Three-dimensional nuclear magnetic resonance spectroscopy: a complementary tool to multiparametric magnetic resonance imaging in the identification of aggressive prostate cancer at 3.0T

Abstract

Background: The limitations of the assessment of tumor aggressiveness by Prostate Imaging Reporting and Data System (PI-RADS) and biopsies suggest that the diagnostic algorithm could be improved by quantitative measurements in some chosen indications. We assessed the tumor high-risk predictive performance of 3.0 Tesla (3.0T) multiparametric magnetic resonance imaging (mp-MRI) combined with nuclear magnetic resonance spectroscopic sequences (NMR-S) in order to show that the metabolic analysis could bring out an evocative result for the aggressive form of prostate cancer. Methods:We conducted a retrospective study of 26 patients (mean age, 62.4 years) who had surgery for prostate cancer between 2009 and 2016 after pre-therapeutic assessment with 3.0T mp-MRI and NMR-S.Groups within the intermediate range of the D'Amico risk classification were divided into two categories, low risk (n=20) and high risk (n=6), according to the International Society of Urological Pathology (ISUP) 2-3 limit. Histoprognostic discordances within various risk groups were compared with the corresponding predictive MRI values. The performance of predictive models was assessed based on sensitivity, specificity, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves.Results: After prostatectomy, histological analysis reclassified 18 patients as high-risk, including 16 who were T3 MRI grade, of whom 13 (81.3%) were found to be pT3. Among the patients who had cT1 or cT2 digital rectal examinations, the T3 MRI factor multiplied by 8.7 [odds ratio (OR), 8.7; 95% confidence interval (CI), 1.3-56.2; P=0.024] the relative risk of being pT3 and by 5.8 (OR, 5.8; 95% CI, 0.95-35.7; P=0.05) the relative risk of being pGleason (pGS) > GS-prostate biopsy. Spectroscopic data showed that the choline concentration was significantly higher (P=0.001) in aggressive disease. Conclusions:The predictive model of tumor aggressiveness combining mp-MRI plus NMR-S was better than the mp-MRI model alone (AUC, 0.95 vs. 0.86). Information obtained by mp-MRI coupled with spectroscopy may improve the detection of occult aggressive disease, helping in the discrimination of intermediate risks.

A multivariate curve resolution analysis of multicenter proton spectroscopic imaging of the prostate for cancer localization and assessment of aggressiveness

“…Several studies have shown that the differentiation between low-and high-risk cancer lesions by mpMRI, employing T 2 W MRI, DW imaging, and DCE imaging, is enhanced by adding 1 H-MRSI data. [20][21][22]24 With our high AUC values for this differentiation, we expect even more enhancement by adding an MCR analysis.…”

“…Prostate MRSI studies of PCa patients commonly demonstrate a correlation between GSs and metabolite ratios and find significant differences between low-and high-risk cancer lesions. 3,20,21,24,[51][52][53] However, the overlap of ratio values between different risk groups hampers the assignment of individual cases to particular risk groups. Several studies have shown that the differentiation between low-and high-risk cancer lesions by mpMRI, employing T 2 W MRI, DW imaging, and DCE imaging, is enhanced by adding 1 H-MRSI data.…”

“…3,8,[13][14][15][19][20][21] However, like other quantitative parameters from an mpMRI examination, the overlap of these ratio values between different (risk) groups necessitates a combined evaluation of parameters for optimal performance in PCa assessments. [20][21][22][23][24] Although 1 H-MRSI was part of the initial PIRADS version, it was excluded from subsequent versions, 25 because of limitations in the robustness of early acquisition procedures, the need for significant in-house expertise, and the lack of standardized automated processing and evaluation tools. 3,13 Yet, novel acquisition methods have shown substantially improved MRSI performance, 13,20,26 which may greatly enhance its clinical value in mpMRI, for example, in an active surveillance setting.…”

“…It has been demonstrated in numerous papers that metabolite ratios can be employed in the localization, aggressiveness assessment, and treatment evaluation of cancer lesions 3,8,13–15,19–21 . However, like other quantitative parameters from an mpMRI examination, the overlap of these ratio values between different (risk) groups necessitates a combined evaluation of parameters for optimal performance in PCa assessments 20–24 . Although 1 H‐MRSI was part of the initial PIRADS version, it was excluded from subsequent versions, 25 because of limitations in the robustness of early acquisition procedures, the need for significant in‐house expertise, and the lack of standardized automated processing and evaluation tools 3,13 .…”

Prediction of prostate cancer recurrence after radiation therapy using multiparametric magnetic resonance imaging and spectroscopy: assessment of prognostic factors on pretreatment imaging

“…The early decrease in citrate is therefore due to both changes in cellular function and in the organization of the tissue, but may also decrease in prostatitis, when the choline level increases proportionally to tumor cell proliferation or in the case of necrosis, a phenomenon which is mainly visible in undifferentiated tumors, particularly with a high Gleason grade ( 39 ). Indeed, the use of metabolite concentrations may even be more pertinent than simple ratios, commonly used in the literature, as shown in the recent study by Deal et al ( 40 ) in which spectroscopic data showed that the choline concentration was significantly higher in aggressive disease. Moreover, in their study, the predictive model of tumor aggressiveness combining mpMRI and 3D MRS was better than the mpMRI model alone.…”