Techniques and Outcomes of MRI-TRUS Fusion Prostate Biopsy

Current Approach to Complications and Difficulties during Transrectal Ultrasound-Guided Prostate Biopsies

Prostate cancer is one of the most common male malignancies worldwide. It affects middle-aged men (45-60 years) and is the leading cause of cancer-related mortality in Western countries. The TRUS (trans rectal ultrasound)-guided prostate biopsy has been a standard procedure in prostate cancer detection for more than thirty years, and it is recommended in male patients with an abnormal PSA (prostate-specific antigens) or abnormalities found during digital rectal examinations. During this procedure, urologists might encounter difficulties which may cause subsequent complications. This manuscript aims to present both the complications and the technical difficulties that may occur during TRUS-guided prostate biopsy, along with resolutions and solutions found in the specialized literature. The conclusions of this manuscript will note that the TRUS-guided prostate biopsy remains a solid, cost-efficient, and safe procedure with which to diagnose prostate cancer. The complications are usually self-limiting and do not require additional medical assistance. The difficulties posed by the procedure can be safely overcome if there are no other available alternatives. Open communication with the patients improves both pre- and post-procedure compliance.

MRI-Targeted Prostate Biopsy Introduces Grade Inflation and Overtreatment

Purpose

The use of MRI-targeted biopsies has led to lower detection of Gleason Grade Group 1 (GG1) prostate cancer and increased detection of GG2 disease. Although this finding is generally attributed to improved sensitivity and specificity of MRI for aggressive cancers, it might also be explained by grade inflation. Our objective was to determine the likelihood of definitive treatment and risk of post-treatment recurrence for patients with GG2 cancer diagnosed using targeted biopsies relative to men with GG1 cancer diagnosed using systematic biopsies.

Methods

We performed a retrospective study on a large tertiary centre registry (HUS Acamedic Datalake) to retrieve data on prostate cancer diagnosis, treatment, and cancer recurrence. We included patients with either GG1 with systematic biopsies (3317 men) or GG2 with targeted biopsies (554 men) from 1993 to 2019. We assessed the risk of curative treatment and recurrence after treatment. Kaplan-Meier survival curves were computed to assess treatment- and recurrence-free survival. Cox proportional hazards regression analysis was performed to assess the risk of posttreatment recurrence.

Results

Patients with systematic biopsy detected GG1 cancer had a significantly longer median time-to-treatment (31 months) than those with targeted biopsy detected GG2 cancer (4 months, p<0.0001). The risk of recurrence after curative treatment was similar between groups with the upper bound of 95% CI, excluding an important difference (HR: 0.94, 95% CI [0.71-1.25], p=0.7).

Conclusion

GG2 cancers detected by MRI-targeted biopsy are treated more aggressively than GG1 cancers detected by systematic biopsy, despite having similar oncologic risk. To prevent further overtreatment related to the MRI pathway, treatment guidelines from the pre-MRI era need to be updated to consider changes in the diagnostic pathway.

Reliability of Systematic and Targeted Biopsies versus Prostatectomy

Systematic Biopsy (SBx) has been and continues to be the standard staple for detecting prostate cancer. The more expensive MRI guided biopsy (MRITBx) is a better way of detecting cancer. The prostatectomy can provide an accurate condition of the prostate. The goal is to assess how reliable SBx and MRITBx are vis à vis prostatectomy. Graded Gleason scores are used for comparison. Cohen's Kappa index and logistic regression after binarization of the graded Gleason scores are some of the methods used to achieve our goals. Machine learning methods, such as classification trees, are employed to improve predictability clinically. The Cohen's Kappa index is 0.31 for SBx versus prostatectomy, which means a fair agreement. The index is 0.34 for MRITBx versus prostatectomy, which again means a fair agreement. A direct comparison of SBx versus prostatectomy via binarized graded scores gives sensitivity 0.83 and specificity 0.50. On the other hand, a direct comparison of MRITBx versus prostatectomy gives sensitivity 0.78 and specificity 0.67, putting MRITBx on a higher level of accuracy. The SBx and MRITBx do not yet match the findings of prostatectomy completely, but they are useful. We have developed new biomarkers, considering other pieces of information from the patients, to improve the accuracy of SBx and MRITBx. From a clinical point of view, we provide a prediction model for prostatectomy Gleason grades using classification tree methodology.

Current magnetic resonance imaging-based diagnostic strategies for prostate cancer

Recent developments in multiparametric MRI and MRI-targeted biopsy have made it possible to detect clinically significant cancers more accurately and efficiently than ever before. Furthermore, software that enables easy MRI/US image fusion has been developed and is already available on the market, and this has provided a tailwind for the spread of MRI-based prostate cancer diagnostic strategies. Such precise diagnosis of prostate cancer localization is essential for highly accurate focal therapy. In addition, a recent large-scale study applying MRI to community screening for prostate cancer has reported its usefulness. By contrast, concerns about overdiagnosis and overtreatment, the existence of inter-reader variability in MRI diagnosis, and issues with current MRI-targeted biopsy have emerged. In this article, we review the development of multiparametric MRI and MRI-targeted biopsy to date and the current issues and discuss future directions.

Predictors of Prostate Cancer at Fusion Biopsy: The Role of Positive Family History, Hypertension, Diabetes, and Body Mass Index

BACKGROUND

PSA density and an elevated PI-RADS score are among the strongest predictors of prostate cancer (PCa) in a fusion biopsy. Positive family history, hypertension, diabetes, and obesity have also been associated with the risk of developing PCa. We aim to identify predictors of the prostate cancer detection rate (CDR) in a series of patients undergoing a fusion biopsy.

METHODS

We retrospectively evaluated 736 consecutive patients who underwent an elastic fusion biopsy from 2020 to 2022. Targeted biopsies (2-4 cores per MRI target) were followed by systematic mapping (10-12 cores). Clinically significant PCa (csPCa) was defined as ISUP score ≥ 2. Uni- and multi-variable logistic regression analyses were performed to identify predictors of CDR among age, body mass index (BMI), hypertension, diabetes, positive family history, PSA, a positive digital rectal examination (DRE), PSA density ≥ 0.15, previous negative biopsy status, PI-RADS score, and size of MRI lesion.

RESULTS

The median patients' age was 71 years, and median PSA was 6.6 ng/mL. A total of 20% of patients had a positive digital rectal examination. Suspicious lesions in mpMRI were scored as 3, 4, and 5 in 14.9%, 55.0%, and 17.5% of cases, respectively. The CDR was 63.2% for all cancers and 58.7% for csPCa. Only age (OR 1.04, p < 0.001), a positive DRE (OR 1.75, p = 0.04), PSA density (OR 2.68, p < 0.001), and elevated PI-RADS score (OR 4.02, p = 0.003) were significant predictors of the CDR in the multivariable analysis for overall PCa. The same associations were found for csPCa. The size of an MRI lesion was associated with the CDR only in uni-variable analysis (OR 1.07, p < 0.001). BMI, hypertension, diabetes, and a positive family history were not predictors of PCa.

CONCLUSIONS

In a series of patients selected for a fusion biopsy, positive family history, hypertension, diabetes, or BMI are not predictors of PCa detection. PSA-density and PI-RADS score are confirmed to be strong predictors of the CDR.

Accuracy of elastic fusion biopsy: Comparing prostate cancer detection between targeted and systematic biopsy

INTRODUCTION

When performing targeted biopsy (TBx), the need to add systematic biopsies (SBx) is often debated. Aim of the study is to evaluate the added value of SBx in addition to TBx in terms of prostate cancer (PCa) detection rates (CDR), and to test the concordance between multiparametric magnetic resonance imaging (mpMRI) findings and fusion biopsy results in terms of cancer location.

METHODS

We performed a retrospective, multicentric study that gathered data on 1992 consecutive patients who underwent elastic fusion biopsy between 2011 and 2020. A standardized approach was used, with TBx (2-4 cores per target) followed by SBx (12-14 cores). We assessed CDR of TBx, of SBx, and TBx+SBx for all cancers and clinically significant PCa (csPCa), defined as ISUP score ≥2. CDR was evaluated according to radiological and clinical parameters, with a particular focus on PI-RADS 3 lesions. In a subgroup of 1254 patients we tested the discordance between mpMRI findings and fusion biopsy results in terms of cancer location. Uni- and multivariable logistic regression analyses were performed to identify predictors of CDR.

RESULTS

CDR of TBx+SBx was 63.0% for all cancers and 38.8% of csPCa. Per-patient analysis showed that SBx in addition to TBx improved CDR by 4.5% for all cancers and 3.4% for csPCa. Patients with lesions scored as PI-RADS 3, 4, and 5 were diagnosed with PCa in 27.9%, 72.8%, and 92.3%, and csPCa in 10.7%, 43.6%, and 69.3%, respectively. When positive, PI-RADS 3 lesions were ISUP grade 1 in 61.1% of cases. Per-lesion analysis showed that discordance between mpMRI and biopsy was found in 56.6% of cases, with 710 patients having positive SBx outside mpMRI targets, of which 414 (58.0%) were clinically significant. PSA density ≥0.15 was a strong predictor of CDR.

CONCLUSIONS

The addition of systematic mapping to TBx contributes to a minority of per-patient diagnoses but detects a high number of PCa foci outside mpMRI targets, increasing biopsy accuracy for the assessment of cancer burden within the prostate. High PSA-density significantly increases the risk of PCa, both in the whole cohort and in PI-RADS 3 cases.

MRI-measured adipose features as predictive factors for detection of prostate cancer in males undergoing systematic prostate biopsy: a retrospective study based on a Chinese population

In this study, we retrospectively evaluated the data of 901 men undergoing ultrasonography-guided systematic prostate biopsy between March 2013 and May 2022. Adipose features, including periprostatic adipose tissue (PPAT) thickness and subcutaneous fat thickness, were measured using MRI before biopsy. Prediction models of all PCa and clinically significant PCa (csPCa) (Gleason score higher than 6) were established based on variables selected by multivariate logistic regression and prediction nomograms were constructed. Patients with PCa had higher PPAT thickness (4.64 [3.65-5.86] vs. 3.54 [2.49-4.51] mm, p < 0.001) and subcutaneous fat thickness (29.19 [23.05-35.95] vs. 27.90 [21.43-33.93] mm, p = 0.013) than those without PCa. Patients with csPCa had higher PPAT thickness (4.78 [3.80-5.88] vs. 4.52 [3.80-5.63] mm, p = 0.041) than those with non-csPCa. Adding adipose features to the prediction models significantly increased the area under the receiver operating characteristics curve for the prediction of all PCa (0.850 vs. 0.819, p < 0.001) and csPCa (0.827 vs. 0.798, p < 0.001). Based on MRI-measured adipose features and clinical parameters, we established two nomograms that were simple to use and could improve patient selection for prostate biopsy in Chinese population.

Machine learning prediction of prostate cancer from transrectal ultrasound video clips

Objective

To build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI).

Methods

We systematically collated data from 501 patients—276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists’ diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists’ diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis.

Results

The AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists’ (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant (p< 0.05).

Conclusion

The prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model’s diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses.

Combination of biparametric magnetic resonance imaging with prostate-specific antigen density to stratify the risk of significant prostate cancer: Initial biopsy and long-term follow-up results

OBJECTIVES

To assess whether the combination of biparametric magnetic resonance imaging with prostate-specific antigen density can properly stratify the risk of significant prostate cancer in patients undergoing prostate biopsies and how this approach affects the detection of prostate cancer during follow-up in patients who do not undergo prostate biopsy.

METHODS

In total, 411 biopsy-naïve patients who had elevated prostate-specific antigen levels and then underwent biparametric magnetic resonance imaging for suspicious prostate cancer were analyzed: 203 patients underwent prostate biopsies, whereas 208 patients did not. Significant prostate cancer detection rates stratified by the combination of Prostate Imaging Reporting and Data System score and prostate-specific antigen density were assessed in patients who underwent prostate biopsies. The cumulative incidence of prostate cancer detection during the follow-up was assessed in patients who omitted biopsy.

RESULTS

The negative predictive value for significant prostate cancer was 89% for Prostate Imaging Reporting and Data System scores 1-3, which increased to 97% when prostate-specific antigen density <0.15 ng/ml/cm³ was combined. Among patients who did not undergo biopsy, patients with Prostate Imaging Reporting and Data System scores 1-3 plus prostate-specific antigen density <0.15 ng/ml/cm³ included significantly less cases in which significant prostate cancer was detected during the follow-up, compared with the others (3.2% versus 17% at 36 months).

CONCLUSIONS

Restriction of prostate biopsies to patients with Prostate Imaging Reporting and Data System scores 4-5 or prostate-specific antigen density ≥0.15 ng/ml/cm³ proved to be the good biopsy strategy, effectively balancing risks and benefits.

Multiparametric ultrasound of prostate: role in prostate cancer diagnosis

Recent advances in ultrasonography (US) technology established modalities, such as Doppler-US, HistoScanning, contrast-enhanced ultrasonography (CEUS), elastography, and micro-ultrasound. The early results of these US modalities have been promising, although there are limitations including the need for specialized equipment, inconsistent results, lack of standardizations, and external validation. In this review, we identified studies evaluating multiparametric ultrasonography (mpUS), the combination of multiple US modalities, for prostate cancer (PCa) diagnosis. In the past 5 years, a growing number of studies have shown that use of mpUS resulted in high PCa and clinically significant prostate cancer (CSPCa) detection performance using radical prostatectomy histology as the reference standard. Recent studies have demonstrated the role mpUS in improving detection of CSPCa and guidance for prostate biopsy and therapy. Furthermore, some aspects including lower costs, real-time imaging, applicability for some patients who have contraindication for magnetic resonance imaging (MRI) and availability in the office setting are clear advantages of mpUS. Interobserver agreement of mpUS was overall low; however, this limitation can be improved using standardized and objective evaluation systems such as the machine learning model. Whether mpUS outperforms MRI is unclear. Multicenter randomized controlled trials directly comparing mpUS and multiparametric MRI are warranted.

Precision interventional radiology

The recent interest in precision medicine among interventionists has led to the establishment of the concept of precision interventional radiology (PIR). This concept focuses not only on the accuracy of interventional operations using traditional image-guided techniques, but also on the comprehensive evaluation of diseases. The invisible features extracted from CT, MRI, or US improve the accuracy and specificity of diagnosis. The integration of multi-omics and molecule imaging provides more information for interventional operations. The development and application of drugs, embolic materials, and devices broaden the concept of PIR. Integrating medicine and engineering brings new image-guided techniques that increase the efficacy of interventional operations while reducing the complications of interventional treatment. In all, PIR, an important part of precision medicine, emphasizing the whole disease management process, including precision diagnosis, comprehensive evaluation, and interventional therapy, maximizes the benefits of patients with limited damage.