scholarly journals Amide proton transfer imaging of human brain tumors at 3T

2006 ◽  
Vol 56 (3) ◽  
pp. 585-592 ◽  
Author(s):  
Craig K. Jones ◽  
Michael J. Schlosser ◽  
Peter C.M. van Zijl ◽  
Martin G. Pomper ◽  
Xavier Golay ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Proton Transfer ◽  
Human Brain ◽  
Amide Proton ◽  
Human Brain Tumors ◽  
Amide Proton Transfer ◽  
Amide Proton Transfer Imaging

scholarly journals Saturation power dependence of amide proton transfer image contrasts in human brain tumors and strokes at 3 T

2011 ◽  
Vol 66 (4) ◽  
pp. 1033-1041 ◽  
Author(s):  
Xuna Zhao ◽  
Zhibo Wen ◽  
Fanheng Huang ◽  
Shilong Lu ◽  
Xianlong Wang ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Proton Transfer ◽  
Human Brain ◽  
Amide Proton ◽  
Power Dependence ◽  
Human Brain Tumors ◽  
Amide Proton Transfer ◽  
Saturation Power

Scan-rescan reproducibility of parallel transmission based amide proton transfer imaging of brain tumors

2015 ◽  
Vol 42 (5) ◽  
pp. 1346-1353 ◽  
Author(s):  
Osamu Togao ◽  
Akio Hiwatashi ◽  
Jochen Keupp ◽  
Koji Yamashita ◽  
Kazufumi Kikuchi ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Proton Transfer ◽  
Amide Proton ◽  
Parallel Transmission ◽  
Amide Proton Transfer ◽  
Amide Proton Transfer Imaging

scholarly journals Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisabeth Sartoretti ◽  
Thomas Sartoretti ◽  
Michael Wyss ◽  
Carolin Reischauer ◽  
Luuk van Smoorenburg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brain Tumors ◽  
Proton Transfer ◽  
Multilayer Perceptron ◽  
Random Forest Classifier ◽  
Amide Proton ◽  
Low Grade ◽  
Who Grade ◽  
Amide Proton Transfer

AbstractWe sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.

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