Literature DB >> 26730884

Stratification of the aggressiveness of prostate cancer using pre-biopsy multiparametric MRI (mpMRI).

Durgesh Kumar Dwivedi1, Rajeev Kumar2, Girdhar S Bora2, Sanjay Thulkar3, Sanjay Sharma3, Siddhartha Datta Gupta4, Naranamangalam R Jagannathan1.   

Abstract

Risk stratification, based on the Gleason score (GS) of a prostate biopsy, is an important decision-making tool in prostate cancer management. As low-grade disease may not need active intervention, the ability to identify aggressive cancers on imaging could limit the need for prostate biopsies. We assessed the ability of multiparametric MRI (mpMRI) in pre-biopsy risk stratification of men with prostate cancer. One hundred and twenty men suspected to have prostate cancer underwent mpMRI (diffusion MRI and MR spectroscopic imaging) prior to biopsy. Twenty-six had cancer and were stratified into three groups based on GS: low grade (GS ≤ 6), intermediate grade (GS = 7) and high grade (GS ≥ 8). A total of 910 regions of interest (ROIs) from the peripheral zone (PZ, range 25-45) were analyzed from these 26 patients. The metabolite ratio [citrate/(choline + creatine)] and apparent diffusion coefficient (ADC) of voxels were calculated for the PZ regions corresponding to the biopsy cores and compared with histology. The median metabolite ratios for low-grade, intermediate-grade and high-grade cancer were 0.29 (range: 0.16, 0.61), 0.17 (range: 0.13, 0.32) and 0.13 (range: 0.05, 0.23), respectively (p = 0.004). The corresponding mean ADCs (×10(-3) mm(2) /s) for low-grade, intermediate-grade and high-grade cancer were 0.99 ± 0.08, 0.86 ± 0.11 and 0.69 ± 0.12, respectively (p < 0.0001). The combined ADC and metabolite ratio model showed strong discriminatory ability to differentiate subjects with GS ≤ 6 from subjects with GS ≥ 7 with an area under the curve of 94%. These data indicate that pre-biopsy mpMRI may stratify PCa aggressiveness noninvasively. As the recent literature data suggest that men with GS ≤ 6 cancer may not need radical therapy, our data may help limit the need for biopsy and allow informed decision making for clinical intervention.
Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Entities:  

Keywords:  MR spectroscopic imaging (MRSI); diffusion-weighted MRI (DWI); disease aggressiveness; prostate cancer; stratification, multiparametric MRI (mpMRI)

Mesh:

Year:  2016        PMID: 26730884     DOI: 10.1002/nbm.3452

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  5 in total

1.  Biophysical review's 'meet the editors series'-a profile of Naranamangalam R. Jagannathan.

Authors:  Naranamangalam R Jagannathan
Journal:  Biophys Rev       Date:  2020-05-27

Review 2.  Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI.

Authors:  Durgesh Kumar Dwivedi; Naranamangalam R Jagannathan
Journal:  MAGMA       Date:  2022-07-22       Impact factor: 2.533

3.  A urologist's perspective on prostate cancer imaging: past, present, and future.

Authors:  Arvin K George; Baris Turkbey; Subin G Valayil; Akhil Muthigi; Francesca Mertan; Michael Kongnyuy; Peter A Pinto
Journal:  Abdom Radiol (NY)       Date:  2016-05

4.  Predictive Models in Differentiating Vertebral Lesions Using Multiparametric MRI.

Authors:  R Rathore; A Parihar; D K Dwivedi; A K Dwivedi; N Kohli; R K Garg; A Chandra
Journal:  AJNR Am J Neuroradiol       Date:  2017-10-12       Impact factor: 3.825

Review 5.  Diagnostic Accuracy of Predictive Models in Prostate Cancer: A Systematic Review and Meta-Analysis.

Authors:  Mohammad Saatchi; Fatemeh Khatami; Rahil Mashhadi; Akram Mirzaei; Leila Zareian; Zeinab Ahadi; Seyed Mohammad Kazem Aghamir
Journal:  Prostate Cancer       Date:  2022-06-08
  5 in total

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