Wen Bing, Li Chengwei, Cai Qiuyi, Shen Dan, Bu Xinyi, Zhou Fuqiang
Department of Radiology, Yiyang Central Hospital, Yiyang, China.
Department of Radiology, The Third People's Hospital of Chengdu, Chengdu, China.
Front Physiol. 2024 Dec 20;15:1507986. doi: 10.3389/fphys.2024.1507986. eCollection 2024.
To evaluate the effectiveness of an MRI radiomics stacking ensemble learning model, which combines T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) with deep learning-based automatic segmentation, for preoperative prediction of the prognosis of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids.
This retrospective study collected data from 360 patients with uterine fibroids who underwent HIFU treatment. The dataset was sourced from Center A (training set: N = 240; internal test set: N = 60) and Center B (external test set: N = 60). Patients were categorized into favorable and unfavorable prognosis groups based on the post-treatment non-perfused volume ratio. Automated segmentation of uterine fibroids was performed using a V-net deep learning models. Radiomics features were extracted from T2WI and CE-T1WI, followed by data preprocessing including normalization and scaling. Feature selection was performed using -test, Pearson correlation, and LASSO to identify the most predictive features for preoperative prognosis Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP) were employed as base learners to construct base predictive models. These models were integrated into a stacking ensemble model, with Logistic Regression serving as the meta-learner to combine the outputs of the base models. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC).
Among the base models developed using T2WI and CE-T1WI features, the MLP model exhibited superior performance, achieving an AUC of 0.858 (95% CI: 0.756-0.959) in the internal test set and 0.828 (95% CI: 0.726-0.930) in the external test set. It was followed by the SVM, LightGBM, and RF, which obtained AUC values of 0.841 (95% CI: 0.737-0.946), 0.823 (95% CI: 0.711-0.934), and 0.750 (95% CI: 0.619-0.881), respectively. The stacking ensemble learning model, which integrated these five algorithms, demonstrated a notable enhancement in performance, with an AUC of 0.897 (95% CI: 0.818-0.977) in the internal test set and 0.854 (95% CI: 0.759-0.948) in the external test set.
The DL based automatic segmentation MRI radiomics stacking ensemble learning model demonstrated high accuracy in predicting the prognosis of HIFU ablation of uterine fibroids.
评估一种磁共振成像（MRI）影像组学堆叠集成学习模型的有效性，该模型将T2加权成像（T2WI）和对比增强T1加权成像（CE-T1WI）与基于深度学习的自动分割相结合，用于术前预测高强度聚焦超声（HIFU）消融子宫肌瘤的预后。
这项回顾性研究收集了360例接受HIFU治疗的子宫肌瘤患者的数据。数据集来自A中心（训练集：N = 240；内部测试集：N = 60）和B中心（外部测试集：N = 60）。根据治疗后无灌注体积比将患者分为预后良好和预后不良组。使用V-net深度学习模型对子宫肌瘤进行自动分割。从T2WI和CE-T1WI中提取影像组学特征，然后进行包括归一化和缩放在内的数据预处理。使用t检验、Pearson相关性和LASSO进行特征选择，以识别术前预后的最具预测性的特征。支持向量机（SVM）、随机森林（RF）、轻梯度提升机（LightGBM）和多层感知器（MLP）被用作基础学习器来构建基础预测模型。这些模型被集成到一个堆叠集成模型中，以逻辑回归作为元学习器来组合基础模型的输出。使用受试者操作特征曲线（AUC）下的面积评估模型的性能。
在使用T2WI和CE-T1WI特征开发的基础模型中，MLP模型表现出卓越的性能，在内部测试集中AUC为0.858（95%CI：0.756 - 0.959），在外部测试集中为0.828（95%CI：0.726 - 0.930）。其次是SVM、LightGBM和RF，它们的AUC值分别为0.841（95%CI：0.737 - 0.946）、0.823（95%CI：0.711 - 0.934）和0.750（95%CI：0.619 - 0.881）。集成这五种算法的堆叠集成学习模型在性能上有显著提升，在内部测试集中AUC为0.897（95%CI：0.818 - 0.977），在外部测试集中为0.854（95%CI：0.759 - 0.948）。
基于深度学习的自动分割MRI影像组学堆叠集成学习模型在预测HIFU消融子宫肌瘤的预后方面显示出高准确性。
