Application of the amide proton transfer-weighted imaging and diffusion kurtosis imaging in the study of cervical cancer

2020 ◽  
Vol 30 (10) ◽  
pp. 5758-5767 ◽  
Author(s):  
Nan Meng ◽  
Xuejia Wang ◽  
Jing Sun ◽  
Dongming Han ◽  
Xiaoyue Ma ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Proton Transfer ◽  
Amide Proton ◽  
Diffusion Kurtosis Imaging ◽  
Amide Proton Transfer ◽  
Diffusion Kurtosis ◽  
And Diffusion

Three‐dimensional turbo‐spin‐echo amide proton transfer‐weighted mri for cervical cancer: A preliminary study

2019 ◽  
Vol 50 (4) ◽  
pp. 1318-1325 ◽  
Author(s):  
Yong‐Lan He ◽  
Yuan Li ◽  
Cheng‐Yu Lin ◽  
Ya‐Fei Qi ◽  
Xiaoqi Wang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Proton Transfer ◽  
Spin Echo ◽  
Three Dimensional ◽  
Amide Proton ◽  
Turbo Spin Echo ◽  
Turbo Spin ◽  
Amide Proton Transfer ◽  
Preliminary Study

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.

Sign in / Sign up

Export Citation Format

Share Document