Cargando…
Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors
PURPOSE: To assess the performance of deep neural network (DNN) and machine learning based radiomics on 3D computed tomography (CT) and clinical characteristics to predict benign or malignant sacral tumors. MATERIALS AND METHODS: This single-center retrospective analysis included 459 patients with p...
Autores principales: | Yin, Ping, Mao, Ning, Chen, Hao, Sun, Chao, Wang, Sicong, Liu, Xia, Hong, Nan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596901/ https://www.ncbi.nlm.nih.gov/pubmed/33178593 http://dx.doi.org/10.3389/fonc.2020.564725 |
Ejemplares similares
-
Radiomics Models for the Preoperative Prediction of Pelvic and Sacral Tumor Types: A Single-Center Retrospective Study of 795 Cases
por: Yin, Ping, et al.
Publicado: (2021) -
Clinical-Deep Neural Network and Clinical-Radiomics Nomograms for Predicting the Intraoperative Massive Blood Loss of Pelvic and Sacral Tumors
por: Yin, Ping, et al.
Publicado: (2021) -
The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland
por: Xu, Yuyun, et al.
Publicado: (2021) -
Radiomics Nomogram for Identifying Sub-1 cm Benign and Malignant Thyroid Lesions
por: Wu, Xinxin, et al.
Publicado: (2021) -
Preoperative Prediction of Meningioma Consistency via Machine Learning-Based Radiomics
por: Zhai, Yixuan, et al.
Publicado: (2021)