Machine-Learning Classifiers in Discrimination of Lesions Located in the Anterior Skull Base
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Machine-Learning Classifiers in Discrimination of Lesions Located in the Anterior Skull Base
Authors
Keywords
-
Journal
Frontiers in Oncology
Volume 10, Issue -, Pages -
Publisher
Frontiers Media SA
Online
2020-05-28
DOI
10.3389/fonc.2020.00752
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer
- (2020) Paul Blanc-Durand et al. EUROPEAN RADIOLOGY
- Emerging Applications of Artificial Intelligence in Neuro-Oncology
- (2019) Jeffrey D. Rudie et al. RADIOLOGY
- Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages
- (2019) Dong Nie et al. Scientific Reports
- Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas
- (2019) Shuang Wu et al. JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
- Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma
- (2019) Chao Li et al. EUROPEAN RADIOLOGY
- Artificial intelligence in cancer imaging: Clinical challenges and applications
- (2019) Wenya Linda Bi et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Intravascular ultrasound-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions
- (2019) June-Goo Lee et al. ATHEROSCLEROSIS
- LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity
- (2018) Christophe Nioche et al. CANCER RESEARCH
- Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature
- (2018) Yiming Li et al. EUROPEAN RADIOLOGY
- Diagnostic accuracy of MRI texture analysis for grading gliomas
- (2018) Austin Ditmer et al. JOURNAL OF NEURO-ONCOLOGY
- Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features
- (2018) Ping Yin et al. EUROPEAN RADIOLOGY
- Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type
- (2018) Helge C. Kniep et al. RADIOLOGY
- Diagnostic challenges in meningioma
- (2017) Martha Nowosielski et al. NEURO-ONCOLOGY
- Preprocedural Prediction Model for Contrast‐Induced Nephropathy Patients
- (2017) Wen‐jun Yin et al. Journal of the American Heart Association
- Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings
- (2016) Prateek Prasanna et al. EUROPEAN RADIOLOGY
- Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models
- (2016) Philipp Kickingereder et al. RADIOLOGY
- Radiomics: Images Are More than Pictures, They Are Data
- (2016) Robert J. Gillies et al. RADIOLOGY
- Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology
- (2016) Weimiao Wu et al. Frontiers in Oncology
- Erratum: Molecular mechanisms of cell death: central implication of ATP synthase in mitochondrial permeability transition
- (2015) M Bonora et al. ONCOGENE
- Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
- (2015) Chintan Parmar et al. Frontiers in Oncology
- Tumors of the anterior skull base
- (2014) Michael E Ivan et al. Expert Review of Neurotherapeutics
- Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features
- (2014) Samuel H. Hawkins et al. IEEE Access
- Rathke’s cleft cysts: review of natural history and surgical outcomes
- (2013) Seunggu J. Han et al. JOURNAL OF NEURO-ONCOLOGY
- Evaluation of the Sellar and Parasellar Regions
- (2012) Brian M. Chin et al. Magnetic Resonance Imaging Clinics of North America
- Pituitary Magnetic Resonance Imaging for Sellar and Parasellar Masses: Ten-Year Experience in 2598 Patients
- (2011) Pouyan Famini et al. JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM
- SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system
- (2010) Sandra Ortega-Martorell et al. BMC BIOINFORMATICS
- ENDOSCOPIC CRANIAL BASE SURGERY
- (2009) Theodore H. Schwartz et al. NEUROSURGERY
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now