Introduction

MR spectroscopic imaging (MRSI) can non-invasively detect and quantify prostate metabolites, such as choline-containing metabolites (tCho), citrate, creatine and spermine. Increased tCho and decreased citrate and spermine levels are metabolic alterations associated with prostate cancer. Lower citrate and spermine and elevated tCho are also correlated with prostate cancer aggressiveness (Gleason score[1])[2, 3], and the metabolite ratio (tCho+creatine+spermine)/citrate has been proposed as a biomarker for prostate cancer[3]. Positron emission tomography (PET) is another imaging technique that provides metabolic information. PET imaging with the amino-acid tracer anti-1-amino-3-18F-fluorocyclobutane-1-carboxylic acid (¹⁸F-Fluciclovine) is promising for detection and characterization of prostate cancer[4]. Despite significant differences in the ¹⁸F-Fluciclovine uptake between cancer tissue and normal prostate tissue, there is overlap of the uptake between prostate cancers and benign lesions, such as benign prostatic hyperplasia (BPH). With an integrated PET/MR scanner, MRSI and PET images can be acquired simultaneously. Since both PET and MRSI focus on metabolism, the aim of this study was to examine their correlations and diagnostic performance in prostate cancer lesion localization.

Methods

Nineteen patients (median (range) age of 66 (55-72) years) with biopsy proven high risk prostate cancer (Gleason score ≥ 8 and/or a prostate specific antigen (PSA) ≥ 20 ng/mL, and/or clinical stage ≥ cT3) were included. Imaging protocol: Imaging was performed on an integrated 3T PET/MR system (Biograph mMR, Siemens Healthineers, Erlangen, Germany). The PET/MR protocol consisted of a full clinical mpMRI examination for T+N-staging, combined with 45 minutes of simultaneous PET imaging as previously described[4]. MRSI data was obtained with a standard point resolved spectroscopy (PRESS) sequence with dual-frequency water and lipid suppression at TR/TE=750/145 ms[5], FOV=70×70×70 cm3, interpolated matrix size of 16×16×16, and outer volume suppression slabs positioned around the prostate. MRSI analysis: Spectroscopy voxels with sufficient signal to noise based on visual inspection were selected for analysis. Spectral data were analyzed using the Java-based software jMRUI v5.2 (http://www.jmrui.eu/), and the spectral peaks were fitted using the AMARES method[6]. The total spectral intensity of all resonances from tCho, creatine, and spermine (3.0 - 3.22 ppm) were summed and expressed as CCS. The citrate peaks were modeled as a sum of four Lorentzian peaks. The metabolite ratio of CCS to citrate (CCS/C) was generated voxel-wise. PET analysis: Standardized uptake values (SUV) were linearly interpolated to overlay on both the corresponding T2-weighted image and the spectroscopic voxel matrix. The MRSI voxels are considerably larger than PET voxels, thus, a voxel from the MRSI data contained sixteen SUV voxels. The mean and maximum values of these sixteen SUVs were calculated, giving the SUV_mean and SUV_max per MRSI voxel (Fig 1). Histopathology: A pathologist specialized in uropathology outlined cancer foci and regions of BPH and inflammation and described cancer grade according to the Gleason scoring system[1] on whole-mount histopathology slices. We used these slices to identify the underlying tissue content of the selected spectroscopy voxels by visually matching them with the corresponding slice of T2-weighted images.

Results

The included patients (n=19) had a median (range) PSA level of 15.4 (3.7-56.9) ng/mL, median (range) biopsy Gleason of 4+5 (3+3 to 4+5), and their clinical stage ranged from cT2b to cT3b. In total 196 spectroscopic voxels with sufficient signal-to-noise were analyzed (on average 10 voxels per patient). MRSI voxels were categorized as representing either healthy (n=105), BPH (n=40), or tumor tissues (n=51) based on histology. The metabolic ratio derived from MRSI was significantly correlated with both SUV_mean (R=0.42, P<0.0001) and SUV_max (R=0.44, P<0.0001) (Fig. 2). The results from ROC analysis show that neither MRSI nor PET performed better than the other in distinguishing tumor voxels from other voxels (healthy and BPH). The AUC of the ROC curves for CCS/C, SUV_mean, and SUV_max did not differ significantly, but were significantly higher than unity (50%) line (Fig. 3). Importantly, the combination of all imaging variables provided a higher AUC value than the AUC values derived from the individual ROCs (P<0.05).

Discussion

In this study, we demonstrate that the simultaneous acquisition of ¹⁸F-Fluciclovine PET and MRSI with an integrated PET/MR system is feasible. Although the number of MRSI voxels with sufficient SNR was low, we could explore the association between MRSI visible metabolites and uptake of the amino acid analogue ¹⁸F-Fluciclovine based on simultaneous MRSI and PET imaging. The results showed a significant, yet moderate correlation between both SUV_mean and SUV_max and the CCS/C ratio, indicating complementary information regarding prostate metabolites from the two modalities. A combination of the imaging outcomes derived from the integrated PET/MRSI modalities improves the localization accuracy of prostate cancer lesions.

Acknowledgements

No acknowledgement found.