Abstract
BackgroundCurrent multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading.MethodsForty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient.ResultsThe addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01).ConclusionThe inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.
Highlights
Current multiparametric Magnetic resonance imaging (MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer
We developed a machine learning platform for mpMRI including support vector machine (SVM) classifications with a radial basis function kernel (RBF-SVM) and area under receiver operator characteristic (ROC) analyses using an in-house Matlab routine to evaluate the diagnostic performance of models with different parametric combinations: T2-weighted imaging (T2WI) + diffusion-weighted imaging (DWI), T2WI + DWI + dynamic contrast enhancement (DCE), T2WI + DWI + magnetic resonance spectroscopic imaging (MRSI), and T2WI + DWI + DCE + MRSI
Multiparametric MRI of prostate cancer patients To avoid inclusion of low-quality data, the MR data from four patients were excluded from the study as they showed distortion artefacts on DWI and had a FWHMwater > 50 Hz for the whole prostate gland, related to patients’ movements and motion during scanning
Summary
Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading. Point-resolved spectroscopy (PRESS) is the most commonly used pulse sequence for prostate MRSI mainly because of its commercial availability, but it suffers from chemical shift displacement error, long acquisition time and bad slice profiles, which causes unpredictable lipid signal contamination These problems were addressed by using a semi-localized adiabatic selective refocusing pulse sequence (sLASER) with gradient-modulated offset-independent adiabatic (GOIA) pulses (GOIAsLASER), which resulted in much cleaner MR spectra of the prostate [12]. There have been no reports on the diagnostic performance of MRSI GOIAsLASER within routine clinical prostate mp-MRI exams
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