Dataset Information

Dual-Tracer PET-MRI-Derived Imaging Biomarkers for Prediction of Clinically Significant Prostate Cancer.

ABSTRACT:

Purpose

To investigate if imaging biomarkers derived from 3-Tesla dual-tracer [(18)F]fluoromethylcholine (FMC) and [⁶⁸Ga]Ga-PSMA^HBED-CC conjugate 11 (PSMA)-positron emission tomography can adequately predict clinically significant prostate cancer (csPC).

Methods

We assessed 77 biopsy-proven PC patients who underwent 3T dual-tracer PET/mpMRI followed by radical prostatectomy (RP) between 2014 and 2017. We performed a retrospective lesion-based analysis of all cancer foci and compared it to whole-mount histopathology of the RP specimen. The primary aim was to investigate the pretherapeutic role of the imaging biomarkers FMC- and PSMA-maximum standardized uptake values (SUVmax) for the prediction of csPC and to compare it to the mpMRI-methods and PI-RADS score.

Results

Overall, we identified 104 cancer foci, 69 were clinically significant (66.3%) and 35 were clinically insignificant (33.7%). We found that the combined FMC+PSMA SUVmax were the only significant parameters (p < 0.001 and p = 0.049) for the prediction of csPC. ROC analysis showed an AUC for the prediction of csPC of 0.695 for PI-RADS scoring (95% CI 0.591 to 0.786), 0.792 for FMC SUVmax (95% CI 0.696 to 0.869), 0.852 for FMC+PSMA SUVmax (95% CI 0.764 to 0.917), and 0.852 for the multivariable CHAID model (95% CI 0.763 to 0.916). Comparing the AUCs, we found that FMC+PSMA SUVmax and the multivariable model were significantly more accurate for the prediction of csPC compared to PI-RADS scoring (p = 0.0123, p = 0.0253, respectively).

Conclusions

Combined FMC+PSMA SUVmax seems to be a reliable parameter for the prediction of csPC and might overcome the limitations of PI-RADS scoring. Further prospective studies are necessary to confirm these promising preliminary results.

SUBMITTER: Grubmuller B

PROVIDER: S-EPMC9954891 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Dual-Tracer PET-MRI-Derived Imaging Biomarkers for Prediction of Clinically Significant Prostate Cancer.

Grubmüller Bernhard B Huebner Nicolai A NA Rasul Sazan S Clauser Paola P Pötsch Nina N Grubmüller Karl Hermann KH Hacker Marcus M Hartenbach Sabrina S Shariat Shahrokh F SF Hartenbach Markus M Baltzer Pascal P

Current oncology (Toronto, Ont.) 20230130 2

<h4>Purpose</h4>To investigate if imaging biomarkers derived from 3-Tesla dual-tracer [(18)F]fluoromethylcholine (FMC) and [<sup>68</sup>Ga]Ga-PSMA<sup>HBED-CC</sup> conjugate 11 (PSMA)-positron emission tomography can adequately predict clinically significant prostate cancer (csPC).<h4>Methods</h4>We assessed 77 biopsy-proven PC patients who underwent 3T dual-tracer PET/mpMRI followed by radical prostatectomy (RP) between 2014 and 2017. We performed a retrospective lesion-based analysis of all ...[more]

PMID: 36826090

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Project description:BackgroundThere are some limitations in the commonly used methods for the detection of prostate cancer. There is a lack of nomograms based on multiparametric magnetic resonance imaging (mpMRI) and 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography (PET-CT) for the prediction of prostate cancer. The study seeks to compare the performance of mpMRI and 68Ga-PSMA PET-CT, and design a novel predictive model capable of predicting clinically significant prostate cancer (csPCa) before biopsy based on a combination of 68Ga-PSMA PET-CT, mpMRI, and patient clinical parameters.MethodsFrom September 2020 to June 2021, we prospectively enrolled 112 consecutive patients with no prior history of prostate cancer who underwent both 68Ga-PSMA PET-CT and mpMRI prior to biopsy at our clinical center. Univariate and multivariate regression analyses were used to identify predictors of csPCa, with a predictive model and its nomogram incorporating 68Ga-PSMA PET-CT, mpMRI, and the clinical predictors then being generated. The constructed model was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis, and further validated with the internal and external cohorts.ResultsThe model incorporated prostate-specific antigen density (PSAd), Prostate Imaging Reporting and Data System (PI-RADS) category, and maximum standardized uptake value (SUVmax), and it exhibited excellent predictive efficacy when applying to evaluate both training and validation cohorts [area under the curve (AUC): 0.936 and 0.940, respectively]. Compared with SUVmax alone, the model demonstrated excellent diagnostic performance with improved specificity (0.910, 95% CI: 0.824-0.963) and positive predictive values (0.811, 95% CI: 0.648-0.920). Calibration curve and decision curve analysis further confirmed that the model exhibited a high degree of clinical net benefit and low error rate.ConclusionsThe constructed model in this study was capable of accurately predicting csPCa prior to biopsy with excellent discriminative ability. As such, this model has the potential to be an effective non-invasive approach for the diagnosis of csPCa.

Project description:Standardized reporting of multiparametric prostate MRI (mpMRI) is widespread and follows international standards (Pi-RADS). However, quantitative measurements from mpMRI are not widely comparable. Although T2 mapping sequences can provide repeatable quantitative image measurements and extract reliable imaging biomarkers from mpMRI, they are often time-consuming. We therefore investigated the value of quantitative measurements on a highly accelerated T2 mapping sequence, in order to establish a threshold to differentiate benign from malignant lesions. For this purpose, we evaluated a novel, highly accelerated T2 mapping research sequence that enables high-resolution image acquisition with short acquisition times in everyday clinical practice. In this retrospective single-center study, we included 54 patients with clinically indicated MRI of the prostate and biopsy-confirmed carcinoma (n = 37) or exclusion of carcinoma (n = 17). All patients had received a standard of care biopsy of the prostate, results of which were used to confirm or exclude presence of malignant lesions. We used the linear mixed-effects model-fit by REML to determine the difference between mean values of cancerous tissue and healthy tissue. We found good differentiation between malignant lesions and normal appearing tissue in the peripheral zone based on the mean T2 value. Specifically, the mean T2 value for tissue without malignant lesions was (151.7 ms [95% CI: 146.9-156.5 ms] compared to 80.9 ms for malignant lesions [95% CI: 67.9-79.1 ms]; p < 0.001). Based on this assessment, a limit of 109.2 ms is suggested. Aditionally, a significant correlation was observed between T2 values of the peripheral zone and PI-RADS scores (p = 0.0194). However, no correlation was found between the Gleason Score and the T2 relaxation time. Using REML, we found a difference of -82.7 ms in mean values between cancerous tissue and healthy tissue. We established a cut-off-value of 109.2 ms to accurately differentiate between malignant and non-malignant prostate regions. The addition of T2 mapping sequences to routine imaging could benefit automated lesion detection and facilitate contrast-free multiparametric MRI of the prostate.

Dataset Information

Dual-Tracer PET-MRI-Derived Imaging Biomarkers for Prediction of Clinically Significant Prostate Cancer.

Purpose

Methods

Results

Conclusions

Publications

Dual-Tracer PET-MRI-Derived Imaging Biomarkers for Prediction of Clinically Significant Prostate Cancer.

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