Quantitative parameters in dynamic contrast-enhanced magnetic resonance imaging for the detection and characterization of prostate cancer

Abstract

Objectivesto assess the diagnostic accuracy of quantitative parameters of DCE-MRI in multi-parametric MRI (mpMRI) in comparison to the histopathology (including Gleason grade) of prostate cancer.Patients and methods150 men with suspected prostate cancer (abnormal digital rectum examination and or elevated prostate-specific antigen) received pre-biopsy 3T mpMRI and were recruited into peer-reviewed, protocol-based prospective study. The DCE-MRI quantitative parameters (Ktrans (influx transfer constant) and kep (efflux rate constant)) of the cancerous and normal areas were recorded using four different kinetic models employing Olea Sphere (Olea Medical, La Ciotat, France). The correlation between these parameters and the histopathology of the lesions (biopsy and in a sub-cohort 41 radical prostatectomy specimen) was assessed.ResultsThe quantitative parameters showed a significant difference between non-cancerous (benign) and cancerous lesions (Gleason score≥3+3) in the prostate gland. The cut-off values for prostate cancer differentiation were: Ktrans (0.205 min−1) and kep (0.665 min−1) in the extended Tofts model (ET) and Ktrans(0.205 min−1 and kep (0.63 min−1) in the Lawrence and Lee delay (LD) models respectively. The mean Ktrans value also showed a difference between low-grade cancer (Gleason score=3+3) and high-grade cancer (Gleason score ≥ 3+4). With the addition of DCE-MRI quantitative parameters, the sensitivity of the PIRAD scoring system was increased from 56.6% to 92.1% (Ktrans_ET), 93.1% (kep_ET), 91.0%, (Ktrans_LD) and 89.4% (kep_LD).ConclusionQuantitative DCE-MRI parameters improved the diagnostic performance of conventional MRI in distinguishing normal and prostate cancers, including characterization of grade of cancers. The ET and LD models in post-image processing analysis provided better cut-off values for prostate cancer differentiation than the other quantitative DCE-MRI parameters.

Prediction of prostate cancer Gleason score upgrading from biopsy to radical prostatectomy using pre-biopsy multiparametric MRI PIRADS scoring system

“…The GS for each patient was obtained. The radical prostate specimens for histology were sliced in patient-specific moulds to aid orientations between imaging and histology, which were fabricated using a 3D printer as described by our group and others previously 12,13 . Specifically, patient specific 3D printed moulds were made prior to surgery based on the T2-weighted MRI prostate capsule the moulds were customised for each patient using MIMICS and Solidworks.…”

Prediction of Postprostatectomy Biochemical Recurrence Using Quantitative Ultrasound Shear Wave Elastography Imaging

“…Previous published studies have reported USWE to have a high diagnostic accuracy in clinical staging and localization of tumors within prostate glands of men suspected of PCa (6, 7, 10, 11, 15, 16). Our findings in this study add to the existing knowledge and show that measurement of stiffness in kPa increase BCR prediction ability of clinicopathological parameters.…”

“…Then, radical prostatectomy was usually done within 2 months (62 days targets) after MRI in this cohort. MRI protocol was derived from the European Society of Uro-radiology Guidelines (ESUR) 2012 (13) for PCa detection; acquisition parameters are shown and summarized in Table 2 (10, 11). All MR images were analyzed and scored by experienced uro-radiologists (SA, SMB) using PI-RADS v2.0; patients' clinicopathology data were blinded to both radiologists.…”

“…In this paper, BCR as an outcome was a patient-based analysis and hence major predominant and highest histological grade was used. There could be multiple lesions for each patient and lesion-based analysis has been published by our research group previously (10, 11). Baseline characteristics and pathological outcomes were compared using the chi-square test for categorical data and the Student's t -test or ANOVA for continuous data.…”

“…We have recently shown that the technology can reliably predict the grade of cancer and may provide essential information on the biology and microenvironment of the cancerous lesions. Moreover, USWE showed a very high diagnostic accuracy in predicting clinically significant PCa in men opting for radical prostatectomy (10, 11).…”