Using prognosis to guide inclusion criteria, define standardised endpoints and stratify follow-up in active surveillance for prostate cancer

The 5-year results of the Stratified Cancer Active Surveillance programme for men with prostate cancer

OBJECTIVES

To report 5-year outcomes from the STRATified CANcer Surveillance (STRATCANS) programme based on progression risks using National Institute for Health and Clinical Excellence (NICE) Cambridge Prognostic Group (CPG) at diagnosis, prostate specific antigen density and magnetic resonance imaging (MRI) visibility.

PATIENTS AND METHODS

Men with CPG1 and CPG2 disease selecting active surveillance (AS) were included into STRATCANS and allocated to one of three increasing follow-up intensities. Outcome measures were: (i) treatment for CPG≥3 progression (main outcome), (ii) any treatment, (iii) conversion to watchful waiting (WW), (iv) patient self-attrition, and (v) mortality.

RESULTS

A total of 297 men (median age 66.0 years) were reviewed. The median (interquartile range, mean) follow-up for men still on AS was 4.9 (2.7-7.6, 5.3) years. In the cohort, 38.0% were CPG2 and 25.0% Grade Group (GG) 2 at AS entry. Overall, 214/297 (72.1%) remained treatment free: 158 (53.1%) were still on AS, 17 (5.7%) died of other causes, and 39 (13.1%) progressed to WW/discharge. Only 10 (3.4%) left AS from anxiety. There were no cancer deaths or metastatic events. In all, 80 men (26.9%) converted to treatment due to biopsy/MRI progression but only 35 (11.7%) of these reached CPG≥3 disease. Treatment for CPG≥3 occurred in 7.6% of CPG1 and 18.5% of CPG2 disease and 9.9% of GG1 and 17.5% of GG2 disease. By STRATCANS tier, treatment for CPG≥3 disease was 4.7% in STRATCANS 1, 12.9% in STRATCANS 2, and 27.4% in STRATCANS 3 (P < 0.001). STRATCANS had an area under the curve (AUC) of 0.74 for predicting CPG≥3 progression out-performing stratification by GG (AUC 0.64), CPG (0.69) and Likert score (0.51) alone or a combination of MRI visibility and GG (0.64). Longitudinal data have allowed further refinement of the STRATCANS schedule.

CONCLUSIONS

The STRATCANS 5-year outcomes demonstrate that a simple risk stratified surveillance using a prognostically meaningful endpoint is safe, durable, has low treatment rates, high patient compliance and appropriately tailors monitoring based on risks of progression. A website and implementation toolkit are now available.

A Personalized, Risk-Based Approach to Active Surveillance for Prostate Cancer with Takeaways from Broader Oncology Practices: A Mixed Methods Review

Background/Objectives: To summarize the current state of knowledge regarding personalized, risk-based approaches in active surveillance (AS) for prostate cancer (PCa) and to explore the lessons learned from AS practices in other types of cancer. Methods: This mixed methods review combined a systematic review and a narrative review. The systematic review was conducted according to the Preferred Reporting Items for Systematic rviews and Meta-Analyses (PRISMA) guidelines, with searches performed in the Medline, Embase, Web of Science, Cochrane Central Register of Controlled Trials, and Google Scholar databases. Only studies evaluating personalized, risk-based AS programs for PCa were included. The narrative review focused on AS approaches in other solid tumors (thyroid, breast, kidney, and bladder cancer) to contextualize the findings and highlight lessons learned. Results: After screening 3137 articles, 9 were suitable for inclusion, describing the following four unique risk-based AS tools: PRIAS, Johns Hopkins, Canary PASS, and STRATCANS. These models were developed using data from men with low-risk (Grade Group 1) disease, with little to no magnetic resonance imaging (MRI) data. They used patient information such as (repeated) prostate-specific antigen (PSA) measurements and biopsy results to predict the risk of upgrading at the next biopsy or at radical prostatectomy, or to assign a patient to a pre-defined risk category with a corresponding pre-defined follow-up (FU) regimen. Performance was moderate across models, with the area under the curve/concordance index values ranging from 0.58 to 0.85 and calibration was generally good. The PRIAS, Canary PASS, and STRATCANS models demonstrated the benefits of less burdensome biopsies, clinic visits, and MRIs during FU when used, compared to current one-size-fits-all practices. Although little is known about risk-based AS in thyroid, breast, kidney, and bladder cancer, learning from their current practices could further refine patient selection, streamline monitoring protocols, and address adoption barriers, improving AS's overall effectiveness in PCa management. Conclusions: Personalized, risk-based AS models allow for a reduction in the FU burden for men at low risk of progression while maintaining sensitive FU visits for those at higher risk. The comparatively limited evidence and practice of risk-based AS in other cancer types highlight the advanced state of AS in PCa.

Prostate cancer theragnostics biomarkers: An update

Biomarkers are molecules such as proteins, genes, or other substances that may be tested to determine the stage of the tumor in a patient. The role of prostate cancer biomarkers is pivotal and the combination of prostate cancer immunotherapy with efficient biomarkers has emerged as a beneficial treatment strategy and its use has increased rapidly. The two primary objectives of this current prostate cancer early detection programs were recognizing non-symptomatic individuals with prostate cancer requiring prostatic core biopsy and identifying men with prostate cancer who might benefit from definitive medical treatment. The progress that has been made so far in the identification of the biomarkers that can be used for the classification, prediction and prognostication of prostate cancer, and as major targets for its clinical intervention has been well summarized in this review.

The efficacy of different biomarkers and endpoints to refine referrals for suspected prostate cancer: the TARGET study (Tiered integrAted tests for eaRly diaGnosis of clinically significant ProstatE Tumours)

BACKGROUND

The majority of men referred with a raised PSA for suspected prostate cancer will receive unnecessary tertiary investigations including MRI and biopsy. Here, we compared different types of biomarkers to refine tertiary referrals and when different definitions of clinically significant cancer were used.

METHODS

Data and samples from 798 men referred for a raised PSA (≥ 3 ng/mL) and investigated through an MRI-guided biopsy pathway were accessed for this study. Bloods were acquired pre-biopsy for liquid biomarkers and germline DNA. Variables explored included PSA + Age (base model), free/total PSA (FTPSA), Prostate Health Index (phi), PSA density (PSAd), polygenic risk score (PRS) and MRI (≥ LIKERT 3). Different diagnostic endpoints for significant cancer (≥ grade group 2 [GG2], ≥ GG3, ≥ Cambridge Prognostic Group 2 [CPG2], ≥ CPG3) were tested. The added value of each biomarker to the base model was evaluated using logistic regression models, AUC and decision curve analysis (DCA) plots.

RESULTS

The median age and PSA was 65 years and 7.13 ng/mL respectively. Depending on definition of clinical significance, ≥ grade group 2 (GG2) was detected in 57.0% (455/798), ≥ GG3 in 27.5% (220/798), ≥ CPG2 in 61.6% (492/798) and ≥ CPG3 in 42.6% (340/798). In the pre-MRI context, the PSA + Age (base model) AUC for prediction of ≥ GG2, ≥ GG3, ≥ CPG2 and ≥ CPG3 was 0.66, 0.68, 0.70 and 0.75 respectively. Adding phi and PSAd to base model improved performance across all diagnostic endpoints but was notably better when the composite CPG prognostic score was used: AUC 0.82, 0.82, 0.83, 0.82 and AUC 0.74, 0.73, 0.79, 0.79 respectively. In contrast, neither FTPSA or PRS scores improved performance especially in detection of ≥ GG3 and ≥ CPG3 disease. Combining biomarkers did not alter results. Models using phi and PSAd post-MRI also improved performances but again benefit varied with diagnostic endpoint. In DCA analysis, models which incorporated PSAd and phi in particular were effective at reducing use of MRI and/or biopsies especially for ≥ CPG3 disease.

CONCLUSION

Incorporating phi or PSAd can refine and tier who is referred for tertiary imaging and/or biopsy after a raised PSA test. Incremental value however varied depending on the definition of clinical significance and was particularly useful when composite prognostic endpoints are used.

The PRECISE Recommendations for Prostate MRI in Patients on Active Surveillance for Prostate Cancer: A Critical Review

The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations were published in 2016 to standardize the reporting of MRI examinations performed to assess for disease progression in patients on active surveillance for prostate cancer. Although a limited number of studies have reported outcomes from use of PRECISE in clinical practice, the available studies have demonstrated PRECISE to have high pooled NPV but low pooled PPV for predicting progression. Our experience in using PRECISE in clinical practice at two teaching hospitals has highlighted issues with its application and areas requiring clarification. This Clinical Perspective critically appraises PRECISE on the basis of this experience, focusing on the system's key advantages and disadvantages and exploring potential changes to improve the system's utility. These changes include consideration of image quality when applying PRECISE scoring, incorporation of quantitative thresholds for disease progression, adoption of a PRECISE 3F sub-category for progression not qualifying as substantial, and comparisons with both the baseline and most recent prior examinations. Items requiring clarification include derivation of a patient-level score in patients with multiple lesions, intended application of PRECISE score 5 (i.e., if requiring development of disease that is no longer organ-confined), and categorization of new lesions in patients with prior MRI-invisible disease.

Re-evaluating the diagnostic efficacy of PSA as a referral test to detect clinically significant prostate cancer in contemporary MRI-based image-guided biopsy pathways

Introduction

Modern image-guided biopsy pathways at diagnostic centres have greatly refined the investigations of men referred with suspected prostate cancer. However, the referral criteria from primary care are still based on historical prostate-specific antigen (PSA) cut-offs and age-referenced thresholds. Here, we tested whether better contemporary pathways and biopsy methods had improved the predictive utility value of PSA referral thresholds.

Methods

PSA referral thresholds, age-referenced ranges and PSA density (PSAd) were assessed for positive predictive value (PPV) in detection of clinically significant prostate cancer (csPCa - histological ⩾ Grade Group 2). Data were analysed from men referred to three diagnostics centres who used multi-parametric magnetic resonance imaging (mpMRI)-guided prostate biopsies for disease characterisation. Findings were validated in a separate multicentre cohort. Results: Data from 2767 men were included in this study. The median age, PSA and PSAd were 66.4 years, 7.3 ng/mL and 0.1 ng/mL2, respectively. Biopsy detected csPCa was found in 38.7%. The overall area under the curve (AUC) for PSA was 0.68 which is similar to historical performance. A PSA threshold of ⩾ 3 ng/mL had a PPV of 40.3%, but this was age dependent (PPV: 24.8%, 32.7% and 56.8% in men 50-59 years, 60-69 years and ⩾ 70 years, respectively). Different PSA cut-offs and age-reference ranges failed to demonstrate better performance. PSAd demonstrated improved AUC (0.78 vs 0.68, p < 0.0001) and improved PPV compared to PSA. A PSAd of ⩾ 0.10 had a PPV of 48.2% and similar negative predictive value (NPV) to PSA ⩾ 3 ng/mL and out-performed PSA age-reference ranges. This improved performance was recapitulated in a separate multi-centre cohort (n = 541).

Conclusion

The introduction of MRI-based image-guided biopsy pathways does not appear to have altered PSA diagnostic test characteristics to positively detect csPCa. We find no added value to PSA age-referenced ranges, while PSAd offers better PPV and the potential for a single clinically useful threshold (⩾0.10) for all age groups.

Level of evidence

IV.

Prospective Implementation and Early Outcomes of a Risk-stratified Prostate Cancer Active Surveillance Follow-up Protocol

Background

Active surveillance (AS) is a major management option for men with early prostate cancer. Current guidelines however advocate identical AS follow-up for all without considering different disease trajectories. We previously proposed a pragmatic three-tier STRATified CANcer Surveillance (STRATCANS) follow-up strategy based on different progression risks from clinic-pathological and imaging features.

Objective

To report early outcomes from the implementation of the STRATCANS protocol in our centre.

Design setting and participants

Men on AS were enrolled into a prospective stratified follow-up programme.

Intervention

Three tiers of increasing follow-up intensity based on National Institute for Health and Care Excellence (NICE): Cambridge Prognostic Group (CPG) 1 or 2, prostate-specific antigen density, and magnetic resonance imaging (MRI) Likert score at entry.

Outcome measurements and statistical analysis

Rates of progression to CPG ≥3, any pathological progression, AS attrition, and patient choice for treatment were assessed. Differences in progression were compared with chi-square statistics.

Results and limitations

Data from 156 men (median age 67.3 yr) were analysed. Of these, 38.4% had CPG2 disease and 27.5% had grade group 2 disease at diagnosis. The median time on AS was 4 yr (interquartile range 3.2-4.9) and 1.5 yr on STRATCANS. Overall, 135/156 (86.5%) men remained on AS or converted to watchful waiting and 6/156 (3.8%) stopped AS by choice by the end of the evaluation period. Of the 156 patients, 66 (42.3%) were allocated to STRATCANS 1 (least intense follow-up), 61 (39.1%) to STRATCANS 2, and 29 (18.6%) to STRATCANS 3 (highest intensity). By increasing STRATCANS tier, progression rates to CPG ≥3 and any progression events were 0% and 4.6%, 3.4% and 8.6%, and 7.4% and 22.2%, respectively (p = 0.019). Modelling resource usage suggested potential reductions in appointments by 22% and MRI by 42% compared with current NICE guideline recommendations (first 12 months of AS). The study is limited by short follow-up, a relatively small cohort, and being single centre.

Conclusions

A simple risk-tiered AS strategy is possible with early outcomes supporting stratified follow-up intensity. STRATCANS implementation could de-escalate follow-up in men at a low risk of progression while husbanding resources for those who need closer follow-up.

Patient summary

We report a practical way to personalise follow-up for men on active surveillance for early prostate cancer. Our method may allow reductions in the follow-up burden for men at a low risk of disease change while maintaining vigilance for those at a higher risk.

Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance

Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease. Using leave-one-out cross-validation (LOOCV), we found that an LSTM-based model combining TSR and serial PSA density (AUC 0.86 [95% CI: 0.78-0.94]) significantly outperformed a model combining conventional delta-radiomics and delta-PSA density (0.75 [0.64-0.87]; p = 0.048) and achieved comparable performance to expert-performed serial MRI analysis using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) scoring system (0.84 [0.76-0.93]; p = 0.710). The proposed TSR framework, therefore, offers a feasible quantitative tool for standardising serial MRI assessment in PCa AS. It also presents a novel methodological approach to serial image analysis that can be used to support clinical decision-making in multiple scenarios, from continuous disease monitoring to treatment response evaluation. KEY POINTS: •LSTM RNN can be used to predict the outcome of PCa AS using time series changes in tumour-derived radiomic features and PSA density. •Using all available TSR features and serial PSA density yields a significantly better predictive performance compared to using just two time points within the delta-radiomics framework. •The concept of TSR can be applied to other clinical scenarios involving serial imaging, setting out a new field in AI-driven radiology research.

Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway

Multiparametric MRI of the prostate is now recommended as the initial diagnostic test for men presenting with suspected prostate cancer, with a negative MRI enabling safe avoidance of biopsy and a positive result enabling MRI-directed sampling of lesions. The diagnostic pathway consists of several steps, from initial patient presentation and preparation to performing and interpreting MRI, communicating the imaging findings, outlining the prostate and intra-prostatic target lesions, performing the biopsy and assessing the cores. Each component of this pathway requires experienced clinicians, optimized equipment, good inter-disciplinary communication between specialists, and standardized workflows in order to achieve the expected outcomes. Assessment of quality and mitigation measures are essential for the success of the MRI-directed prostate cancer diagnostic pathway. Quality assurance processes including Prostate Imaging-Reporting and Data System, template biopsy, and pathology guidelines help to minimize variation and ensure optimization of the diagnostic pathway. Quality control systems including the Prostate Imaging Quality scoring system, patient-level outcomes (such as Prostate Imaging-Reporting and Data System MRI score assignment and cancer detection rates), multidisciplinary meeting review and audits might also be used to provide consistency of outcomes and ensure that all the benefits of the MRI-directed pathway are achieved.

Development and External Validation of the STRATified CANcer Surveillance (STRATCANS) Multivariable Model for Predicting Progression in Men with Newly Diagnosed Prostate Cancer Starting Active Surveillance

For men with newly diagnosed prostate cancer, we aimed to develop and validate a model to predict the risk of progression on active surveillance (AS), which could inform more personalised AS strategies. In total, 883 men from 3 European centres were used for model development and internal validation, and 151 men from a fourth European centre were used for external validation. Men with Cambridge Prognostic Group (CPG) 1-2 disease at diagnosis were eligible. The endpoint was progression to the composite endpoint of CPG3 disease or worse (≥CPG3). Model performance at 4 years was evaluated through discrimination (C-index), calibration plots, and decision curve analysis. The final multivariable model incorporated prostate-specific antigen (PSA), Grade Group, magnetic resonance imaging (MRI) score (Prostate Imaging Reporting & Data System (PI-RADS) or Likert), and prostate volume. Calibration and discrimination were good in both internal validation (C-index 0.742, 95% CI 0.694-0.793) and external validation (C-index 0.845, 95% CI 0.712-0.958). In decision curve analysis, the model offered net benefit compared to a 'follow-all' strategy at risk thresholds of ≥0.08 and ≥0.04 in development and external validation, respectively. In conclusion, our model demonstrated good accuracy and clinical utility in predicting the progression on AS at 4 years post-diagnosis. Men with lower risk predictions could subsequently be offered less-intense surveillance. Further external validation in larger cohorts is now required.

Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer

Active Surveillance (AS) for prostate cancer is a management option that continually monitors early disease and considers intervention if progression occurs. A robust method to incorporate “live” updates of progression risk during follow-up has hitherto been lacking. To address this, we developed a deep learning-based individualised longitudinal survival model using Dynamic-DeepHit-Lite (DDHL) that learns data-driven distribution of time-to-event outcomes. Further refining outputs, we used a reinforcement learning approach (Actor-Critic) for temporal predictive clustering (AC-TPC) to discover groups with similar time-to-event outcomes to support clinical utility. We applied these methods to data from 585 men on AS with longitudinal and comprehensive follow-up (median 4.4 years). Time-dependent C-indices and Brier scores were calculated and compared to Cox regression and landmarking methods. Both Cox and DDHL models including only baseline variables showed comparable C-indices but the DDHL model performance improved with additional follow-up data. With 3 years of data collection and 3 years follow-up the DDHL model had a C-index of 0.79 (±0.11) compared to 0.70 (±0.15) for landmarking Cox and 0.67 (±0.09) for baseline Cox only. Model calibration was good across all models tested. The AC-TPC method further discovered 4 distinct outcome-related temporal clusters with distinct progression trajectories. Those in the lowest risk cluster had negligible progression risk while those in the highest cluster had a 50% risk of progression by 5 years. In summary, we report a novel machine learning approach to inform personalised follow-up during active surveillance which improves predictive power with increasing data input over time.

A Feasibility Study of the Therapeutic Response and Durability of Short-term Androgen-targeted Therapy in Early Prostate Cancer Managed with Surveillance: The Therapeutics in Active Prostate Surveillance (TAPS01) Study

Background

Active surveillance (AS) is a preferred management option for men with prostate cancer with favourable prognosis. However, nearly half of men on AS switch to treatment within 5 years, so therapeutic strategies to prevent or delay disease progression could be considered. The androgen receptor is the pre-eminent oncogenic driver in prostate cancer.

Objective

To explore image-based tumour responses and the patient impact of short-duration androgen-targeted therapy (ATT) to abrogate disease progression during AS.

Design, setting, and participants

Men on AS with Cambridge Prognostic Group 1 & 2 (low and favourable intermediate risk) prostate cancer and lesions visible on magnetic resonance imaging (MRI) were recruited to an open-label, single-centre, phase 2 feasibility study of short-term ATT (the TAPS01 study).

Intervention

Apalutamide 240 mg was administered for 90 days.

Outcome measurements and statistical analysis

MRI-measured tumour volume (TV), gland volume (GV), and the TV/GV ratio were calculated at baseline, at day 90 (end of treatment), and at 6- and 18-month follow-up. Quality of life metrics were measured at day 0, day 90, and 6 weeks after ATT.

Results and limitations

Eleven patients (40% of eligible men approached) agreed to participate, of whom nine completed treatment. At day 90, the median percentage reduction was −38.2% (range −51.8% to −23.5%) for GV, −54.2% (range −74.1% to −13.8%) for TV, and −27.2% (range −61.5% to −7.5%) for TV/GV (all p < 0.0001). At 6 mo, while GV had returned to baseline (p = 0.95) both TV (−31.9%; p = 0.0007) and TV/GV (−28.7%; p = 0.0009) remained significantly reduced. This reduction was sustained at 18 months (TV −18%, TV/GV −23.8%; p = 0.01). European Organization for Research and Treatment of Cancer QoL core 30-item questionnaire scores for global, physical, role, and social functioning decreased during treatment, but all were recovering by 6 weeks. EQ-VAS scores were unchanged compared to baseline.

Conclusions

TAPS01 has demonstrated feasibility and patient tolerability for short-term ATT in men on AS. Our data suggest a selective and durable antitumour effect in the short term and support a larger-scale randomised trial.

Patient summary

We investigated the feasibility of short-term treatment with an androgen inhibitor to prevent or delay disease progression for men on active surveillance for prostate cancer. Results for a small group of patients show that 90-day treatment led to a sustained decrease in tumour volume over 18 months. The findings warrant a larger clinical trial for this approach, which could allow patients to delay or even avoid longer-term active treatments.

Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression

OBJECTIVE

To analyse serial changes in MRI-derived tumour measurements and apparent diffusion coefficient (ADC) values in prostate cancer (PCa) patients on active surveillance (AS) with and without histopathological disease progression.

METHODS

This study included AS patients with biopsy-proven PCa with a minimum of two consecutive MR examinations and at least one repeat targeted biopsy. Tumour volumes, largest axial two-dimensional (2D) surface areas, and maximum diameters were measured on T2 weighted images (T2WI). ADC values were derived from the whole lesions, 2D areas, and small-volume regions of interest (ROIs) where tumours were most conspicuous. Areas under the ROC curve (AUCs) were calculated for combinations of T2WI and ADC parameters with optimal specificity and sensitivity.

RESULTS

60 patients (30 progressors and 30 non-progressors) were included. In progressors, T2WI-derived tumour volume, 2D surface area, and maximum tumour diameter had a median increase of +99.5%,+55.3%, and +21.7% compared to +29.2%,+8.1%, and +6.9% in non-progressors (p < 0.005 for all). Follow-up whole-volume and small-volume ROIs ADC values were significantly reduced in progressors (-11.7% and -9.5%) compared to non-progressors (-6.1% and -1.6%) (p < 0.05 for both). The combined AUC of a relative increase in maximum tumour diameter by 20% and reduction in small-volume ADC by 10% was 0.67.

CONCLUSION

AS patients show significant differences in tumour measurements and ADC values between those with and without histopathological disease progression.

ADVANCES IN KNOWLEDGE

This paper proposes specific clinical cut-offs for T2WI-derived maximum tumour diameter (+20%) and small-volume ADC (-10%) to predict histopathological PCa progression on AS and supplement subjective serial MRI assessment.

Integration of Prostate Biopsy Results with Pre-Biopsy Multiparametric Magnetic Resonance Imaging Findings Improves Local Staging of Prostate Cancer

PURPOSE

To assess the added value of histological information for local staging of prostate cancer (PCa) by comparing the accuracy of multiparametric MRI alone (mpMRI) and mpMRI with biopsy Gleason grade (mpMRI+Bx).

METHODS

133 consecutive patients who underwent preoperative 3T-MRI and subsequent radical prostatectomy for PCa were included in this single-centre retrospective study. mpMRI imaging was reviewed independently by two uroradiologists for the presence of extracapsular extension (ECE) and seminal vesicle invasion (SVI) on a 5-point Likert scale. For second reads, the radiologists received results of targeted fused MR/US biopsy (mpMRI+Bx) prior to re-staging.

RESULTS

The median patient age was 63 years (interquartile range (IQR) 58-67 years) and median PSA was 6.5 ng/mL (IQR 5.0-10.0 ng/mL). Extracapsular extension was present in 85/133 (63.9%) patients and SVI was present in 22/133 (16.5%) patients. For ECE prediction, mpMRI showed sensitivity and specificity of 63.5% and 81.3%, respectively, compared to 77.7% and 81.3% achieved by mpMRI+Bx. At an optimal cut-off value of Likert score ≥ 3, areas under the curves (AUCs) was .85 for mpMRI+Bx and .78 for mpMRI, P < .01. For SVI prediction, AUC was .95 for mpMRI+Bx compared to .92 for mpMRI; P = .20. Inter-reader agreement for ECE and SVI prediction was substantial for mpMRI (k range, .78-.79) and mpMRI+Bx (k range, .74-.79).

CONCLUSIONS

MpMRI+Bx showed superior diagnostic performance with an increased sensitivity for ECE prediction but no significant difference for SVI prediction. Inter-reader agreement was substantial for both protocols. Integration of biopsy information adds value when staging prostate mpMRI.

Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance

OBJECTIVES

To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS).

METHODS

The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test.

RESULTS

The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77).

CONCLUSIONS

PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients.

KEY POINTS

• The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T2-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481-0.743) to 0.75 (95% CI 0.64-0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.

MRI-derived PRECISE scores for predicting pathologically-confirmed radiological progression in prostate cancer patients on active surveillance

OBJECTIVES

To assess the predictive value and correlation to pathological progression of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) scoring system in the follow-up of prostate cancer (PCa) patients on active surveillance (AS).

METHODS

A total of 295 men enrolled on an AS programme between 2011 and 2018 were included. Baseline multiparametric magnetic resonance imaging (mpMRI) was performed at AS entry to guide biopsy. The follow-up mpMRI studies were prospectively reported by two sub-specialist uroradiologists with 10 years and 13 years of experience. PRECISE scores were dichotomized at the cut-off value of 4, and the sensitivity, specificity, positive predictive value and negative predictive value were calculated. Diagnostic performance was further quantified by using area under the receiver operating curve (AUC) which was based on the results of targeted MRI-US fusion biopsy. Univariate analysis using Cox regression was performed to assess which baseline clinical and mpMRI parameters were related to disease progression on AS.

RESULTS

Progression rate of the cohort was 13.9% (41/295) over a median follow-up of 52 months. With a cut-off value of category ≥ 4, the PRECISE scoring system showed sensitivity, specificity, PPV and NPV for predicting progression on AS of 0.76, 0.89, 0.52 and 0.96, respectively. The AUC was 0.82 (95% CI = 0.74-0.90). Prostate-specific antigen density (PSA-D), Likert lesion score and index lesion size were the only significant baseline predictors of progression (each p < 0.05).

CONCLUSION

The PRECISE scoring system showed good overall performance, and the high NPV may help limit the number of follow-up biopsies required in patients on AS.

KEY POINTS

• PRECISE scores 1-3 have high NPV which could reduce the need for re-biopsy during active surveillance. • PRECISE scores 4-5 have moderate PPV and should trigger either close monitoring or re-biopsy. • Three baseline predictors (PSA density, lesion size and Likert score) have a significant impact on the progression-free survival (PFS) time.

Discovery of PTN as a serum-based biomarker of pro-metastatic prostate cancer

Distinguishing clinically significant from indolent prostate cancer (PC) is a major clinical challenge. We utilised targeted protein biomarker discovery approach to identify biomarkers specific for pro-metastatic PC. Serum samples from the cancer-free group; Cambridge Prognostic Group 1 (CPG1, low risk); CPG5 (high risk) and metastatic disease were analysed using Olink Proteomics panels. Tissue validation was performed by immunohistochemistry in a radical prostatectomy cohort (n = 234). We discovered that nine proteins (pleiotrophin (PTN), MK, PVRL4, EPHA2, TFPI-2, hK11, SYND1, ANGPT2, and hK14) were elevated in metastatic PC patients when compared to other groups. PTN levels were increased in serum from men with CPG5 compared to benign and CPG1. High tissue PTN level was an independent predictor of biochemical recurrence and metastatic progression in low- and intermediate-grade disease. These findings suggest that PTN may represent a novel biomarker for the presence of poor prognosis local disease with the potential to metastasise warranting further investigation.

Update and optimization of active surveillance in prostate cancer in 2021

INTRODUCTION AND OBJECTIVES

Within the paradigm shift of the last decade in the management of prostate cancer (PCa), perhaps the most relevant event has been the emergence of active surveillance (AS) as a mandatory strategy in low-risk disease. We carry out a critical review of the clinical, pathological and radiological improvements that allow optimizing AS in 2021.

MATERIAL AND METHODS

Critical narrative review of the literature on improvement issues and controversial aspects of AS.

RESULTS

Adequate use of traditional criteria, optimized by enhanced biopsy and calculation of the prostate volume technique thanks to multiparametric magnetic resonance imaging (mpMRI) allow a better selection of patients for AS. This management should not be limited to patients under 60years of age, and patients with intermediate-risk PCa should be carefully selected to be included. Biopsies are still required in the follow-up, which can be personalized according to risk patterns. The pathologist must identify the cribriform or intraductal histology on biopsies in order to exclude these patients from AS, in the same way as with patients with alterations in DNA repair genes.

CONCLUSIONS

Controversial indications such as the inclusion of patients from intermediate-risk groups, or the transition to active treatment due to exclusive progression in tumor volume, should be further optimized. It is possible that the future competition of tissue biomarkers, the refinement of objective parameters of mpMRI and the validation of PSA kinetics calculators may sub-stratify risk groups.

Risk stratification for prostate cancer management: value of the Cambridge Prognostic Group classification for assessing treatment allocation

BACKGROUND

The five-tiered Cambridge Prognostic Group (CPG) classification is a better predictor of prostate cancer-specific mortality than the traditional three-tiered classification (low, intermediate, and high risk). We investigated radical treatment rates according to CPG in men diagnosed with non-metastatic prostate cancer in England between 2014 and 2017.

METHODS

Patients diagnosed with non-metastatic prostate cancer were identified from the National Prostate Cancer Audit database. Men were risk stratified according to the CPG classification. Risk ratios (RR) were estimated for undergoing radical treatment according to CPG and for receiving radiotherapy for those treated radically. Funnel plots were used to display variation in radical treatment rates across hospitals.

RESULTS

A total of 61,999 men were included with 10,963 (17.7%) in CPG1 (lowest risk group), 13,588 (21.9%) in CPG2, 9452 (15.2%) in CPG3, 12,831 (20.7%) in CPG4, and 15,165 (24.5%) in CPG5 (highest risk group). The proportion of men receiving radical treatment increased from 11.3% in CPG1 to 78.8% in CGP4, and 73.3% in CPG5. Men in CPG3 were more likely to receive radical treatment than men in CPG2 (66.3% versus 48.4%; adjusted RR 1.44; 95% CI 1.36-1.53; P < 0.001). Radically treated men in CPG3 were also more likely to receive radiotherapy than men in CPG2 (59.2% versus 43.9%; adjusted RR, 1.18; 95% CI 1.10-1.26). Although radical treatment rates were similar in CPG4 and CPG5 (78.8% versus 73.3%; adjusted RR 1.01; 95% CI 0.98-1.04), more men in CPG5 had radiotherapy than men in CPG4 (79.9% versus 59.1%, adjusted RR 1.26; 95% CI 1.12-1.40).

CONCLUSIONS

The CPG classification distributes men in five risk groups that are about equal in size. It reveals differences in treatment practices in men with intermediate-risk disease (CPG2 and CPG3) and in men with high-risk disease (CPG4 and CPGP5) that are not visible when using the traditional three-tiered risk classification.