Skip to main content
Top
Published in: Current Urology Reports 12/2023

08-11-2023 | Prostate Cancer

Management Strategy for Prostate Imaging Reporting and Data System Category 3 Lesions

Authors: Zhen Kang, Daniel J. Margolis, Shaogang Wang, Qiubai Li, Jian Song, Liang Wang

Published in: Current Urology Reports | Issue 12/2023

Login to get access

Abstract

Purpose of Review

Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions present a clinical dilemma due to their uncertain nature, which complicates the development of a definitive management strategy. These lesions have an incidence rate of approximately 22–32%, with clinically significant prostate cancer (csPCa) accounting for about 10–30%. Therefore, a thorough evaluation is warranted.

Recent Findings

This review highlights the need for radiology peer review, including the confirmation of dynamic contrast-enhanced (DCE) compliance, as the initial step. Additional MRI models such as VERDICT or Tofts need to be verified. Current evidence shows that imaging and clinical indicators can be used for risk stratification of PI-RADS 3 lesions. For low-risk lesions, a safety net monitoring approach involving annual repeat MRI can be employed. In contrast, lesions deemed potentially risky based on prostate-specific antigen density (PSAD), 68 Ga-PSMA PET/CT, MPS, Proclarix, or AI/machine learning models should undergo biopsy. It is recommended to establish a multidisciplinary team that takes into account factors such as age, PSAD, prostate, and lesion size, as well as previous biopsy pathological findings.

Summary

Combining expert opinions, clinical-imaging indicators, and emerging methods will contribute to the development of management strategies for PI-RADS 3 lesions.
Literature
2.
go back to reference •• Wadera A, et al. Impact of PI-RADS Category 3 lesions on the diagnostic accuracy of MRI for detecting prostate cancer and the prevalence of prostate cancer within each PI-RADS category: a systematic review and meta-analysis. Br J Radiol. 2021;94(1118):20191050. https://doi.org/10.1259/bjr.20191050. The study found that PI-RADS category 3 lesions can significantly impact the diagnostic test accuracy of MRI for prostate cancer detection. This is the foundation for paying attention to PI-RADS 3 lesions.CrossRefPubMed •• Wadera A, et al. Impact of PI-RADS Category 3 lesions on the diagnostic accuracy of MRI for detecting prostate cancer and the prevalence of prostate cancer within each PI-RADS category: a systematic review and meta-analysis. Br J Radiol. 2021;94(1118):20191050. https://​doi.​org/​10.​1259/​bjr.​20191050The study found that PI-RADS category 3 lesions can significantly impact the diagnostic test accuracy of MRI for prostate cancer detection. This is the foundation for paying attention to PI-RADS 3 lesions.CrossRefPubMed
5.
go back to reference Lim CS, et al. Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis. Abdom Radiol (NY). 2021;46(12):5647–5658. https://doi.org/10.1007/s00261-021-03235-0. Lim CS, et al. Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis. Abdom Radiol (NY). 2021;46(12):5647–5658. https://​doi.​org/​10.​1007/​s00261-021-03235-0.
6.
go back to reference •• Hermie I, et al. Which clinical and radiological characteristics can predict clinically significant prostate cancer in PI-RADS 3 lesions? A retrospective study in a high-volume academic center. Eur J Radiol. 2019;114:92–8. https://doi.org/10.1016/j.ejrad.2019.02.031. The study found that prostate volume and the ratio of ADC tumor on ADC of the contralateral prostate have the potential to predict csPCa in PI-RADS 3 lesions with a sensitivity of 59% and specificity of 88%. This study explores useful clinical imaging indicators for diagnosing csPCa in PI-RADS 3 lesions.CrossRefPubMed •• Hermie I, et al. Which clinical and radiological characteristics can predict clinically significant prostate cancer in PI-RADS 3 lesions? A retrospective study in a high-volume academic center. Eur J Radiol. 2019;114:92–8. https://​doi.​org/​10.​1016/​j.​ejrad.​2019.​02.​031. The study found that prostate volume and the ratio of ADC tumor on ADC of the contralateral prostate have the potential to predict csPCa in PI-RADS 3 lesions with a sensitivity of 59% and specificity of 88%. This study explores useful clinical imaging indicators for diagnosing csPCa in PI-RADS 3 lesions.CrossRefPubMed
9.
go back to reference •• Rahota RG, et al. Pathological features of Prostate Imaging Reporting and Data System (PI-RADS) 3 MRI lesions in biopsy and radical prostatectomy specimens. BJU Int. 2022;129(5):621–6. https://doi.org/10.1111/bju.15563. The study found that PI-RADS 3 lesions exhibited aggressive features in almost 40% of cases, and PSA density and presence of csPCa on targeted biopsy are independent predictive factors for high grade and/or extraprostatic disease. This study elucidates the pathological features of PI-RADS 3 lesions and provides the proportion of those with malignant characteristics.CrossRefPubMed •• Rahota RG, et al. Pathological features of Prostate Imaging Reporting and Data System (PI-RADS) 3 MRI lesions in biopsy and radical prostatectomy specimens. BJU Int. 2022;129(5):621–6. https://​doi.​org/​10.​1111/​bju.​15563. The study found that PI-RADS 3 lesions exhibited aggressive features in almost 40% of cases, and PSA density and presence of csPCa on targeted biopsy are independent predictive factors for high grade and/or extraprostatic disease. This study elucidates the pathological features of PI-RADS 3 lesions and provides the proportion of those with malignant characteristics.CrossRefPubMed
20.
37.
go back to reference Cao Y, et al. The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer. Oncotarget. 2017;8(27):44040–44049. https://doi.org/10.18632/oncotarget.17182. Cao Y, et al. The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer. Oncotarget. 2017;8(27):44040–44049. https://​doi.​org/​10.​18632/​oncotarget.​17182.
46.
go back to reference •• Jin P, et al. Utility of clinical-radiomic model to identify clinically significant prostate cancer in biparametric MRI PI-RADS V2.1 category 3 lesions. Front Oncol. 2022;12:840786. https://doi.org/10.3389/fonc.2022.840786. The study found that the clinical-radiomic model could effectively identify csPCa among biparametric PI-RADS 3 lesions and thus could help avoid unnecessary biopsy and improve the life quality of patients. This study is one of the representatives of the radiomics research on PI-RADS 3 lesions. •• Jin P, et al. Utility of clinical-radiomic model to identify clinically significant prostate cancer in biparametric MRI PI-RADS V2.1 category 3 lesions. Front Oncol. 2022;12:840786. https://​doi.​org/​10.​3389/​fonc.​2022.​840786. The study found that the clinical-radiomic model could effectively identify csPCa among biparametric PI-RADS 3 lesions and thus could help avoid unnecessary biopsy and improve the life quality of patients. This study is one of the representatives of the radiomics research on PI-RADS 3 lesions.
51.
go back to reference •• Tolkach Y, et al. High-accuracy prostate cancer pathology using deep learning. Nat Mach Intell. 2020;2:1–8. https://doi.org/10.1038/s42256-020-0200-7. The study found that the overall accuracy of our model for tumor detection in two validation cohorts is comparable to that of pathologists and reaches 97.3% in a native version and more than 98% using the suggested DL-based augmentation strategies. This study inspired the use of deep learning models to predict the nature of PI-RADS 3 lesions.CrossRef •• Tolkach Y, et al. High-accuracy prostate cancer pathology using deep learning. Nat Mach Intell. 2020;2:1–8. https://​doi.​org/​10.​1038/​s42256-020-0200-7The study found that the overall accuracy of our model for tumor detection in two validation cohorts is comparable to that of pathologists and reaches 97.3% in a native version and more than 98% using the suggested DL-based augmentation strategies. This study inspired the use of deep learning models to predict the nature of PI-RADS 3 lesions.CrossRef
Metadata
Title
Management Strategy for Prostate Imaging Reporting and Data System Category 3 Lesions
Authors
Zhen Kang
Daniel J. Margolis
Shaogang Wang
Qiubai Li
Jian Song
Liang Wang
Publication date
08-11-2023
Publisher
Springer US
Published in
Current Urology Reports / Issue 12/2023
Print ISSN: 1527-2737
Electronic ISSN: 1534-6285
DOI
https://doi.org/10.1007/s11934-023-01187-0

Other articles of this Issue 12/2023

Current Urology Reports 12/2023 Go to the issue