scholarly journals Assessment of Amide proton transfer weighted (APTw) MRI for pre-surgical prediction of final diagnosis in gliomas

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244003
Author(s):  
Faris Durmo ◽  
Anna Rydhög ◽  
Frederik Testud ◽  
Jimmy Lätt ◽  
Benjamin Schmitt ◽  
...  

Purpose Radiological assessment of primary brain neoplasms, both high (HGG) and low grade tumors (LGG), based on contrast-enhancement alone can be inaccurate. We evaluated the radiological value of amide proton transfer weighted (APTw) MRI as an imaging complement for pre-surgical radiological diagnosis of brain tumors. Methods Twenty-six patients were evaluated prospectively; (22 males, 4 females, mean age 55 years, range 26–76 years) underwent MRI at 3T using T1-MPRAGE pre- and post-contrast administration, conventional T2w, FLAIR, and APTw imaging pre-surgically for suspected primary/secondary brain tumor. Assessment of the additional value of APTw imaging compared to conventional MRI for correct pre-surgical brain tumor diagnosis. The initial radiological pre-operative diagnosis was based on the conventional contrast-enhanced MR images. The range, minimum, maximum, and mean APTw signals were evaluated. Conventional normality testing was performed; with boxplots/outliers/skewness/kurtosis and a Shapiro–Wilk’s test. Mann-Whitney U for analysis of significance for mean/max/min and range APTw signal. A logistic regression model was constructed for mean, max, range and Receiver Operating Characteristic (ROC) curves calculated for individual and combined APTw signals Results Conventional radiological diagnosis prior to surgery/biopsy was HGG (8 patients), LGG (12 patients), and metastasis (6 patients). Using the mean and maximum: APTw signal would have changed the pre-operative evaluation the diagnosis in 8 of 22 patients (two LGGs excluded, two METs excluded). Using a cut off value of >2.0% for mean APTw signal integral, 4 of the 12 radiologically suspected LGG would have been diagnosed as high grade glioma, which was confirmed by histopathological diagnosis. APTw mean of >2.0% and max >2.48% outperformed four separate clinical radiological assessments of tumor type, P-values = .004 and = .002, respectively. Conclusions Using APTw-images as part of the daily clinical pre-operative radiological evaluation may improve diagnostic precision in differentiating LGGs from HGGs, with potential improvement of patient management and treatment.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisabeth Sartoretti ◽  
Thomas Sartoretti ◽  
Michael Wyss ◽  
Carolin Reischauer ◽  
Luuk van Smoorenburg ◽  
...  

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.


2008 ◽  
Vol 60 (4) ◽  
pp. 842-849 ◽  
Author(s):  
Jinyuan Zhou ◽  
Jaishri O. Blakeley ◽  
Jun Hua ◽  
Mina Kim ◽  
John Laterra ◽  
...  

2019 ◽  
Vol 29 (12) ◽  
pp. 6643-6652 ◽  
Author(s):  
Bio Joo ◽  
Kyunghwa Han ◽  
Sung Soo Ahn ◽  
Yoon Seong Choi ◽  
Jong Hee Chang ◽  
...  

2019 ◽  
Vol 61 (5) ◽  
pp. 525-534 ◽  
Author(s):  
Chong Hyun Suh ◽  
Ji Eun Park ◽  
Seung Chai Jung ◽  
Choong Gon Choi ◽  
Sang Joon Kim ◽  
...  

2019 ◽  
Vol 29 (9) ◽  
pp. 4957-4967 ◽  
Author(s):  
Daniel Paech ◽  
Constantin Dreher ◽  
Sebastian Regnery ◽  
Jan-Eric Meissner ◽  
Steffen Goerke ◽  
...  

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