$$\text {M}^2\text {Net}$$: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients

2020 ◽  
pp. 221-231
Author(s):  
Tao Zhou ◽  
Huazhu Fu ◽  
Yu Zhang ◽  
Changqing Zhang ◽  
Xiankai Lu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Brain Tumor ◽  
Survival Time ◽  
Channel Network ◽  
Tumor Patients ◽  
Time Prediction ◽  
Overall Survival Time

scholarly journals Post-Hoc Overall Survival Time Prediction From Brain MRI

2021 ◽  
Author(s):  
Renato Hermoza ◽  
Gabriel Maicas ◽  
Jacinto C. Nascimento ◽  
Gustavo Carneiro
Keyword(s):  
Overall Survival ◽  
Survival Time ◽  
Brain Mri ◽  
Time Prediction ◽  
Overall Survival Time ◽  
Post Hoc

scholarly journals Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Dong Nie ◽  
Junfeng Lu ◽  
Han Zhang ◽  
Ehsan Adeli ◽  
Jun Wang ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Survival Time ◽  
Feature Learning ◽  
Tumor Patients ◽  
Time Prediction ◽  
Deep Feature ◽  
Deep Feature Learning

scholarly journals Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks

2018 ◽  
Vol 13 (5) ◽  
pp. 1333-1351 ◽  
Author(s):  
Luyan Liu ◽  
Han Zhang ◽  
Jinsong Wu ◽  
Zhengda Yu ◽  
Xiaobo Chen ◽  
...  
Keyword(s):  
Overall Survival ◽  
Survival Time ◽  
Large Scale ◽  
High Grade Glioma ◽  
Functional Networks ◽  
High Grade ◽  
Time Prediction ◽  
Overall Survival Time ◽  
Brain Functional Networks

scholarly journals Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients

2021 ◽  
Vol 11 (12) ◽  
pp. 1336
Author(s):  
Lina Chato ◽  
Shahram Latifi
Keyword(s):  
Machine Learning ◽  
Overall Survival ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Survival Time ◽  
Treatment Plan ◽  
Imaging Features ◽  
Prediction System ◽  
Feature Extraction Method ◽  
Overall Survival Time

Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A novel radiomic feature extraction method is proposed and developed on the basis of volumetric and location information of brain tumor subregions extracted from MRI scans. This method is based on calculating the volumetric features from two brain sub-volumes obtained from the whole brain volume in MRI images using brain sectional planes (sagittal, coronal, and horizontal). Many experiments are conducted on the basis of various ML methods and combinations of feature extraction methods to develop the best OST system. In addition, the feature fusions of both radiomic and non-imaging features are examined to improve the accuracy of the prediction system. The best performance was achieved by the neural network and feature fusions.

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