Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma-Reference-Cited by-同舟云学术

Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma

Published:2020-12 Issue: Volume:74 Page:161-170
ISSN:0730-725X
Container-title:Magnetic Resonance Imaging
language:en
Short-container-title:Magnetic Resonance Imaging

Author:

Lu Yiping,Patel Markand,Natarajan Kal,Ughratdar Ismail,Sanghera Paul,Jena Raj,Watts Colin,Sawlani Vijay

Publisher

Elsevier BV

Subject

Radiology, Nuclear Medicine and imaging,Biomedical Engineering,Biophysics

Reference38 articles.

1. Challenges to curing primary brain tumours;Aldape;Nat Rev Clin Oncol,2019

2. NCCN guidelines insights: central nervous system cancers, version 1.2017;Nabors;J Natl Compr Cancer Netw,2017

3. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas;Cancer Genome Atlas Research Network;New Engl J Med,2015

4. Radiotherapy plus concomitant and adjuvant Temozolomide for glioblastoma;Stupp;New Engl J Med,2005

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting response to chemotherapy in brain tumor patients based on MRI features;Clinical Neurology and Neurosurgery;2024-09

2. Magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics for prognosis prediction in glioma patients;Clinical Radiology;2024-08

3. Ten Years of VASARI Glioma Features: Systematic Review and Meta-Analysis of Their Impact and Performance;American Journal of Neuroradiology;2024-06-27

4. Artificial intelligence-based MRI radiomics and radiogenomics in glioma;Cancer Imaging;2024-03-14

5. Machine Learning Meets Cancer;Cancers;2024-03-08