Academic Journal

Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers

التفاصيل البيبلوغرافية
العنوان: Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers
المؤلفون: Dixiang Song, Yixuan Zhai, Xiaogang Tao, Chao Zhao, Minkai Wang, Xinting Wei
المصدر: Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
بيانات النشر: Nature Portfolio, 2021.
سنة النشر: 2021
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: Abstract This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply groups according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomics classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selecting methods and classifiers was calculated separately to compare the performance of the models. Obtain and compare the best combination results with the performance of differentiation through visual observation in clinical diagnosis. 191 patients were included in this study. 3918 stable features were extracted from each patient. Least absolute shrinkage and selection operator (LASSO) and logistic regression model was selected as the optimal combinations after comparing the AUC calculated by models, which predicted the blood supply of vestibular schwannoma by K-Fold cross-validation method with a mean AUC = 0.88 and F1-score = 0.83. Radiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-021-97865-5
URL الوصول: https://doaj.org/article/b22de609d9784200bdc8e7f85ef91a87
رقم الانضمام: edsdoj.b22de609d9784200bdc8e7f85ef91a87
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20452322
DOI:10.1038/s41598-021-97865-5