Dhiman A, Kumar V, Das CJ. Quantitative magnetic resonance imaging in prostate cancer: A review of current technology. World J Radiol 2024; 16(10): 497-511 [DOI: 10.4329/wjr.v16.i10.497]
Corresponding Author of This Article
Chandan Jyoti Das, MD, PhD, Professor, Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, Delhi, India. chandan.das@aiims.edu
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Radiol. Oct 28, 2024; 16(10): 497-511 Published online Oct 28, 2024. doi: 10.4329/wjr.v16.i10.497
Quantitative magnetic resonance imaging in prostate cancer: A review of current technology
Ankita Dhiman, Virendra Kumar, Chandan Jyoti Das
Ankita Dhiman, Chandan Jyoti Das, Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
Virendra Kumar, Department of NMR & MRI Facility, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
Author contributions: Dhiman A, Kumar V, and Das CJ wrote the manuscript; Kumar V and Das CJ supervised and finally approval of the manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Chandan Jyoti Das, MD, PhD, Professor, Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, Delhi, India. chandan.das@aiims.edu
Received: May 14, 2024 Revised: September 26, 2024 Accepted: October 20, 2024 Published online: October 28, 2024 Processing time: 166 Days and 21.4 Hours
Abstract
Prostate cancer (PCa) imaging forms an important part of PCa clinical management. Magnetic resonance imaging is the modality of choice for prostate imaging. Most of the current imaging assessment is qualitative i.e., based on visual inspection and thus subjected to inter-observer disagreement. Quantitative imaging is better than qualitative assessment as it is more objective, and standardized, thus improving interobserver agreement. Apart from detecting PCa, few quantitative parameters may have potential to predict disease aggressiveness, and thus can be used for prognosis and deciding the course of management. There are various magnetic resonance imaging-based quantitative parameters and few of them are already part of PIRADS v.2.1. However, there are many other parameters that are under study and need further validation by rigorous multicenter studies before recommending them for routine clinical practice. This review intends to discuss the existing quantitative methods, recent developments, and novel techniques in detail.
Core Tip: Quantitative imaging has many advantages over conventional qualitative assessment. A few parameters have also been shown to correlate with the Gleason score and can help in deciding disease prognosis and clinical management. A quantitative imaging biomarker could improve prostate cancer (PCa) detection by minimizing inter-observer variability, thereby reducing overdiagnosis of clinically insignificant PCa (Gleason score < 7). This would help avoid unnecessary biopsies and decrease the overtreatment of slow-growing PCa. In addition, with further advancement in the quantitative imaging parameters, they may be used to monitor therapeutic response or to predict response to a particular treatment.
