Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients

Automated glioma grading on conventional MRI images using deep convolutional neural networks

(2020) Ying Zhuge et al.   MEDICAL PHYSICS

Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis

(2020) Kevin Jang et al.   NEURORADIOLOGY

Conventional magnetic resonance imaging–based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas

(2020) Chendan Jiang et al.   NEURORADIOLOGY

A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization

(2020) Sonal Gore et al.   ACADEMIC RADIOLOGY

Radiomics in neuro-oncology: Basics, workflow, and applications

(2020) Philipp Lohmann et al.   METHODS

Fully Automated Hybrid Approach to Predict the IDH Mutation Status of Gliomas via Deep Learning and Radiomics

(2020) Yoon Seong Choi et al.   NEURO-ONCOLOGY

Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas

(2020) Ziren Kong et al.   Frontiers in Neurology

Automated MRI based pipeline for segmentation and prediction of grade, IDH mutation and 1p19q co-deletion in glioma

(2020) Milan Decuyper et al.   COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas

(2019) Hao Zhou et al.   JOURNAL OF NEURO-ONCOLOGY

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

Radiomics of multi-parametric MRI for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study

(2019) Zhenyu Liu et al.   CLINICAL CANCER RESEARCH

Predicting Response to Cancer Immunotherapy using Non-invasive Radiomic Biomarkers

(2019) S Trebeschi et al.   ANNALS OF ONCOLOGY

Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer

(2019) D Dong et al.   ANNALS OF ONCOLOGY

A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery

(2019) Yan Tan et al.   EUROPEAN RADIOLOGY

Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network

(2019) Kyu Sung Choi et al.   NEURO-ONCOLOGY

Improving survival prediction of high-grade glioma via machine learning techniques based on MRI radiomic, genetic and clinical risk factors

(2019) Yan Tan et al.   EUROPEAN JOURNAL OF RADIOLOGY

Genetic and molecular epidemiology of adult diffuse glioma

(2019) Annette M. Molinaro et al.   Nature Reviews Neurology

Prognostic value of preoperative hematological markers combined with molecular pathology in patients with diffuse gliomas

(2019) Zhen-Yu Zhang et al.   Aging-US

Noninvasive O6 Methylguanine-DNA Methyltransferase Status Prediction in Glioblastoma Multiforme Cancer Using Magnetic Resonance Imaging Radiomics Features: Univariate and Multivariate Radiogenomics Analysis

(2019) Ghasem Hajianfar et al.   World Neurosurgery

Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

(2019) Burak Kocak et al.   EUROPEAN RADIOLOGY

Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas

(2019) W. Han et al.   AMERICAN JOURNAL OF NEURORADIOLOGY

Prediction of lower-grade glioma molecular subtypes using deep learning

(2019) Yutaka Matsui et al.   JOURNAL OF NEURO-ONCOLOGY

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

(2018) P. Chang et al.   AMERICAN JOURNAL OF NEURORADIOLOGY

Machine Learning-Based Radiomics for Molecular Subtyping of Gliomas

(2018) Chia-Feng Lu et al.   CLINICAL CANCER RESEARCH

Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI

(2018) Xi Zhang et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

(2018) Freddie Bray et al.   CA-A CANCER JOURNAL FOR CLINICIANS

Noninvasive Prediction of IDH1 Mutation and ATRX Expression Loss in Low-Grade Gliomas Using Multiparametric MR Radiomic Features

(2018) Yan Ren et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study

(2018) Qiuyu Wang et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Diagnostic accuracy of MRI texture analysis for grading gliomas

(2018) Austin Ditmer et al.   JOURNAL OF NEURO-ONCOLOGY

Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas

(2018) Yuqi Han et al.   JOURNAL OF NEURO-ONCOLOGY

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients

(2018) Jung Youn Kim et al.   NEURO-ONCOLOGY

Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas

(2018) Hideyuki Arita et al.   Scientific Reports

The 2016 World Health Organization classification of tumours of the central nervous system

(2018) Chiara Villa et al.   PRESSE MEDICALE

Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma

(2018) Zhi-Cheng Li et al.   Cancer Medicine

Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning

(2018) Yang Yang et al.   Frontiers in Neuroscience

Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence

(2017) Zeynettin Akkus et al.   JOURNAL OF DIGITAL IMAGING

Radiomics: the bridge between medical imaging and personalized medicine

(2017) Philippe Lambin et al.   Nature Reviews Clinical Oncology

MRI radiomics analysis of molecular alterations in low-grade gliomas

(2017) Ben Shofty et al.   International Journal of Computer Assisted Radiology and Surgery

Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma

(2017) Zeju Li et al.   Scientific Reports

The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary

(2016) David N. Louis et al.   ACTA NEUROPATHOLOGICA

Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma

(2016) Jinhua Yu et al.   EUROPEAN RADIOLOGY

Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors

(2015) Jeanette E. Eckel-Passow et al.   NEW ENGLAND JOURNAL OF MEDICINE

The epidemiology of glioma in adults: a "state of the science" review

(2014) Q. T. Ostrom et al.   NEURO-ONCOLOGY

Personalized care in neuro-oncology coming of age: why we need MGMT and 1p/19q testing for malignant glioma patients in clinical practice

(2012) M. Weller et al.   NEURO-ONCOLOGY

Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group

(2010) Patrick Y. Wen et al.   JOURNAL OF CLINICAL ONCOLOGY