和临床特征的联合模型在子宫内膜癌（EC）术前预测淋巴血管间隙浸润（LVSI）中的应用价值。
本回顾性研究收集了2018年3月至2022年5月运城市中心医院218例EC患者的临床病理及影像资料。根据7:3的比例将患者随机分为训练组（n = 152）和验证组（n = 66）。基于每位患者的ADC、CE-sag、CE-tra、DWI、T2WI-sag-fs、T2WI-tra序列图像，手动分割感兴趣区域并提取特征。采用基于最大相关最小冗余（MRMR）和最小绝对收缩与选择算子（LASSO）回归的四步降维方法进行特征选择和影像组学模型构建。通过多因素logistic回归分析筛选临床病理特征的独立预测因素。构建基于ADC、CE-sag、CE-tra、DWI、T2WI-sag-fs、T2WI-tra单序列和联合序列的影像模型以及与临床病理特征的融合模型，并制作列线图。采用ROC曲线、校正曲线和决策分析曲线评估列线图的效能和临床效益。
训练组和验证组的一般临床资料无显著差异（P > 0.05）。对提取的特征进行筛选后，获得16个影像组学特征，均与EC患者的LVSI相关（P < 0.05）。训练组中六个独立序列影像组学模型的ROC曲线下面积（AUC）分别为0.807、0.794、0.826、0.794、0.828、0.824。联合序列构建的影像组学模型对应的AUC为0.884，诊断效率最佳，在验证组中得到验证。训练组和验证组中由联合影像组学模型与年龄、最大肿瘤直径（MTD）、淋巴结肿大（LNE）构建的列线图的AUC分别为0.914和0.912。校正曲线显示列线图具有良好的校正性能。决策曲线表明，当风险阈值> 10%时，采用影像组学列线图预测LVSI的净效益优于将所有患者视为LVSI阳性或阴性。
基于多参数MRI影像组学特征和临床特征的联合模型对EC患者的LVSI状态具有良好的预测价值。
