Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters

作者全名："Zhou, Ye; Zhang, Jinwei; Li, Chenghai; Chen, Jinyun; Lv, Fajin; Deng, Yongbin; Chen, Siyao; Du, Yuling; Li, Faqi"

作者地址："[Zhou, Ye; Zhang, Jinwei; Li, Chenghai; Chen, Jinyun; Chen, Siyao; Du, Yuling; Li, Faqi] Chongqing Med Univ, Coll Biomed Engn, State Key Lab Ultrasound Med & Engn, Chongqing 400016, Peoples R China; [Zhou, Ye; Zhang, Jinwei; Li, Chenghai; Chen, Jinyun; Chen, Siyao; Du, Yuling; Li, Faqi] Chongqing Med Univ, Chongqing Key Lab Biomed Engn, Chongqing 400016, Peoples R China; [Lv, Fajin] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing 400016, Peoples R China; [Deng, Yongbin] Chongqing Haifu Hosp, Chongqing 401121, Peoples R China"

通信作者："Li, CH; Li, FQ (通讯作者)，Chongqing Med Univ, Coll Biomed Engn, State Key Lab Ultrasound Med & Engn, Chongqing 400016, Peoples R China.; Li, CH; Li, FQ (通讯作者)，Chongqing Med Univ, Chongqing Key Lab Biomed Engn, Chongqing 400016, Peoples R China."

来源：BIOMEDICAL ENGINEERING ONLINE

ESI学科分类：MOLECULAR BIOLOGY & GENETICS

WOS号：WOS:001123982400001

JCR分区：Q3

影响因子：2.9

年份：2023

卷号：22

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Radiomics; Magnetic resonance imaging; Uterine fibroid; Ultrasound guided high-intensity focused ultrasound; Prediction; Machine learning

摘要："BackgroundPrediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially benefit from ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, as it reduces the risk of treatment failure. The purpose of this study is to construct an optimal model for predicting NPVR based on T2-weighted magnetic resonance imaging (T2MRI) radiomics features combined with clinical parameters by machine learning.Materials and methodsThis retrospective study was conducted among 223 patients diagnosed with uterine fibroids from two centers. The patients from one center were allocated to a training cohort (n = 122) and an internal test cohort (n = 46), and the data from the other center (n = 55) was used as an external test cohort. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection in the training cohort. The support vector machine (SVM) was adopted to construct a radiomics model, a clinical model, and a radiomics-clinical model for NPVR prediction, respectively. The area under the curve (AUC) and the decision curve analysis (DCA) were performed to evaluate the predictive validity and the clinical usefulness of the model, respectively.ResultsA total of 851 radiomic features were extracted from T2MRI, of which seven radiomics features were screened for NPVR prediction-related radiomics features. The radiomics-clinical model combining radiomics features and clinical parameters showed the best predictive performance in both the internal (AUC = 0.824, 95% CI 0.693-0.954) and external (AUC = 0.773, 95% CI 0.647-0.902) test cohorts, and the DCA also suggested the radiomics-clinical model had the highest net benefit.ConclusionsThe radiomics-clinical model could be applied to the NPVR prediction of patients with uterine fibroids treated by HIFU to provide an objective and effective method for selecting potential patients who would benefit from the treatment mostly."

基金机构：National Natural Science Foundation of China

基金资助正文：The authors are grateful to all study participants.