Development of MRI-based radiomics predictive model for classifying endometrial lesions

作者：Jiaqi Liu, Shiyun Li, Huashan Lin, Peiei Pang, Puying Luo, Bing Fan, Juhong Yu · 发表于：Scientific Reports · 年份：2023 · DOI：10.1038/s41598-023-28819-2 · 被引用次数：11 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Endometrial and Cervical Cancer Treatments、MRI in cancer diagnosis

An unbiased and accurate diagnosis of benign and malignant endometrial lesions is essential for the gynecologist, as each type might require distinct treatment. Radiomics is a quantitative method that could facilitate deep mining of information and quantification of the heterogeneity in images, thereby aiding clinicians in proper lesion diagnosis. The aim of this study is to develop an appropriate predictive model for the classification of benign and malignant endometrial lesions, and evaluate potential clinical applicability of the model. 139 patients with pathologically-confirmed endometrial lesions from January 2018 to July 2020 in two independent centers (center A and B) were finally analyzed. Center A was used for training set, while center B was used for test set. The lesions were manually drawn on the largest slice based on the lesion area by two radiologists. After feature extraction and feature selection, the possible associations between radiomics features and clinical parameters were assessed by Uni- and multi- variable logistic regression. The receiver operator characteristic (ROC) curve and DeLong validation were employed to evaluate the possible predictive performance of the models. Decision curve analysis (DCA) was used to evaluate the net benefit of the radiomics nomogram. A radiomics prediction model was established from the 15 selected features, and were found to be relatively high discriminative on the basis of the area under the ROC curve (AUC) for both th...