目的:评估基于深度学习的三维(3D)超分辨率扩散加权成像(DWI)影像组学模型预测高强度聚焦超声(HIFU)消融子宫肌瘤预后的可行性和有效性。
方法:这项回顾性研究包括360例接受HIFU治疗的子宫肌瘤患者,包括中心A(训练集:N=240;内部测试集:N=60)和中心B(外部测试集:N=60),并根据术后非灌注体积比分类为预后良好或不良。在传统高分辨率DWI(HR-DWI)的基础上,采用深度迁移学习方法构建超分辨率DWI(SR-DWI),从两种图像类型的手动分割的感兴趣区域中提取1198个影像组学特征。在数据预处理和特征选择之后,使用支持向量机(SVM)构建HR-DWI和SR-DWI的影像组学模型,随机森林(RF),和光梯度提升机(LightGBM)算法,使用曲线下面积(AUC)和决策曲线评估性能。
结果:与放射科专家相比,所有DWI影像组学模型在预测HIFU消融子宫肌瘤预后方面均表现出优异的AUC(AUC:0.706,95%CI:0.647-0.748)。当使用不同的机器学习算法时,支持向量机的HR-DWI模型的AUC值为0.805(95%CI:0.679-0.931),0.797(95%CI:0.672-0.921)射频,和0.770(95%CI:0.631-0.908)与LightGBM。同时,在所有算法中,SR-DWI模型优于HR-DWI模型(P<0.05),SVM的AUC值为0.868(95%CI:0.775-0.960),0.824(95%CI:0.715-0.934)与RF,和0.821(95%CI:0.709-0.933)与LightGBM。而决策曲线分析进一步证实了该模型良好的临床应用价值。
结论:基于深度学习的3DSR-DWI影像组学模型在预测HIFU消融子宫肌瘤预后方面具有良好的可行性和有效性。优于HR-DWI模型和放射科专家的评估。
OBJECTIVE: To assess the feasibility and efficacy of a deep learning-based three-dimensional (3D) super-resolution diffusion-weighted imaging (DWI) radiomics model in predicting the prognosis of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids.
METHODS: This retrospective study included 360 patients with uterine fibroids who received HIFU treatment, including Center A (training set: N = 240; internal testing set: N = 60) and Center B (external testing set: N = 60) and were classified as having a favorable or unfavorable prognosis based on the postoperative non-perfusion volume ratio. A deep transfer learning approach was used to construct super-resolution DWI (SR-DWI) based on conventional high-resolution DWI (HR-DWI), and 1198 radiomics features were extracted from manually segmented regions of interest in both image types. Following data preprocessing and feature selection, radiomics models were constructed for HR-DWI and SR-DWI using Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM) algorithms, with performance evaluated using area under the curve (AUC) and decision curves.
RESULTS: All DWI radiomics models demonstrated superior AUC in predicting HIFU ablated uterine fibroids prognosis compared to expert radiologists (AUC: 0.706, 95% CI: 0.647-0.748). When utilizing different machine learning algorithms, the HR-DWI model achieved AUC values of 0.805 (95% CI: 0.679-0.931) with SVM, 0.797 (95% CI: 0.672-0.921) with RF, and 0.770 (95% CI: 0.631-0.908) with LightGBM. Meanwhile, the SR-DWI model outperformed the HR-DWI model (P < 0.05) across all algorithms, with AUC values of 0.868 (95% CI: 0.775-0.960) with SVM, 0.824 (95% CI: 0.715-0.934) with RF, and 0.821 (95% CI: 0.709-0.933) with LightGBM. And decision curve analysis further confirmed the good clinical value of the models.
CONCLUSIONS: Deep learning-based 3D SR-DWI radiomics model demonstrated favorable feasibility and effectiveness in predicting the prognosis of HIFU ablated uterine fibroids, which was superior to HR-DWI model and assessment by expert radiologists.