Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction

Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

(2019) Veronika Cheplygina et al.   MEDICAL IMAGE ANALYSIS

EP-1362: Random forest analysis to predict Disease-Free Survival using FDG-PET and CT in Lung Cancer

(2018) M. Kirienko et al.   RADIOTHERAPY AND ONCOLOGY

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

(2017) Justin Guinney et al.   LANCET ONCOLOGY

A clinical-molecular prognostic model to predict survival in patients with post polycythemia vera and post essential thrombocythemia myelofibrosis

(2017) F Passamonti et al.   LEUKEMIA

Brain tumor segmentation with Deep Neural Networks

(2017) Mohammad Havaei et al.   MEDICAL IMAGE ANALYSIS

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

(2017) Spyridon Bakas et al.   Scientific Data

A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

(2017) Jiangwei Lao et al.   Scientific Reports

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

(2015) Bjoern H. Menze et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma

(2015) Dalu Yang et al.   MEDICAL PHYSICS

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

(2015) Luke Macyszyn et al.   NEURO-ONCOLOGY

A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

(2015) M Vallières et al.   PHYSICS IN MEDICINE AND BIOLOGY

Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

(2015) Haruka Itakura et al.   Science Translational Medicine

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma

(2015) Leland S. Hu et al.   PLoS One

GBM's multifaceted landscape: highlighting regional and microenvironmental heterogeneity

(2014) A. Vartanian et al.   NEURO-ONCOLOGY

Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor

(2014) Rajan Jain et al.   RADIOLOGY

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

(2014) Hugo J. W. L. Aerts et al.   Nature Communications

Glioblastoma survival in the United States improved after Food and Drug Administration approval of bevacizumab: A population-based analysis

(2013) Derek R. Johnson et al.   CANCER

Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors

(2013) Atiq Islam et al.   IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set

(2013) David A. Gutman et al.   RADIOLOGY

Recent advances in the molecular understanding of glioblastoma

(2012) Fonnet E. Bleeker et al.   JOURNAL OF NEURO-ONCOLOGY

Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers

(2012) Rajan Jain et al.   RADIOLOGY

The Use of Magnetic Resonance Imaging to Noninvasively Detect Genetic Signatures in Oligodendroglioma

(2008) R. Brown et al.   CLINICAL CANCER RESEARCH

Glioma Grading by Using Histogram Analysis of Blood Volume Heterogeneity from MR-derived Cerebral Blood Volume Maps

(2008) Kyrre E. Emblem et al.   RADIOLOGY