Current role of systematic biopsy in diagnosis of clinically significant prostate cancer in primary combined MRI-targeted biopsy: a high-volume single-center study

Factors improving the diagnostic performance of targeted biopsies in the diagnosis of significant prostate cancer

INTRODUCTION

MRI-targeted biopsy improves detection of significant prostate cancer (csPCa) and grade prediction. The aim of this study was to identify factors improving the diagnostic performance of targeted biopsies (TB) in detecting csPCa.

METHODS

Retrospective monocenter study of patients who underwent a radical prostatectomy (RP) for prostate cancer (PCa) and diagnosed by transrectal combined biopsies (CB) with elastic MRI/ultrasound fusion. We evaluate the diagnostic performance of standardized (SB), targeted (TB) and CB for csPCa, including sensitivity, specificity, and ROC curve. Univariables and logistic regression analysis were performed to analyze factors improving the diagnostic performance of TB in detecting csPCa on final histopathology.

RESULTS

Two hundred and four men underwent RP after CB with suspicious lesions (PI-RADS≥3) on MRI were included. csPCa was significantly associated with prostate volume, PSA density, a lesion index in the peripheral zone, with a diameter≥7mm. TB were positives for 174 patients (85.3%). Prostate volume, PSA density, radiological coherence, previous biopsies, and a number of biopsies≥3 were significantly associated with a cancer detection. csPCa on TB, a prostate volume<60mL, an index lesion≥7mm and a peripheral zone location were significant predictive factors for diagnostic of csPCa on final histopathology. Area under the ROC curve values, sensitivities and specificities of CB and TB (adjusted model) were 0.78 [0.72-0.84], 77.3 [70.3-83.4], 78.1 [60-90.7], and 0.85 [0.79-0.90], 83.7 [77.3-88.9] and 75 [56.6-88.5] respectively.

CONCLUSION

This study confirms the benefit of CB and suggests that TB for a selected population could be as effective as CB.

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions

PURPOSE OF REVIEW

In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC.

RECENT FINDINGS

In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy.

SUMMARY

The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

Elucidating the need for prostate cancer risk calculators in conjunction with mpMRI in initial risk assessment before prostate biopsy at a tertiary prostate cancer center

OBJECTIVE

Utilizing personalized risk assessment for clinically significant prostate cancer (csPCa) incorporating multiparametric magnetic resonance imaging (mpMRI) reduces biopsies and overdiagnosis. We validated both multi- and univariate risk models in biopsy-naïve men, with and without the inclusion of mpMRI data for csPCa detection.

METHODS

N = 565 men underwent mpMRI-targeted prostate biopsy, and the diagnostic performance of risk calculators (RCs), mpMRI alone, and clinical measures were compared using receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). Subgroups were stratified based on mpMRI findings and quality.

RESULTS

csPCa was detected in 56.3%. PI-RADS score achieved the highest area under the curve (AUC) when comparing univariate risk models (AUC 0.82, p < 0.001). Multivariate RCs showed only marginal improvement in csPCa detection compared to PI-RADS score alone, with just one of four RCs showing significant superiority. In mpMRI-negative cases, the non-MRI-based RC performed best (AUC 0.80, p = 0.016), with the potential to spare biopsies for 23%. PSA-density and multivariate RCs demonstrated comparable performance for PI-RADS 3 constellation (AUC 0.65 vs. 0.60-0.65, p > 0.5; saved biopsies 16%). In men with suspicious mpMRI, both mpMRI-based RCs and the PI-RADS score predicted csPCa excellently (AUC 0.82-0.79 vs. 0.80, p > 0.05), highlighting superior performance compared to non-MRI-based models (all p < 0.002). Quality-assured imaging consistently improved csPCa risk stratification across all subgroups.

CONCLUSION

In tertiary centers serving a high-risk population, high-quality mpMRI provides a simple yet effective way to assess the risk of csPCa. Using multivariate RCs reduces multiple biopsies, especially in mpMRI-negative and PI-RADS 3 constellation.

Key learnings from concordant systematic biopsies in prostate-specific membrane antigen positron emission tomography/computed tomography-guided prostate biopsies: Enhancing targeting accuracy

BACKGROUND

Prostate cancer (PCa) diagnosis and staging have evolved with the advent of 68Ga-Prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT). This study investigates the role of complementary systematic biopsies (SB) during PSMA-PET/CT-guided targeted prostate biopsies (PET-TB) for PCa detection, grading, and distribution. We address the uncertainty surrounding the necessity of SB in conjunction with PET-TB.

METHODS

We analyzed PCa grading and distribution in 30 men who underwent PET-TB and SB because of contraindication to magnetic resonance imaging or high clinical suspicion of PCa. Tumor distribution was assessed in relation to the PET-highlighted lesions. Standardized reporting schemes, encompassing SUVmax , PRIMARY score, and miTNM classification, were evaluated.

RESULTS

80% of patients were diagnosed with PCa, with 70% classified as clinically significant (csPCa). SB detected more csPCa cases than PET-TB, but the differences were not statistically significant. Discordant results were observed in 25% of cases, where SB outperformed PET-TB. Spatial analysis revealed that tumor-bearing cores from SB were often located in close proximity to the PET-highlighted region. Reporting schemes showed potential for csPCa detection with significantly increased SUVmax in csPCA patients. Subsequent follow-up data underscored the importance of SB in precise PCa grading and staging.

CONCLUSIONS

While PET-TB can simplify prostate biopsy and reduce invasiveness by core number, SB cannot be omitted yet due to potential PET-TB targeting errors. Factors such as limited spatial resolution and fusion inaccuracies contribute to the need for SB. Standardization in reporting schemes currently cannot compensate for targeting errors highlighting the need for refinement.

Detecting MRI-Invisible Prostate Cancers Using a Weakly Supervised Deep Learning Model

Background

MRI is an important tool for accurate detection and targeted biopsy of prostate lesions. However, the imaging appearances of some prostate cancers are similar to those of the surrounding normal tissue on MRI, which are referred to as MRI-invisible prostate cancers (MIPCas). The detection of MIPCas remains challenging and requires extensive systematic biopsy for identification. In this study, we developed a weakly supervised UNet (WSUNet) to detect MIPCas.

Methods

The study included 777 patients (training set: 600; testing set: 177), all of them underwent comprehensive prostate biopsies using an MRI-ultrasound fusion system. MIPCas were identified in MRI based on the Gleason grade (≥7) from known systematic biopsy results.

Results

The WSUNet model underwent validation through systematic biopsy in the testing set with an AUC of 0.764 (95% CI: 0.728-0.798). Furthermore, WSUNet exhibited a statistically significant precision improvement of 91.3% (p < 0.01) over conventional systematic biopsy methods in the testing set. This improvement resulted in a substantial 47.6% (p < 0.01) decrease in unnecessary biopsy needles, while maintaining the same number of positively identified cores as in the original systematic biopsy.

Conclusions

In conclusion, the proposed WSUNet could effectively detect MIPCas, thereby reducing unnecessary biopsies.

Do we need MRI in all biopsy naïve patients? A multicenter cohort analysis

PURPOSE

The combined approach (CB) of magnetic resonance imaging (MRI)-guided biopsy (TB) and systematic biopsy (SB) is strongly recommended based on numerous studies in biopsy naïve men with suspicion of clinically significant prostate cancer (csPCA). However, the unbalanced accessibility of MRI, challenges related to reimbursement and the scarcity of specialized medical practitioners continue to impede a widespread implementation. Therefore, our objective was to determine a subset of men that could undergo SB without an increased risk of underdiagnosis at reduced expenses.

METHODS

A multicenter analysis of 2714 men with confirmed PCA and suspicious MRI who underwent CB were enrolled. Cancer detection rates were compared between the different biopsy routes SB, TB and CB using McNemar paired test. Additionally, Gleason grade up- and down-grading was determined.

RESULTS

CB detected more csPCA than TB and SB (p < 0.001), irrespective of MRI findings or biopsy route (transperineal vs. transrectal). Thereby, single biopsy approaches misgraded > 50% of csPCA. TB showed higher diagnostic efficiency, defined as csPCA detection per biopsy core than CB and SB (p < 0.001). For patients with abnormal DRE and PSA levels > 12.5 ng/ml, PSAD > 0.35 ng/ml/cm3, or > 75 years, SB and CB showed similar csPCA detection rates.

CONCLUSION

Conducting CB provides the highest level of diagnostic certainty and minimizes the risk of underdiagnosis in almost all biopsy-naive men. However, in patients with suspicious DRE and high PSA levels, PSAD, or advanced age solely using SB leads to similar csPCA detection rates. Thus, a reduced biopsy protocol may be considered for these men in case resources are limited.

AI-predicted mpMRI image features for the prediction of clinically significant prostate cancer

PURPOSE

To evaluate the feasibility of using mpMRI image features predicted by AI algorithms in the prediction of clinically significant prostate cancer (csPCa).

MATERIALS AND METHODS

This study analyzed patients who underwent prostate mpMRI and radical prostatectomy (RP) at the Affiliated Hospital of Jiaxing University between November 2017 and December 2022. The clinical data collected included age, serum prostate-specific antigen (PSA), and biopsy pathology. The reference standard was the prostatectomy pathology, and a Gleason Score (GS) of 3 + 3 = 6 was considered non-clinically significant prostate cancer (non-csPCa), while a GS ≥ 3 + 4 was considered csPCa. A pre-trained AI algorithm was used to extract the lesion on mpMRI, and the image features of the lesion and the prostate gland were analyzed. Two logistic regression models were developed to predict csPCa: an MR model and a combined model. The MR model used age, PSA, PSA density (PSAD), and the AI-predicted MR image features as predictor variables. The combined model used biopsy pathology and the aforementioned variables as predictor variables. The model's effectiveness was evaluated by comparing it to biopsy pathology using the area under the curve (AUC) of receiver operation characteristic (ROC) analysis.

RESULTS

A total of 315 eligible patients were enrolled with an average age of 70.8 ± 5.9. Based on RP pathology, 18 had non-csPCa, and 297 had csPCa. PSA, PSAD, biopsy pathology, and ADC value of the prostate outside the lesion (ADCprostate) varied significantly across different ISUP grade groups of RP pathology (P < 0.001). Other clinical variables and image features did not vary significantly across different ISUP grade groups (P > 0.05). The MR model included PSAD, the ratio of ADC value between the lesion and the prostate outside the lesion (ADClesion/prostate), the signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate), and the ratio of DWIlesion/prostate to ADClesion/prostate. The combined model included biopsy pathology, ADClesion/prostate, mean signal intensity of the lesion on DWI (DWIlesion), DWI signal intensity of the prostate outside the lesion (DWIprostate), and signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate). The AUC of the MR model (0.830, 95% CI 0.743, 0.916) was not significantly different from that of biopsy pathology (0.820, 95% CI 0.728, 0.912, P = 0.884). The AUC of the combined model (0.915, 95% CI 0.849, 0.980) was higher than that of the biopsy pathology (P = 0.042) and MR model (P = 0.031).

CONCLUSION

The aggressiveness of prostate cancer can be effectively predicted using AI-extracted image features from mpMRI images, similar to biopsy pathology. The prediction accuracy was improved by combining the AI-extracted mpMRI image features with biopsy pathology, surpassing the performance of biopsy pathology alone.