MR Imaging of Prostate Zonal Anatomy

Local Staging of Prostate Cancer at MRI: What the Urologist and Radiation Oncologist Want to Know

This slide presentation provides an overview of prostate cancer risk stratification and management, reviews key anatomic and imaging principles pertaining to local staging of prostate cancer, and highlights inflection points where MRI findings regarding local staging may modify clinical management.

Could Biparametric MRI Replace Multiparametric MRI in the Management of Prostate Cancer?

Prostate cancer (PCa) is a worldwide epidemiological problem, since it is one of the most prevalent types of neoplasia among men, and the third-leading cause of cancer-related deaths, after lung and colorectal tumors. Unfortunately, the early stages of PCa have a wide range of unspecific symptoms. For these reasons, early diagnosis and accurate evaluation of suspicious lesions are crucial. Multiparametric MRI (mpMRI) is currently the imaging modality of choice for diagnostic screening and local staging of PCa, but also has a leading role in guiding biopsies and in treatment biparametric MRI (bpMRI) could partially replace mpMRI due to its lack of adverse reactions caused by contrast agents, relatively lower costs, and shorter acquisition time. Further, 31 relevant articles regarding the advantages and disadvantages of the aforementioned imaging techniques were scanned. As a result, while bpMRI has comparable accuracy in detecting PCa, its roles in the other steps of PCa management are limited.

MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer

Objective

To reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa).

Methods

MRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2020. PA, central gland sectional area (CGA), and peripheral zone sectional area (PZA) were measured on axial T2-weighted prostate MRI. Multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curve were performed to evaluate and integrate the predictors of PCa. Based on multivariate logistic regression coefficients after excluding combinations of collinear variables, three models and nomograms were generated and intercompared by Delong test, calibration curve, and decision curve analysis (DCA).

Results

The positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, MRI, transCGA, coroPZA, transPA, and transPAI (transverse PZA-to-CGA ratio) were independent predictors of PCa. Compared with no PCa patients, transCGA (AUC = 0.801) was significantly lower and transPAI (AUC = 0.749) was significantly higher in PCa patients. Both of them have a significantly higher AUC than PSA (AUC = 0.714) and PV (AUC = 0.725). Our best predictive model included the factors age, PSA, MRI, transCGA, and coroPZA with the AUC of 0.918 for predicting PCa status. Based on this predictive model, a novel nomogram for predicting PCa was conducted and internally validated (C-index = 0.913).

Conclusions

We found the potential clinical utility of transCGA and transPAI in predicting PCa. Then, we firstly built the nomogram based on PA and its zone area to evaluate its diagnostic efficacy for PCa, which could reduce unnecessary prostate biopsies.

Biparametric (bp) and multiparametric (mp) magnetic resonance imaging (MRI) approach to prostate cancer disease: a narrative review of current debate on dynamic contrast enhancement

Prostate cancer is the most common malignancy in male population. Over the last few years, magnetic resonance imaging (MRI) has proved to be a robust clinical tool for identification and staging of clinically significant prostate cancer. Though suggestions by the European Society of Urogenital Radiology to use complete multiparametric (mp) T2-weighted/diffusion weighted imaging (DWI)/dynamic contrast enhancement (DCE) acquisition for all prostate MRI examinations, the real advantage of functional DCE remains a matter of debate. Recent studies demonstrate that biparametric (bp) and mp approaches have similar accuracy, but controversial evidences remain, and the specific potential benefits of contrast medium administration are still poorly discussed in literature. The bp approach is in fact sufficient in most cases to adequately identify a negative test, or to accurately define the degree of aggressiveness of a lesion, especially if larger or with major characteristics of malignancy. This feature would give the DCE a secondary role, probably limited to a second evaluation of the lesion location, for detecting small cancer or in case of controversy. However, DCE has proved to increase the sensitivity of prostate MRI, though a less specificity. Therefore, an appropriate decision algorithm is needed to standardize the MRI approach. Aim of this review study was to provide a schematic description of bpMRI and mpMRI approaches in the study of prostatic anatomy, focusing on comparative validity and current DCE application. Additional theoretical considerations on prostate MRI are provided.

Prostate MRI: practical guidelines for interpreting and reporting according to PI-RADS version 2.1

The increasing precision of multiparametric magnetic resonance imaging of the prostate, together with greater experience and standardization in its interpretation, has given this technique an important role in the management of prostate cancer, the most prevalent non-cutaneous cancer in men. This article reviews the concepts in PI-RADS version 2.1 for estimating the probability and zonal location of significant tumors of the prostate, using a practical approach that includes current considerations about the prerequisites for carrying out the test and recommendations for interpreting the findings. It emphasizes benign findings that can lead to confusion and the criteria for evaluating the probability of local spread, which must be included in the structured report.

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.

Pursuing an Elusive Prostate Carcinoma: A Case Report Involving Multiparametric MR and CT-Guided Biopsy

Prostate cancer is usually diagnosed when elevated PSA levels lead to a TRUS biopsy. We present a case in which there were several negative biopsies and a rising PSA. This led to a multiparametric MR (mpMRI) which demonstrated a large mass which originated in the anterior transition zone and had extended through the anterior fibromuscular stroma and prostate capsule with a significant extra-prostatic component. The mass was successfully approached anteriorly with a CT-guided biopsy. The patient was subsequently successfully treated with radiotherapy utilizing MR in the planning process. This case report summarizes the utility of mpMRI in this clinical setting. For patients with high suspicion of prostate malignancy despite prior negative biopsies, it can identify tumor in locations not amenable to TRUS biopsy. It also is critical for accurate radiation treatment planning, allowing for increased confidence in tumor targeting as well as sparing sensitive normal tissue.

Outcome prediction of prostatic artery embolization: post hoc analysis of a randomized, open-label, non-inferiority trial

OBJECTIVES

To identify predictors for different treatment outcomes after prostatic artery embolization (PAE) in the treatment of lower urinary tract symptoms secondary to benign prostatic hyperplasia.

PATIENTS AND METHODS

A post hoc analysis of data derived from the 48 patients undergoing PAE in a randomized, open-label, non-inferiority trial was performed. Relative changes in the International Prostate Symptoms Score (IPSS), absolute changes in maximum urinary flow rate (Q_max ), and relative changes in magnetic resonance imaging-assessed prostate volume from baseline to 12 weeks were defined as the outcomes measures of interest. Their association with various baseline characteristics and measures, technical details of PAE, and early postoperative measures were analysed using Spearman rank correlations and Wilcoxon rank-sum tests. The most promising predictors were further evaluated in receiver-operating characteristic (ROC) curve analyses.

RESULTS

Higher total prostate and central gland (i.e. central plus transitional zone) volumes were associated with more pronounced improvements in the IPSS (Spearman rank correlation [rs]: -0.35 and -0.34; P = 0.01 and P = 0.02, respectively) and the Q_max (rs: 0.31 and 0.39; P = 0.05 and P = 0.01, respectively). ROC curve analyses suggested that volumes of 39 and 38 mL for total prostate and central gland volume, respectively, would be the optimal thresholds with which to predict PAE success as measured by the IPSS. Other anatomical characteristics of the prostate, such as the central gland index, also showed an even more distinct correlation to the improvement in Q_max (rs: 0.46, P = 0.003). The relative changes in prostate volume were clearly dependent on the technical performance of PAE. Occurrence of postoperative pain and blood levels of prostate-specific antigen and C-reactive protein emerged as potential early-stage outcome predictors after PAE.

CONCLUSION

Baseline and peri-operative findings might help to guide patient selection and outcome prediction for PAE. Patients with larger prostates have a higher chance of success with PAE. Larger-scale clinical trials including a longer follow-up are warranted to further elucidate the most suitable patients for PAE.