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

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.618604

DOI=10.3389/fonc.2021.618604

ISSN=2234-943X

ABSTRACT=Objectives: This study sought to develop a multi-parametric MRI radiomics-based machine learning model for the preoperative prediction of clinical success for high-intensity focused ultrasound (HIFU) ablation of uterine leiomyomas.
Methods: One hundred and thirty patients receiving 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 neighbor (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.
Results: The radiomics analysis provided an effective preoperative prediction for HIFU ablation of uterine leiomyomas. Using SVM with ReliefF algorithm, the multi-parametric 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 an ROC curve (AUC) of 0.887 (95% CI = 0.848–0.939) in 5-fold cross validation, followed by RF with ReliefF. Calibration and decision curve analyses confirmed the potential of model in predication ability and clinical usefulness.
Conclusions: The radiomics based machine learning model can predict preoperatively HIFU ablation response for patients with uterine leiomyomas and contribute to determining individual treatment strategies.