An interpretable MRI-based radiomics model predicting the prognosis of high-intensity focused ultrasound ablation of uterine fibroids

作者全名："Li, Chengwei; He, Zhimin; Lv, Fajin; Liu, Yang; Hu, Yan; Zhang, Jian; Liu, Hui; Ma, Si; Xiao, Zhibo"

作者地址："[Li, Chengwei; He, Zhimin; Lv, Fajin; Liu, Yang; Hu, Yan; Zhang, Jian; Liu, Hui; Ma, Si; Xiao, Zhibo] Chongqing Med Univ, Coll Biomed Engn, State Key Lab Ultrasound Med & Engn, Chongqing, Peoples R China; [Lv, Fajin; Xiao, Zhibo] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing 400016, Peoples R China"

通信作者："Xiao, ZB (通讯作者)，Chongqing Med Univ, Coll Biomed Engn, State Key Lab Ultrasound Med & Engn, Chongqing, Peoples R China.; Xiao, ZB (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing 400016, Peoples R China."

来源：INSIGHTS INTO IMAGING

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001033632900001

JCR分区：Q1

影响因子：4.1

年份：2023

卷号：14

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Machine learning; Radiomics; HIFU; Uterine fibroid; Magnetic resonance imaging

摘要："Background Accurate preoperative assessment of the efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine fibroids is essential for good treatment results. The aim of this study was to develop robust radiomics models for predicting the prognosis of HIFU-treated uterine fibroids and to explain the internal predictive process of the model using Shapley additive explanations (SHAP). Methods This retrospective study included 300 patients with uterine fibroids who received HIFU and were classified as having a favorable or unfavorable prognosis based on the postoperative nonperfusion volume ratio. Patients were divided into a training set (N = 240) and a test set (N = 60). The 1295 radiomics features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) scans. After data preprocessing and feature filtering, radiomics models were constructed by extreme gradient boosting and light gradient boosting machine (LightGBM), and the optimal performance was obtained by Bayesian optimization. Finally, the SHAP approach was used to explain the internal prediction process. Results The models constructed using LightGBM had the best performance, and the AUCs of the T2WI and CE-T1WI models were 87.2 (95% CI = 87.1-87.5) and 84.8 (95% CI = 84.6-85.7), respectively. The use of SHAP technology can help physicians understand the impact of radiomic features on the predicted outcomes of the model from a global and individual perspective. Conclusion Multiparametric radiomic models have shown their robustness in predicting HIFU prognosis. Radiomic features can be a potential source of biomarkers to support preoperative assessment of HIFU treatment and improve the understanding of uterine fibroid heterogeneity."

基金机构：Chongqing Medical Scientific Research Project (Joint Project of Chongqing Health Commission and Science and Technology Bureau) [2021MSXM102]

基金资助正文："This work was supported by the Chongqing Medical Scientific Research Project (Joint Project of Chongqing Health Commission and Science and Technology Bureau), Contract grant number: 2021MSXM102."