Personalised biopsy schedules based on risk of Gleason upgrading for patients with low-risk prostate cancer on active surveillance

Personalized Dynamic Prediction Model for Biopsy Timing in Patients With Prostate Cancer During Active Surveillance

Importance

Active surveillance (AS) for patients with prostate cancer (PC) often includes fixed repeat prostate biopsies that do not account for the varying risk of reclassification to significant disease. Given the invasive nature and potential complications of biopsies, a personalized approach is needed to balance the burden of biopsies with the risk of missing disease progression.

Objective

To develop and externally validate a dynamic model that predicts an individual's risk of PC reclassification during AS.

Design, Setting, and Participants

This prognostic study developed a dynamic prediction model using data from the Prostate Cancer Research International: Active Surveillance (PRIAS) study, which was initiated in 2006. Follow-up was truncated until April 2023. External validation was conducted using cohorts from the world's largest centralized AS database, the Global Action Plan Prostate Cancer Active Surveillance initiative database. The PRIAS study is a multicenter, prospective, web-based cohort study monitoring patients undergoing AS, involving more than 175 academic, nonacademic, and private centers across 23 countries worldwide. For the development and external validation of the model, all patients diagnosed with Grade Group 1 PC who underwent at least 1 baseline or follow-up magnetic resonance imaging (MRI) and 1 follow-up biopsy were included. Data were analyzed from September 2023 to January 2024.

Exposures

AS, including prostate-specific antigen (PSA) tests, MRI, and prostate biopsies according to a fixed follow-up schedule.

Main Outcomes and Measures

A joint model for longitudinal and time-to-event data was used to predict reclassification to Grade Group 2 or greater on repeat biopsy using predefined baseline and repeated clinical characteristics. Performance was assessed using time-dependent area under the receiver operating characteristic curve and negative predictive value.

Results

The development cohort included 2512 patients (median [IQR] age, 65 [59-69] years). Characteristics significantly associated with a higher risk of reclassification were increased age, higher PSA and velocity, lower prostate volume, a suspicious lesion on MRI, and no previous negative biopsy findings. Depending on the threshold and time point used, the model demonstrated a negative predictive value of 86% to 97%. External validation included 3199 patients from 9 other cohorts. The time-dependent area under the curve ranged from 0.81 to 0.84 in the development cohort and 0.52 to 0.90 at external validation.

Conclusions and Relevance

In this prognostic study, the developed dynamic risk model effectively identified patients at low risk of PC reclassification during AS. After prospective validation, this model may support personalized, risk-based AS and reduce the burden of unnecessary biopsies.

From Screening to Mortality Reduction: An Overview of Empirical Data on the Patient Journey in European Randomized Study of Screening for Prostate Cancer Rotterdam After 21 Years of Follow-up and a Reflection on Quality of Life

BACKGROUND

Previous research quantified the effect of prostate-specific antigen (PSA)-based prostate cancer (PCa) screening on quality-adjusted life years using 11-yr follow-up data from the European Randomized Study of Screening for Prostate Cancer (ERSPC) extrapolated by the Microsimulation Screening Analysis (MISCAN). ERSPC data now matured to 21 yr of follow-up.

OBJECTIVE

To provide an overview of the effect of PSA-based screening on tumour characteristics and PCa treatment using long-term, detailed, empirical ERSPC data.

DESIGN, SETTING, AND PARTICIPANTS

Men were included from the ERSPC Rotterdam who were randomised to a PSA-based screening (S) or control (C) arm.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We assessed the effects of PSA-based screening on the number of PCa diagnoses, tumour characteristics, treatments, and cumulative incidence of disease progression. We also evaluated the changes in tumour characteristics and treatments over time for both study arms.

RESULTS AND LIMITATIONS

Among PCa patients in the S-arm, fewer patients were diagnosed with advanced tumour stages (T3/T4: 12% vs 23%; relative risk [RR] = 0.50; 95% confidence interval [CI] 0.44-0.57), less disease progression was observed, and less secondary treatment (30% vs 48%; RR = 0.61; 95% CI 0.57-0.66; p < 0.001) and less palliative treatment were needed (21% vs 55%; RR = 0.38; 95% CI 0.35-0.42) than among those in the C-arm. This was at the cost of overdiagnosis and increased local treatments (eg, radical prostatectomy: 32% vs 14%; RR = 2.18; 95% CI 1.92-2.48). Over time, the number of local treatments decreased, whereas expectant management strategies increased. The RRs of treatments were slightly different from those of the MISCAN.

CONCLUSIONS

After 21 yr of follow-up, empirical data of the ERSPC showed that PSA-based screening reduces advanced PCa stages, disease progression, and extensive treatments at the cost of more overdiagnosis and probably more overtreatment. Our data showed reduced local treatments and increased expectant management strategies over time.

PATIENT SUMMARY

Prostate-specific antigen-based screening reduces the number of invasive prostate cancer treatments needed, however, at the cost of more overdiagnosis and probably more overtreatment. Limiting these costs remains crucial to benefit optimally from prostate cancer screening.

De-escalation of Monitoring in Active Surveillance for Prostate Cancer: Results from the GAP3 Consortium

BACKGROUND AND OBJECTIVE

There is no consensus on de-escalation of monitoring during active surveillance (AS) for prostate cancer (PCa). Our objective was to determine clinical criteria that can be used in decisions to reduce the intensity of AS monitoring.

METHODS

The global prospective AS cohort from the Global Action Plan prostate cancer AS consortium was retrospectively analyzed. The 24656 patients with complete outcome data were considered. The primary goal was to develop a model identifying a subgroup with a high ratio of other-cause mortality (OCM) to PCa-specific mortality (PCSM). Nonparametric competing-risks models were used to estimate cause-specific mortality. We hypothesized that the subgroup with the highest OCM/PCSM ratio would be good candidates for de-escalation of AS monitoring.

KEY FINDINGS AND LIMITATIONS

Cumulative mortality at 15 yr, accounting for censoring, was 1.3% for PCSM, 11.5% for OCM, and 18.7% for death from unknown causes. We identified body mass index (BMI) >25 kg/m2 and <11% positive cores at initial biopsy as an optimal set of criteria for discriminating OCM from PCSM. The 15-yr OCM/PCSM ratio was 34.2 times higher for patients meeting these criteria than for those not meeting the criteria. According to these criteria, 37% of the cohort would be eligible for de-escalation of monitoring. Limitations include the retrospective nature of the study and the lack of external validation.

CONCLUSIONS

Our study identified BMI >25 kg/m2 and <11% positive cores at initial biopsy as clinical criteria for de-escalation of AS monitoring in PCa.

PATIENT SUMMARY

We investigated factors that could help in deciding on when to reduce the intensity of monitoring for patients on active surveillance for prostate cancer. We found that patients with higher BMI (body mass index) and lower prostate cancer volume may be good candidates for less intensive monitoring. This model could help doctors and patients in making decisions on active surveillance for prostate cancer.

Individualized Dynamic Prediction Model for Patient-Reported Voice Quality in Early-Stage Glottic Cancer

OBJECTIVE

Early-stage glottic cancer (ESGC) is a malignancy of the head and neck. Besides disease control, preservation and improvement of voice quality are essential. To enable expectation management and well-informed decision-making, patients should be sufficiently counseled with individualized information on expected voice quality. This study aims to develop an individualized dynamic prediction model for patient-reported voice quality. This model should be able to provide individualized predictions at every time point from intake to the end of follow-up.

STUDY DESIGN

Longitudinal cohort study.

SETTING

Tertiary cancer center.

METHODS

Patients treated for ESGC were included in this study (N = 294). The Voice Handicap Index was obtained prospectively. The framework of mixed and joint models was used. The prognostic factors used are treatment, age, gender, comorbidity, performance score, smoking, T-stage, and involvement of the anterior commissure. The overall performance of these models was assessed during an internal cross-validation procedure and presentation of absolute errors using box plots.

RESULTS

The mean age in this cohort was 67 years and 81.3% are male. Patients were treated with transoral CO2 laser microsurgery (57.8%), single vocal cord irradiation up to (24.5), or local radiotherapy (17.5%). The mean follow-up was 43.4 months (SD 21.5). Including more measurements during prediction improves predictive performance. Including more clinical and demographic variables did not provide better predictions. Little differences in predictive performance between models were found.

CONCLUSION

We developed a dynamic individualized prediction model for patient-reported voice quality. This model has the potential to empower patients and professionals in making well-informed decisions and enables tailor-made counseling.

Aggressive Prostate Cancer in Patients Treated with Active Surveillance

Active surveillance has emerged as a promising approach for managing low-risk and favorable intermediate-risk prostate cancer (PC), with the aim of minimizing overtreatment and maintaining the quality of life. However, concerns remain about identifying "aggressive prostate cancer" within the active surveillance cohort, which refers to cancers with a higher potential for progression. Previous studies are predictors of aggressive PC during active surveillance. To address this, a personalized risk-based follow-up approach that integrates clinical data, biomarkers, and genetic factors using risk calculators was proposed. This approach enables an efficient risk assessment and the early detection of disease progression, minimizes unnecessary interventions, and improves patient management and outcomes. As active surveillance indications expand, the importance of identifying aggressive PC through a personalized risk-based follow-up is expected to increase.

Active Surveillance for Prostate Cancer: Past, Current, and Future Trends

In response to the rising incidence of indolent, low-risk prostate cancer (PCa) due to increased prostate-specific antigen (PSA) screening in the 1990s, active surveillance (AS) emerged as a treatment modality to combat overtreatment by delaying or avoiding unnecessary definitive treatment and its associated morbidity. AS consists of regular monitoring of PSA levels, digital rectal exams, medical imaging, and prostate biopsies, so that definitive treatment is only offered when deemed necessary. This paper provides a narrative review of the evolution of AS since its inception and an overview of its current landscape and challenges. Although AS was initially only performed in a study setting, numerous studies have provided evidence for the safety and efficacy of AS which has led guidelines to recommend it as a treatment option for patients with low-risk PCa. For intermediate-risk disease, AS appears to be a viable option for those with favourable clinical characteristics. Over the years, the inclusion criteria, follow-up schedule and triggers for definitive treatment have evolved based on the results of various large AS cohorts. Given the burdensome nature of repeat biopsies, risk-based dynamic monitoring may further reduce overtreatment by avoiding repeat biopsies in selected patients.

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.

Interaction of MRI and active surveillance in prostate cancer: Time to re-evaluate the active surveillance inclusion criteria

Currently available data from long-running single- and multi-center active surveillance (AS) studies show that AS has excellent cancer-specific survival rates. For AS to be effective the 'right' patients should be selected for which up until 5-to-10 years ago systematic prostate biopsies were used. Because the systematic prostate strategy relies on sampling efficiency for the detection of prostate cancer (PCa), it is subject to sampling error. Due to this sampling error, many of the Gleason 3+3 PCas that were included on AS in the early days and were classified as low-risk, may in fact have had a higher Gleason score. Subsequently, AS-criteria were more strict to overcome or limit the number of men missing the potential window of curability in case their tumor would be reclassified. Five to ten years ago the prostate biopsy landscape changed drastically by the addition of magnetic resonance imaging (MRI) into the diagnostic PCa-care pathway, which has by now trickled down into the EAU guidelines. At the moment, the EAU guidelines recommend performing a (multi-parametric) MRI before prostate biopsy and combine systematic and targeted prostate biopsy when the MRI is positive (i.e. PIRADS ≥3). So because of the introduction of the MRI into the diagnostic PCa-care pathway, literature is showing that more Gleason 3+4 PCas are being diagnosed. But can it not be that the inclusion of MRI into the diagnostic PCa-care pathway causes risk inflation, resulting in men earlier eligible for AS, now being labelled ineligible for AS? Would it not be possible to include these current Gleason 3+4 PCas on AS? The authors hypothesize that the improved accuracy that comes with the introduction of MRI into the diagnostic PCa-care pathway permits to widen both the AS-inclusion and follow-up criteria. Maintaining our inclusion criteria for AS from the systematic biopsy era will unnecessarily and undesirably expose patients to the increased risk of overtreatment. The evidence behind the addition of MRI-targeted biopsies to systematic biopsies calls upon the re-evaluation of the AS inclusion criteria and research from one-size-fits-all protocols used so far, into the direction of more dynamic and individual risk-based AS-approaches.

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.

A Multivariable Approach Using Magnetic Resonance Imaging to Avoid a Protocol-based Prostate Biopsy in Men on Active Surveillance for Prostate Cancer-Data from the International Multicenter Prospective PRIAS Study

BACKGROUND

There is ongoing discussion whether a multivariable approach including magnetic resonance imaging (MRI) can safely prevent unnecessary protocol-advised repeat biopsy during active surveillance (AS).

OBJECTIVE

To determine predictors for grade group (GG) reclassification in patients undergoing an MRI-informed prostate biopsy (MRI-Bx) during AS and to evaluate whether a confirmatory biopsy can be omitted in patients diagnosed with upfront MRI.

DESIGN, SETTING, AND PARTICIPANTS

The Prostate cancer Research International: Active Surveillance (PRIAS) study is a multicenter prospective study of patients on AS (www.prias-project.org). We selected all patients undergoing MRI-Bx (targeted ± systematic biopsy) during AS.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

A time-dependent Cox regression analysis was used to determine the predictors of GG progression/reclassification in patients undergoing MRI-Bx. A sensitivity analysis and a multivariable logistic regression analysis were also performed.

RESULTS AND LIMITATIONS

A total of 1185 patients underwent 1488 MRI-Bx sessions. The time-dependent Cox regression analysis showed that age (per 10 yr, hazard ratio [HR] 0.84 [95% confidence interval {CI} 0.71-0.99]), MRI outcome (Prostate Imaging Reporting and Data System [PIRADS] 3 vs negative HR 2.46 [95% CI 1.56-3.88], PIRADS 4 vs negative HR 3.39 [95% CI 2.28-5.05], and PIRADS 5 vs negative HR 4.95 [95% CI 3.25-7.56]), prostate-specific antigen (PSA) density (per 0.1 ng/ml cm³, HR 1.20 [95% CI 1.12-1.30]), and percentage positive cores on the last systematic biopsy (per 10%, HR 1.16 [95% CI 1.10-1.23]) were significant predictors of GG reclassification. Of the patients with negative MRI and a PSA density of <0.15 ng/ml cm³ (n = 315), 3% were reclassified to GG ≥2 and 0.6% to GG ≥3. At the confirmatory biopsy, reclassification to GG ≥2 and ≥3 was observed in 23% and 7% of the patients diagnosed without upfront MRI and in 19% and 6% of the patients diagnosed with upfront MRI, respectively. The multivariable analysis showed no significant difference in upgrading at the confirmatory biopsy between patients diagnosed with or without upfront MRI.

CONCLUSIONS

Age, MRI outcome, PSA density, and percentage positive cores are significant predictors of reclassification at an MRI-informed biopsy. Patients with negative MRI and a PSA density of <0.15 ng/ml cm³ can safely omit a protocol-based prostate biopsy, whereas in other patients, a multivariable approach is advised. Being diagnosed with upfront MRI appears not to significantly affect reclassification risk; hence, a confirmatory MRI-Bx cannot totally be omitted yet.

PATIENT SUMMARY

A protocol-based prostate biopsy while on active surveillance can be omitted in patients with negative magnetic resonance imaging (MRI) and prostate-specific antigen density <0.15 ng/ml cm³. A confirmatory biopsy cannot simply be omitted in all patients diagnosed with upfront MRI.

Impact of Prostate Health Index Results for Prediction of Biopsy Grade Reclassification During Active Surveillance

PURPOSE

We assessed whether Prostate Health Index results improve prediction of grade reclassification for men on active surveillance.

METHODS AND MATERIALS

We identified men in Canary Prostate Active Surveillance Study with Grade Group 1 cancer. Outcome was grade reclassification to Grade Group 2+ cancer. We considered decision rules to maximize specificity with sensitivity set at 95%. We derived rules based on clinical data (R1) vs clinical data+Prostate Health Index (R3). We considered an "or"-logic rule combining clinical score and Prostate Health Index (R4), and a "2-step" rule using clinical data followed by risk stratification based on Prostate Health Index (R2). Rules were applied to a validation set, where values of R2-R4 vs R1 for specificity and sensitivity were evaluated.

RESULTS

We included 1,532 biopsies (n = 610 discovery; n = 922 validation) among 1,142 men. Grade reclassification was seen in 27% of biopsies (23% discovery, 29% validation). Among the discovery set, at 95% sensitivity, R2 yielded highest specificity at 27% vs 17% for R1. In the validation set, R3 had best performance vs R1 with Δsensitivity = -4% and Δspecificity = +6%. There was slight improvement for R3 vs R1 for confirmatory biopsy (AUC 0.745 vs R1 0.724, ΔAUC 0.021, 95% CI 0.002-0.041) but not for subsequent biopsies (ΔAUC -0.012, 95% CI -0.031-0.006). R3 did not have better discrimination vs R1 among the biopsy cohort overall (ΔAUC 0.007, 95% CI -0.007-0.020).

CONCLUSIONS

Among active surveillance patients, using Prostate Health Index with clinical data modestly improved prediction of grade reclassification on confirmatory biopsy and did not improve prediction on subsequent biopsies.

Joint models for dynamic prediction in localised prostate cancer: a literature review

BACKGROUND

Prostate cancer is a very prevalent disease in men. Patients are monitored regularly during and after treatment with repeated assessment of prostate-specific antigen (PSA) levels. Prognosis of localised prostate cancer is generally good after treatment, and the risk of having a recurrence is usually estimated based on factors measured at diagnosis. Incorporating PSA measurements over time in a dynamic prediction joint model enables updates of patients' risk as new information becomes available. We review joint model strategies that have been applied to model time-dependent PSA trajectories to predict time-to-event outcomes in localised prostate cancer.

METHODS

We identify articles that developed joint models for prediction of localised prostate cancer recurrence over the last two decades. We report, compare, and summarise the methodological approaches and applications that use joint modelling accounting for two processes: the longitudinal model (PSA), and the time-to-event process (clinical failure). The methods explored differ in how they specify the association between these two processes.

RESULTS

Twelve relevant articles were identified. A range of methodological frameworks were found, and we describe in detail shared-parameter joint models (9 of 12, 75%) and joint latent class models (3 of 12, 25%). Within each framework, these articles presented model development, estimation of dynamic predictions and model validations.

CONCLUSIONS

Each framework has its unique principles with corresponding advantages and differing interpretations. Regardless of the framework used, dynamic prediction models enable real-time prediction of individual patient prognosis. They utilise all available longitudinal information, in addition to baseline prognostic risk factors, and are superior to traditional baseline-only prediction models.

Risk of progression following a negative biopsy in prostate cancer active surveillance

BACKGROUND

Currently, follow-up protocols are applied equally to men on active surveillance (AS) for prostate cancer (PCa) regardless of findings at their initial follow-up biopsy. To determine whether less intensive follow-up is suitable following negative biopsy findings, we assessed the risk of converting to active treatment, any subsequent upgrading, volume progression (>33% positive cores), and serious upgrading (grade group >2) for negative compared with positive findings on initial follow-up biopsy.

METHODS

13,161 men from 24 centres participating in the Global Action Plan Active Surveillance Prostate Cancer [GAP3] consortium database, with baseline grade group ≤2, PSA ≤ 20 ng/mL, cT-stage 1-2, diagnosed after 1995, and ≥1 follow-up biopsy, were included in this study. Risk of converting to treatment was assessed using multivariable mixed-effects survival regression. Odds of volume progression, any upgrading and serious upgrading were assessed using mix-effects binary logistic regression for men with ≥2 surveillance biopsies.

RESULTS

27% of the cohort (n = 3590) had no evidence of PCa at their initial biopsy. Over 50% of subsequent biopsies in this group were also negative. A negative initial biopsy was associated with lower risk of conversion (adjusted hazard ratio: 0.45; 95% confidence interval [CI]: 0.42-0.49), subsequent upgrading (adjusted odds ratio [OR]: 0.52; 95%CI: 0.45-0.62) and serious upgrading (OR: 0.74; 95%CI: 0.59-92). Radiological progression was not assessed due to limited imaging data.

CONCLUSION

Despite heterogeneity in follow-up schedules, findings from this global study indicated reduced risk of converting to treatment, volume progression, any upgrading and serious upgrading among men whose initial biopsy findings were negative compared with positive. Given the low risk of progression and high likelihood of further negative biopsy findings, consideration should be given to decreasing follow-up intensity for this group to reduce unnecessary invasive biopsies.

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.

Comparison of outcomes of different biopsy schedules among men on active surveillance for prostate cancer: An analysis of the G.A.P.3 global consortium database

BACKGROUND

The optimal interval for repeat biopsy during active surveillance (AS) for prostate cancer is yet to be defined. This study examined whether risk of upgrading (to grade group ≥ 2) or risk of converting to treatment varied according to intensity of repeat biopsy using data from the GAP3 consortium's global AS database.

MATERIALS AND METHODS

Intensity of surveillance biopsy schedules was categorized according to centers' protocols: (a) Prostate Cancer Research International Active Surveillance project (PRIAS) protocols with biopsies at years 1, 4, and 7 (10 centers; 7532 men); (b) biennial biopsies, that is, every other year (8 centers; 4365 men); and (c) annual biopsy schedules (4 centers; 1602 men). Multivariable Cox regression was used to compare outcomes according to biopsy intensity.

RESULTS

Out of the 13,508 eligible participants, 56% were managed according to PRIAS protocols (biopsies at years 1, 4, and 7), 32% via biennial biopsy, and 12% via annual biopsy. After adjusting for baseline characteristics, risk of converting to treatment was greater for those on annual compared with PRIAS biopsy schedules (hazard ratio [HR] = 1.66; 95% confidence interval [CI] = 1.51-1.83; p < 0.001), while risk of upgrading did not differ (HR = 0.96; 95% CI = 0.84-1.10).

CONCLUSION

Results suggest more frequent biopsy schedules may deter some men from continuing AS despite no evidence of grade progression.

Active Surveillance in Prostate Cancer: Role of Available Biomarkers in Daily Practice

Prostate cancer (PCa) is the most commonly diagnosed cancer in men. The diagnosis is currently based on PSA levels, which are associated with overdiagnosis and overtreatment. Moreover, most PCas are localized tumours; hence, many patients with low-/very low-risk PCa could benefit from active surveillance (AS) programs instead of more aggressive, active treatments. Heterogeneity within inclusion criteria and follow-up strategies are the main controversial issues that AS presently faces. Many biomarkers are currently under investigation in this setting; however, none has yet demonstrated enough diagnostic ability as an independent predictor of pathological or clinical progression. This work aims to review the currently available literature on tissue, blood and urine biomarkers validated in clinical practice for the management of AS patients.

What is the effect of MRI with targeted biopsies on the rate of patients discontinuing active surveillance? A reflection of the use of MRI in the PRIAS study

BACKGROUND

The reduction of overtreatment by active surveillance (AS) is limited in patients with low-risk prostate cancer (PCa) due to high rates of patients switching to radical treatment. MRI improves biopsy accuracy and could therewith affect inclusion in or continuation of AS. We aim to assess the effect of MRI with target biopsies on the total rate of patients discontinuing AS, and in particular discontinuation due to Grade Group (GG) reclassification.

METHODS

Three subpopulations included in the prospective PRIAS study with GG 1 were studied. Group A consists of patients diagnosed before 2009 without MRI before or during AS. Group B consists of patients diagnosed without MRI, but all patients underwent MRI within 6 months after diagnosis. Group C consists of patients who underwent MRI before diagnosis and during follow-up. We used cumulative incidence curves to estimate the rates of discontinuation.

RESULTS

In Group A (n = 500), the cumulative probability of discontinuing AS at 2 years is 27.5%; GG reclassification solely accounted for 6.9% of the discontinuation. In Group B (n = 351) these numbers are 30.9 and 22.8%, and for Group C (n = 435) 24.2 and 13.4%. The three groups were not randomized, however, baseline characteristics are highly comparable.

CONCLUSIONS

Performing an MRI before starting AS reduces the cumulative probability of discontinuing AS at 2 years. Performing an MRI after already being on AS increases the cumulative probability of discontinuing AS in comparison to not performing an MRI, especially because of an increase in GG reclassification. These results suggest that the use of MRI could lead to more patients being considered unsuitable for AS. Considering the excellent long-term cancer-specific survival of AS before the MRI era, the increased diagnostic accuracy of MRI could potentially lead to more overtreatment if definitions and treatment options of significant PCa are not adapted.