scholarly journals Prediction of H3K27M-mutant brainstem glioma by amide proton transfer–weighted imaging and its derived radiomics

2021 ◽  
Author(s):  
Zhizheng Zhuo ◽  
Liying Qu ◽  
Peng Zhang ◽  
Yunyun Duan ◽  
Dan Cheng ◽  
...  
Keyword(s):  
Proton Transfer ◽  
Brainstem Glioma ◽  
Amide Proton ◽  
Amide Proton Transfer

scholarly journals Prediction of H3K27M-mutant Brainstem Glioma by Amide Proton Transfer Weighted Imaging and Its Derived Radiomics

2021 ◽  
Author(s):  
Zhizheng Zhuo ◽  
Liying Qu ◽  
Peng Zhang ◽  
Yunyun Duan ◽  
Dan Cheng ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Proton Transfer ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Brainstem Glioma ◽  
Amide Proton ◽  
Mutation Status ◽  
Amide Proton Transfer ◽  
H3k27m Mutation

Abstract Purpose H3K27M-mutant associated brainstem glioma (BSG) carries a very poor prognosis. We aimed to predict H3K27M mutation status by amide proton transfer weighted (APTw) imaging and radiomic features. Methods Eighty-one BSG patients with APTw imaging at 3T MRI and known H3K27M status were retrospectively studied. APTw values (mean, median and max) and radiomic features within manually delineated 3D tumor masks were extracted. Comparison of APTw measures between H3K27M-mutant and wildtype groups was conducted by two-sample Student’s T/Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis. H3K27M-mutant prediction using APTw-derived radiomics was conducted using a machine-learning algorithm in randomly selected train (n=64) and test (n=17) sets. Sensitivity analysis with additional random splits of train and test sets, 2D tumor masks and other classifiers were conducted. Finally, a prospective cohort including 29 BSG patients was acquired for validation of the radiomics algorithm. Results BSG patients with H3K27M-mutant were younger and had higher max APTw values than those with wildtype. APTw-derived radiomic measures reflecting tumor heterogeneity could predict H3K27M mutation status with an accuracy of 0.88, sensitivity of 0.92 and specificity of 0.80 in the test set. Sensitivity analysis confirmed the predictive ability (accuracy range: 0.71-0.94). In the independent prospective validation cohort, the algorithm reached an accuracy of 0.86, sensitivity of 0.88 and specificity of 0.85 for predicting H3K27M-mutation status. Conclusion BSG patients with H3K27M-mutant had higher max APTw values than those with wildtype. APTw-derived radiomics could accurately predict a H3K27M-mutant status in BSG patients.

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.

Amide proton transfer imaging for differentiation of tuberculomas from high-grade gliomas: Preliminary experience

2021 ◽  
pp. 197140092110027
Author(s):  
Karthik Kulanthaivelu ◽  
Shumyla Jabeen ◽  
Jitender Saini ◽  
Sanita Raju ◽  
Atchayaram Nalini ◽  
...  
Keyword(s):  
Proton Transfer ◽  
Chemical Exchange ◽  
Spin Echo ◽  
Chemical Exchange Saturation Transfer ◽  
Amide Proton ◽  
High Grade ◽  
Saturation Pulse ◽  
Amide Proton Transfer ◽  
High Grade Gliomas ◽  
Amide Protons

Purpose Tuberculomas can occasionally masquerade as high-grade gliomas (HGG). Evidence from magnetisation transfer (MT) imaging suggests that there is lower protein content in the tuberculoma microenvironment. Building on the principles of chemical exchange saturation transfer and MT, amide proton transfer (APT) imaging generates tissue contrast as a function of the mobile amide protons in tissue’s native peptides and intracellular proteins. This study aimed to further the understanding of tuberculomas using APT and to compare it with HGG. Method Twenty-two patients ( n = 8 tuberculoma; n = 14 HGG) were included in the study. APT was a 3D turbo spin-echo Dixon sequence with inbuilt B0 correction. A two-second, 2 μT saturation pulse alternating over transmit channels was applied at ±3.5 ppm around water resonance. The APT-weighted image (APTw) was computed as the MT ratio asymmetry (MTRasym) at 3.5 ppm. Mean MTRasym values in regions of interest (areas = 9 mm2; positioned in component with homogeneous enhancement/least apparent diffusion coefficient) were used for the analysis. Results MTRasym values of tuberculomas ( n = 14; 8 cases) ranged from 1.34% to 3.11% ( M = 2.32 ± 0.50). HGG ( n = 17;14 cases) showed MTRasym ranging from 2.40% to 5.70% ( M = 4.32 ± 0.84). The inter-group difference in MTRasym was statistically significant ( p < 0.001). APTw images in tuberculomas were notable for high MTRasym values in the perilesional oedematous-appearing parenchyma (compared to contralateral white matter; p < 0.001). Conclusion Tuberculomas demonstrate lower MTRasym ratios compared to HGG, reflective of a relative paucity of mobile amide protons in the ambient microenvironment. Elevated MTRasym values in perilesional parenchyma in tuberculomas are a unique observation that may be a clue to the inflammatory milieu.

scholarly journals Characterization of Lung Cancer by Amide Proton Transfer (APT) Imaging: An In-Vivo Study in an Orthotopic Mouse Model

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e77019 ◽  
Author(s):  
Osamu Togao ◽  
Chase W. Kessinger ◽  
Gang Huang ◽  
Todd C. Soesbe ◽  
Koji Sagiyama ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Mouse Model ◽  
Proton Transfer ◽  
In Vivo Study ◽  
Amide Proton ◽  
Orthotopic Mouse Model ◽  
Amide Proton Transfer ◽  
Apt Imaging

Quantitative Analysis of Mobile Proteins in Normal Brain Tissue by Amide Proton Transfer Imaging

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Kazuaki Sugawara ◽  
Tosiaki Miyati ◽  
Ryo Ueda ◽  
Daisuke Yoshimaru ◽  
Masanobu Nakamura ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Proton Transfer ◽  
Brain Tissue ◽  
Amide Proton ◽  
Normal Brain ◽  
Normal Brain Tissue ◽  
Amide Proton Transfer ◽  
Amide Proton Transfer Imaging

scholarly journals Noninvasive amide proton transfer magnetic resonance imaging in evaluating the grading and cellularity of gliomas

Oncotarget ◽  
2016 ◽  
Vol 8 (4) ◽  
pp. 5834-5842 ◽  
Author(s):  
Yan Bai ◽  
Yusong Lin ◽  
Wei Zhang ◽  
Lingfei Kong ◽  
Lifu Wang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Proton Transfer ◽  
Amide Proton ◽  
Resonance Imaging ◽  
Amide Proton Transfer

scholarly journals Amide proton transfer (APT) contrast for imaging of brain tumors

2003 ◽  
Vol 50 (6) ◽  
pp. 1120-1126 ◽  
Author(s):  
Jinyuan Zhou ◽  
Bachchu Lal ◽  
David A. Wilson ◽  
John Laterra ◽  
Peter C.M. van Zijl
Keyword(s):  
Brain Tumors ◽  
Proton Transfer ◽  
Amide Proton ◽  
Amide Proton Transfer
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