Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images

Purpose.To assess the diagnostic accuracy of dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSCE-MRI) in differentiation between benign and malignant liver lesions by assessment of tumoral perfusion parameters.Methods Materials.Seventy-three patients with known focal liver lesions, including 45 benign (16 FNH, 27 angiomas, and 2 abscesses) and 28 malignant ones (17 metastases, 9 HCCs, and 2 cholangiocarcinoma) underwent 1.5 T MRI upper abdominal study, with standard protocol that included dynamic contrast-enhanced sequences. On dedicated workstation, time-intensity curves were determined and the following perfusion parameters were calculated: relative arterial, venous and late enhancement (RAE, RVE, RLE), maximum enhancement (ME), relative enhancement (RE), and time to peak (TTP).Results.All diagnoses were established either by histopathology or imaging follow-up. Perfusion mean values calculated in benign lesions were RAE 33.8%, RVE 66.03%, RLE 80.63%, ME 776.00%, MRE 86.27%, and TTP 146.95 sec. Corresponding perfusion values calculated in malignant lesions were RAE 22.47%, RVE 40.54%, RLE 47.52%, ME 448.78%, MRE 49.85%, and TTP 183.79 sec. Statistical difference (p<0.05) was achieved in all the perfusion parameters calculated, obtaining different cluster of perfusion kinetics between benign and malignant lesions.Conclusions.DSCE-MRI depicts kinetic differences in perfusion parameters among the different common liver lesions, related to tumour supply and microvascular characteristics.

Download Full-text

Dynamic Susceptibility Contrast-Enhanced Magnetic Resonance Imaging

10.32388/f6dpic ◽

2020 ◽

Author(s):

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Dynamic Susceptibility ◽

Resonance Imaging ◽

Dynamic Susceptibility Contrast ◽

Contrast Enhanced ◽

Susceptibility Contrast

Download Full-text

Quantification of Tumor Micro-Environment Acidity in Glioblastoma Using Principal Component Analysis of Dynamic Susceptibility Contrast-Enhanced MR Imaging and Machine Learning

10.21203/rs.3.rs-431537/v1 ◽

2021 ◽

Author(s):

Hamed Akbari ◽

Anahita Kazerooni ◽

Jeffery B. Ware ◽

Elizabeth Mamourian ◽

Hannah Anderson ◽

...

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Support Vector ◽

Dynamic Susceptibility ◽

Dynamic Susceptibility Contrast ◽

Tumor Ph ◽

Contrast Enhanced ◽

Susceptibility Contrast

Abstract Glioblastoma (GBM) has high metabolic demands, which can lead to acidification of the tumor microenvironment. We hypothesize that a machine learning model built on temporal principal component analysis (PCA) of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI can be used to estimate tumor acidity in GBM, as estimated by pH-sensitive amine chemical exchange saturation transfer echo-planar imaging (CEST-EPI). We analyzed 78 MRI scans in 32 treatment naïve and post-treatment GBM patients. All patients were imaged with DSC-MRI, and pH-weighting that was quantified from CEST-EPI estimation of the magnetization transfer ratio asymmetry (MTRasym) at 3 ppm. Enhancing tumor (ET), non-enhancing core (NC), and peritumoral T2 hyperintensity (namely, edema, ED) were used to extract principal components (PCs) and to build support vector machines regression (SVR) models to predict MTRasym values using PCs. Our predicted map correlated with MTRasym values with Spearman’s r equal to 0.66, 0.47, 0.67, 0.71, in NC, ET, ED, and overall, respectively (p<0.006). The results of this study demonstrates that PCA analysis of DSC imaging data can provide information about tumor pH in GBM patients, with the strongest association within the peritumoral regions.

Download Full-text