Prostate MRI added to CAPRA, MSKCC and Partin cancer nomograms significantly enhances the prediction of adverse findings and biochemical recurrence after radical prostatectomy

Prostate magnetic resonance imaging to predict grade concordance, extra prostatic extension, and biochemical recurrence after radical prostatectomy

OBJECTIVES

To investigate whether preoperative prostate MRI findings predicted biopsy to radical prostate (RP) grade group concordance, presence of extraprostatic extension (EPE), and biochemical recurrence (BCR) after RP.

MATERIAL AND METHODS

We conducted a multi-institutional study (tertiary academic center and community practice) including patients who underwent RP (2014-2021) with preoperative MRI. Grade concordance for systematic, targeted, and combined prostate biopsy was compared to RP. Concordances were also compared for a contemporaneous RP cohort without prebiopsy MRI (No MRI cohort). We assessed association of extracapsular extension on MRI (MRI-ECE) with EPE and BCR after RP.

RESULTS

Among 768 men, concordance between biopsy and RP was 65.7% for combined, 58.3% for targeted, and 44.7% for systematic biopsy (P < 0.001). There was no difference in upgrading, concordance, and downgrading compared to 1014 men in the No MRI cohort (P = 0.6). Combined biopsy decreased upgrading to Grade Group ≥3 by 9.2%. EPE after RP was present in 292/768 (38%). MRI-ECE had 56% sensitivity, 74% specificity, 57% positive predictive value, and 73% negative predictive value. MRI-ECE was associated with EPE (OR: 2.25, P < 0.001) and BCR (HR: 1.77, P = 0.006). An MRI-based model improved EPE prediction in the development cohort (AUC 0.80) compared to a traditional nomogram but failed external validation (AUC 0.68).

CONCLUSIONS

Preoperative MRI findings predicted grade concordance, presence of EPE, and risk of BCR after RP. Variability in MRI-ECE interpretation limited generalizability of models to predict EPE indicating a need for more standardized reporting to increase clinical utility.

Variation in Prostate Magnetic Resonance Imaging Performance: Data from the Prostate Biopsy Collaborative Group

BACKGROUND AND OBJECTIVE

The quality and reporting of prostate magnetic resonance imaging (MRI) are operator dependent, leading to variations in estimates such as positive predictive value across sites. This impacts patient counseling, risk modeling, and risk calculators. This study assessed variation in Prostate Imaging Reporting and Data System (PI-RADS) score classification and subsequent probability of grade group (GG) ≥2 + prostate cancer.

METHODS

Data from the Prostate Biopsy Collaborative Group, including multiple sites in North America, Europe, and Asia Pacific, were analyzed. Patients underwent multiparametric MRI (mpMRI) of the prostate followed by prostate biopsy during the years 2010-2023. Only those with MRI-targeted biopsy and PI-RADS score ≥3 were included. The risk of being assigned PI-RADS 4 or 5 and risk of GG ≥2 disease for these scores were estimated using logistic regression.

KEY FINDINGS AND LIMITATIONS

The cohort included 7325 biopsies from 7320 unique patients from 13 sites. A two-fold variation in the probability of PI-RADS 4 or 5 assignment across sites persisted even after adjustment for patient risk (heterogeneity p < 0.001 for both). There were significant differences in the absolute risk of GG ≥2 disease for PI-RADS 4 and 5 (heterogeneity p < 0.001 for both), varying between 23% and 68% and between 49% and 87%, respectively. The use of prostate biopsy as a reference standard has limitations but reflects typical usage of mpMRI in clinical practice.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The probability of being assigned PI-RADS 4 or 5 and subsequent detection of GG ≥2 disease varies widely between institutions. This impacts counseling, risk stratification, and clinical practice, necessitating better standardization in the performance and interpretation of mpMRI.

Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review

Background/Objectives: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a significant predictor of distal metastases and mortality in prostate cancer (PCa) patients. This systematic review aims to evaluate the accuracy of artificial intelligence (AI) in predicting BCR post-RP. Methods: Adhering to PRISMA guidelines, a comprehensive literature search was conducted across Medline, Embase, Web of Science, and IEEE Xplore. Studies were included if they utilised AI to predict BCR in patients post-RP. Studies involving patients who underwent radiotherapy or salvage RP were excluded. This systematic review was registered on PROSPERO (International prospective register of systematic reviews) under the ID CRD42023482392. Results: After screening 9764 articles, 24 met the inclusion criteria. The included studies involved 27,216 patients, of whom 7267 developed BCR. AI algorithms developed using radiological parameters demonstrated higher predictive accuracy (median AUROC of 0.90) compared to algorithms based solely on pathological variables (median AUROC of 0.74) or clinicopathological variables (median AUROC of 0.81). According to the Prediction Model Risk of Bias Assessment Tool (PROBAST), the overall risk of bias was unclear in three studies due to ambiguous inclusion criteria and the exclusion of many patients because of missing follow-up data. In seven studies, the developed AI outperformed or was at least equivocal to traditional methods of BCR prediction. Conclusions: AI shows promise in predicting BCR post-RP, particularly when radiological data were used in its development. However, the significant variability in AI performance and study methodologies highlights the need for larger, standardised prospective studies with external validation prior to clinical application.

Association between mpMRI detected tumor apparent diffusion coefficient and 5-year biochemical recurrence risk after radical prostatectomy

PURPOSE

To assess the ability of tumor apparent diffusion coefficient (ADC) values obtained from multiparametric magnetic resonance imaging (mpMRI) to predict the risk of 5-year biochemical recurrence (BCR) after radical prostatectomy (RP).

MATERIALS AND METHODS

This retrospective analysis included 1207 peripheral and 232 non-peripheral zone prostate cancer (PCa) patients who underwent mpMRI before RP (2012-2015), with the outcome of interest being 5-year BCR. ADC was evaluated as a continuous variable and as categories: low (< 850 µm2/s), intermediate (850-1100 µm2/s), and high (> 1100 µm2/s). Kaplan-Meier curves with log-rank testing of BCR-free survival, multivariable Cox proportional hazard regression models were formed to estimate the risk of BCR.

RESULTS

Among the 1439 males with median age 63 (± 7) years, the median follow-up was 59 months, and 306 (25%) patients experienced BCR. Peripheral zone PCa patients with BCR had lower tumor ADC values than those without BCR (874 versus 1025 µm2/s, p < 0.001). Five-year BCR-free survival rates were 52.3%, 74.4%, and 87% for patients in the low, intermediate, and high ADC value categories, respectively (p < 0.0001). Lower ADC was associated with BCR, both as continuously coded variable (HR: 5.35; p < 0.001) and as ADC categories (intermediate versus high ADC-HR: 1.56, p = 0.017; low vs. high ADC-HR; 2.36, p < 0.001). In the non-peripheral zone PCa patients, no association between ADC and BCR was observed.

CONCLUSION

Tumor ADC values and categories were found to be predictive of the 5-year BCR risk after RP in patients with peripheral zone PCa and may serve as a prognostic biomarker.

Histomic and transcriptomic features of MRI-visible and invisible clinically significant prostate cancers are associated with prognosis

Magnetic resonance imaging (MRI) is increasingly used to triage patients for prostate biopsy. However, 9% to 24% of clinically significant (cs) prostate cancers (PCas) are not visible in MRI. We aimed to identify histomic and transcriptomic determinants of MRI visibility and their association to metastasis, and PCa-specific death (PCSD). We studied 45 radical prostatectomy-treated patients with csPCa (grade group [GG]2-3), including 30 with MRI-visible and 15 with MRI-invisible lesions, and 18 men without PCa. First, histological composition was quantified. Next, transcriptomic profiling was performed using NanoString technology. MRI visibility-associated differentially expressed genes (DEGs) and Reactome pathways were identified. MRI visibility was classified using publicly available genes in MSK-IMPACT and Decipher, Oncotype DX, and Prolaris. Finally, DEGs and clinical parameters were used to classify metastasis and PCSD in an external cohort, which included 76 patients with metastatic GG2-4 PCa, and 84 baseline-matched controls without progression. Luminal area was lower in MRI-visible than invisible lesions and low luminal area was associated with short metastasis-free and PCa-specific survival. We identified 67 DEGs, eight of which were associated with survival. Cell division, inflammation and transcriptional regulation pathways were upregulated in MRI-visible csPCas. Genes in Decipher, Oncotype DX and MSK-IMPACT performed well in classifying MRI visibility (AUC = 0.86-0.94). DEGs improved classification of metastasis (AUC = 0.69) and PCSD (AUC = 0.68) over clinical parameters. Our data reveals that MRI-visible csPCas harbor more aggressive histomic and transcriptomic features than MRI-invisible csPCas. Thus, targeted biopsy of visible lesions may be sufficient for risk stratification in patients with a positive MRI.

ACR Appropriateness Criteria® Pretreatment Detection, Surveillance, and Staging of Prostate Cancer: 2022 Update

Prostate cancer is second leading cause of death from malignancy after lung cancer in American men. The primary goal during pretreatment evaluation of prostate cancer is disease detection, localization, establishing disease extent (both local and distant), and evaluating aggressiveness, which are the driving factors of patient outcomes such as recurrence and survival. Prostate cancer is typically diagnosed after the recognizing elevated serum prostate-specific antigen level or abnormal digital rectal examination. Tissue diagnosis is obtained by transrectal ultrasound-guided biopsy or MRI-targeted biopsy, commonly with multiparametric MRI without or with intravenous contrast, which has recently been established as standard of care for detecting, localizing, and assessing local extent of prostate cancer. Although bone scintigraphy and CT are still typically used to detect bone and nodal metastases in patients with intermediate- or high-risk prostate cancer, novel advanced imaging modalities including prostatespecific membrane antigen PET/CT and whole-body MRI are being more frequently utilized for this purpose with improved detection rates. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Added value of shear-wave elastography in the prediction of extracapsular extension and seminal vesicle invasion before radical prostatectomy

The purpose of this study was to analyze the value of transrectal shear-wave elastography (SWE) in combination with multivariable tools for predicting adverse pathological features before radical prostatectomy (RP). Preoperative clinicopathological variables, multiparametric magnetic resonance imaging (mp-MRI) manifestations, and the maximum elastic value of the prostate (Emax) on SWE were retrospectively collected. The accuracy of SWE for predicting adverse pathological features was evaluated based on postoperative pathology, and parameters with statistical significance were selected. The diagnostic performance of various models, including preoperative clinicopathological variables (model 1), preoperative clinicopathological variables + mp-MRI (model 2), and preoperative clinicopathological variables + mp-MRI + SWE (model 3), was evaluated with area under the receiver operator characteristic curve (AUC) analysis. Emax was significantly higher in prostate cancer with extracapsular extension (ECE) or seminal vesicle invasion (SVI) with both P < 0.001. The optimal cutoff Emax values for ECE and SVI were 60.45 kPa and 81.55 kPa, respectively. Inclusion of mp-MRI and SWE improved discrimination by clinical models for ECE (model 2 vs model 1, P = 0.031; model 3 vs model 1, P = 0.002; model 3 vs model 2, P = 0.018) and SVI (model 2 vs model 1, P = 0.147; model 3 vs model 1, P = 0.037; model 3 vs model 2, P = 0.134). SWE is valuable for identifying patients at high risk of adverse pathology.

A Review of Modern Imaging Landscape for Prostate Cancer: A Comprehensive Clinical Guide

The development of prostate cancer imaging is rapidly evolving, with many changes to the way patients are diagnosed, staged, and monitored for recurrence following treatment. New developments, including the potential role of imaging in screening and the combined diagnostic and therapeutic applications in the field of theranostics, are underway. In this paper, we aim to outline the current landscape in prostate cancer imaging and look to the future at the potential modalities and applications to come.

Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population

Introduction

Pre-treatment risk and prognostic groups are the cornerstone for deciding management in non-metastatic prostate cancer. All however, were developed in the pre-MRI era. Here we compared categorisation of cancers using either only clinical parameters or with MRI enhanced information in men referred for suspected prostate cancer from an unscreened population.

Patient and methods

Data from men referred from primary care to our diagnostic service and with both clinical (digital rectal examination [DRE] and systematic biopsies) and MRI enhanced attributes (MRI stage and combined systematic/targeted biopsies) were used for this study. Clinical vs MRI data were contrasted for clinico-pathological and risk group re-distribution using the European Association of Urology (EAU), American Urological Association (AUA) and UK National Institute for Health Care Excellence (NICE) Cambridge Prognostic Group (CPG) models. Differences were retrofitted to a population cohort with long-term prostate cancer mortality (PCM) outcomes to simulate impact on model performance. We further contrasted individualised overall survival (OS) predictions using the Predict Prostate algorithm.

Results

Data from 370 men were included (median age 66y). Pre-biopsy MRI stage reassignments occurred in 7.8% (versus DRE). Image-guided biopsies increased Grade Group 2 and ≥ Grade Group 3 assignments in 2.7% and 2.9% respectively. The main change in risk groups was more high-risk cancers (6.2% increase in the EAU and AUA system, 4.3% increase in CPG4 and 1.9% CPG5). When extrapolated to a historical population-based cohort (n = 10,139) the redistribution resulted in generally lower concordance indices for PCM. The 5-tier NICE-CPG system outperformed the 4-tier AUA and 3-tier EAU models (C Index 0.70 versus 0.65 and 0.64). Using an individualised prognostic model, changes in predicted OS were small (median difference 1% and 2% at 10- and 15-years’ respectively). Similarly, estimated treatment survival benefit changes were minimal (1% at both 10- and 15-years’ time frame).

Conclusion

MRI guided diagnostics does change pre-treatment risk groups assignments but the overall prognostic impact appears modest in men referred from unscreened populations. Particularly, when using more granular tiers or individualised prognostic models. Existing risk and prognostic models can continue to be used to counsel men about treatment option until long term survival outcomes are available.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09955-w.