Prediction of Clinical Outcome for High-Intensity Focused Ultrasound Ablation of Uterine Leiomyomas Using Multiparametric MRI Radiomics-Based Machine Leaning Model.

Yineng Zheng,Renqiang Yu,Jiahui Wu,Liping Chen,Fajin Lv,Mengqi Liu

doi:10.3389/fonc.2021.618604

Abstract

ObjectivesThis study sought to develop a multiparametric MRI radiomics-based machine learning model for the preoperative prediction of clinical success for high-intensity-focused ultrasound (HIFU) ablation of uterine leiomyomas.MethodsOne hundred and thirty patients who received HIFU ablation therapy for uterine leiomyomas were enrolled in this retrospective study. Radiomics features were extracted from T2-weighted (T2WI) image and ADC map derived from diffusion-weighted imaging (DWI). Three feature selection algorithms including least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF algorithm were used to select radiomics features, respectively, which were fed into four machine learning classifiers including k-nearest neighbors (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) for the construction of outcome prediction models before HIFU treatment. The performance, predication ability, and clinical usefulness of these models were verified and evaluated using receiver operating characteristics (ROC), calibration, and decision curve analyses.ResultsThe radiomics analysis provided an effective preoperative prediction for HIFU ablation of uterine leiomyomas. Using SVM with ReliefF algorithm, the multiparametric MRI radiomics model showed the favorable performance with average accuracy of 0.849, sensitivity of 0.814, specificity of 0.896, positive predictive value (PPV) of 0.903, negative predictive value (NPV) of 0.823, and the area under the ROC curve (AUC) of 0.887 (95% CI = 0.848–0.939) in fivefold cross-validation, followed by RF with ReliefF. Calibration and decision curve analyses confirmed the potential of model in predication ability and clinical usefulness.ConclusionsThe radiomics-based machine learning model can predict preoperatively HIFU ablation response for the patients with uterine leiomyomas and contribute to determining individual treatment strategies.

Highlights

Uterine leiomyomas are benign smooth-muscle neoplasm of the uterus in women of reproductive age, with a high morbidity of more than 70%, and seriously makes the quality of life of patients worse or even affects fertility [1]
Three feature selection algorithms including least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF algorithm were used to select radiomics features, respectively, which were fed into four machine learning classifiers including k-nearest neighbors (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) for the construction of outcome prediction models before high-intensity focused ultrasound (HIFU) treatment
Using SVM with ReliefF algorithm, the multiparametric magnetic resonance imaging (MRI) radiomics model showed the favorable performance with average accuracy of 0.849, sensitivity of 0.814, specificity of 0.896, positive predictive value (PPV) of 0.903, negative predictive value (NPV) of 0.823, and the area under the receiver operating characteristics (ROC) curve (AUC) of 0.887 in fivefold cross-validation, followed by RF with ReliefF

Summary

Introduction

Uterine leiomyomas are benign smooth-muscle neoplasm of the uterus in women of reproductive age, with a high morbidity of more than 70%, and seriously makes the quality of life of patients worse or even affects fertility [1]. When uterine leiomyomas are symptomatic and pharmacotherapy fails [2], the choice between hysteromyomectomy and hysterectomy treatment depends on female fertility needs [3, 4], which is a common factor for surgical removal of the uterus. Preoperative evaluation is a crucial factor for ensuring a high ablation rate of leiomyoma tissue as well as the success rate of HIFU treatment, so preoperative outcome prediction would better guide clinical decision making for therapeutic strategy [8, 9]. Magnetic resonance imaging (MRI) is commonly used for preoperative evaluation and response assessment before and after HIFU ablation treatment, respectively [10]. Several studies have investigated the relationship between the degree of signal intensity on T2weighted (T2WI) images and treatment outcome of HIFU ablation for uterine leiomyomas, but only showed a limited predictive power [11, 12]

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