Active surveillance: a review of risk-based, dynamic monitoring

Nieboer, Daan; Tomer, Anirudh; Rizopoulos, Dimitris; Roobol, Monique J.

doi:10.21037/tau.2017.12.27

Cited by 11 publications

(17 citation statements)

References 26 publications

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“…To our knowledge, risk-based biopsy schedules have barely been explored in AS. 9,10 The part of our results pertaining to the fixed/heuristic schedules is comparable with corresponding results obtained in existing work, 6 even though the AS cohorts are not the same. Thus, we anticipate similar validity for the results pertaining to the personalized schedules.…”

Section: Discussionsupporting

confidence: 86%

Personalized Decision Making for Biopsies in Prostate Cancer Active Surveillance Programs

et al. 2019

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Background. Low-risk prostate cancer patients enrolled in active surveillance programs commonly undergo biopsies for examination of cancer progression. Biopsies are conducted as per a fixed and frequent schedule (e.g., annual biopsies). Since biopsies are burdensome, patients do not always comply with the schedule, which increases the risk of delayed detection of cancer progression. Objective. Our aim is to better balance the number of biopsies (burden) and the delay in detection of cancer progression (less is beneficial) by personalizing the decision of conducting biopsies. Data Sources. We used patient data of the world’s largest active surveillance program (Prostate Cancer Research International Active Surveillance; PRIAS). It enrolled 5270 patients, had 866 cancer progressions, and an average of 9 prostate-specific antigen (PSA) and 5 digital rectal examination (DRE) measurements per patient. Methods. Using joint models for time-to-event and longitudinal data, we model the historical DRE and PSA measurements and biopsy results of a patient at each follow-up visit. This results in a visit and patient-specific cumulative risk of cancer progression. If this risk is above a certain threshold, we schedule a biopsy. We compare this personalized approach with the currently practiced biopsy schedules via an extensive and realistic simulation study, based on a replica of the patients from the PRIAS program. Results. The personalized approach saved a median of 6 biopsies (median: 4, interquartile range [IQR]: 2–5) compared with the annual schedule (median: 10, IQR: 3–10). However, the delay in detection of progression (years) is similar for the personalized (median: 0.7, IQR: 0.3–1.0) and the annual schedule (median: 0.5, IQR: 0.3–0.8). Conclusions. We conclude that personalized schedules provide substantially better balance in the number of biopsies per detected progression for men with low-risk prostate cancer.

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Section: Discussionsupporting

confidence: 86%

Personalized Decision Making for Biopsies in Prostate Cancer Active Surveillance Programs

et al. 2019

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“…In PRIAS, 2250 patients were provided treatment based on their PSA level, the number of biopsy cores with cancer, or anxiety/other reasons. However, our reason for focussing solely on upgrading was that upgrading is strongly associated with cancer-related outcomes, and other treatment triggers vary between cohorts [10].…”

Section: Study Cohortmentioning

confidence: 99%

“…Except for the confirmatory biopsy at 1 year of AS [7], opinions and practice regarding the timing of remaining biopsies lack consensus [10]. Some AS programmes utilise patients' observed PSA level, DRE, previous biopsy Gleason Grade, and lately, MRI results to decide whether to take biopsies [4,10,11]. In contrast, others discourage schedules based on clinical data and MRI results [5,12], and instead support periodical one-size-fits-all biopsy schedules.…”

Section: Introductionmentioning

confidence: 99%

“…Also, decisions based on clinical data typically rely only on the latest data point and ignore previous repeated measurements. A novel alternative that counters these drawbacks is first processing patient data via a statistical model, and subsequently using model-predicted upgrading risks to create personalised biopsy schedules [10] (Fig. 2).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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To develop a model and methodology for predicting the risk of Gleason upgrading in patients with prostate cancer on active surveillance (AS) and using the predicted risks to create risk-based personalised biopsy schedules as an alternative to one-size-fits-all schedules (e.g. annually). Furthermore, to assist patients and doctors in making shared decisions on biopsy schedules, by providing them quantitative estimates of the burden and benefit of opting for personalised vs any other schedule in AS. Lastly, to externally validate our model and implement it along with personalised schedules in a ready to use web-application. Patients and Methods Repeat prostate-specific antigen (PSA) measurements, timing and results of previous biopsies, and age at baseline from the world's largest AS study, Prostate Cancer Research International Active Surveillance (PRIAS; 7813 patients, 1134 experienced upgrading). We fitted a Bayesian joint model for time-to-event and longitudinal data to this dataset. We then validated our model externally in the largest six AS cohorts of the Movember Foundation's third Global Action Plan (GAP3) database (>20 000 patients, 27 centres worldwide). Using the model predicted upgrading risks; we scheduled biopsies whenever a patient's upgrading risk was above a certain threshold. To assist patients/doctors in the choice of this threshold, and to compare the resulting personalised schedule with currently practiced schedules, along with the timing and the total number of biopsies (burden) planned, for each schedule we provided them with the time delay expected in detecting upgrading (shorter is better). Results The cause-specific cumulative upgrading risk at the 5-year follow-up was 35% in PRIAS, and at most 50% in the GAP3 cohorts. In the PRIAS-based model, PSA velocity was a stronger predictor of upgrading (hazard ratio [HR] 2.47, 95% confidence interval [CI] 1.93-2.99) than the PSA level (HR 0.99, 95% CI 0.89-1.11). Our model had a moderate area under the receiver operating characteristic curve (0.6-0.7) in the validation cohorts. The prediction error was moderate (0.1-0.2) in theGAP3 cohorts where the impact of the PSA level and velocity on upgrading risk was similar to PRIAS, but large (0.2-0.3) otherwise. Our model required re-calibration of baseline upgrading risk in the validation cohorts. We implemented the validated models and the methodology for personalised schedules in a web-application (http://tiny.cc/biopsy). Conclusions We successfully developed and validated a model for predicting upgrading risk, and providing risk-based personalised biopsy decisions in AS of prostate cancer. Personalised prostate biopsies are a novel alternative to fixed one-size-fits-all schedules, which may help to reduce unnecessary prostate biopsies, while maintaining cancer control. The model and schedules made available via a web-application enable shared decision-making on biopsy schedules by comparing fixed and personalised schedules on total biopsies and expected time delay in detecting upgrading.

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“…Risk calculators, such as the Canary Prostate Active Surveillance Study risk calculation 87 , the Johns Hopkins model 88 and the PRIAS model 89 have been developed in an effort to address this need. These AS risk calculators can selectively predict men at risk of progression and balance reclassification detection with the number of surveillance biopsies by incorporating the serial measurement of the monitoring tools into the prediction model (for example, patient age, prostate volume, PSA, time since diagnosis, number of previous biopsies and all previous biopsy results) [90][91][92][93][94] .…”

Section: Research Need Number 2 Develop Indicators To Better Stratifymentioning

confidence: 99%

The Movember Prostate Cancer Landscape Analysis: an assessment of unmet research needs

et al. 2020

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Prostate cancer is the second most diagnosed solid-organ malignancy in men 1 , with >1.2 million new cases and 358,989 deaths reported worldwide in 2018 (ref. 2). Since 2005, the global men's health charity Movember has invested in a variety of very influential programmes in biomedical research, survivorship and clinical quality and has set ambitious organizational objectives to work with its global partners to halve the number of deaths from prostate cancer and to halve the number of men facing ongoing adverse effects from treatment by 2030. In order to transform these aspirations into reality, Movember needs to understand the gaps and opportunities in the field. Given that the last published analysis of the field was conducted by Tindall and Scardino 3 ~20 years ago, Movember's Global Scientific Committee (GSC) recommended that the Movember Biomedical Research Team commission a formal landscape analysis to assess the international prostate cancer research field in order to optimize future research investment and maximize biomedical research outcomes. The scope of this analysis was restricted to the role of Movember as a funder whose purpose is to positively change clinical practice in the prostate cancer biomedical research field for the benefit of men. In this Consensus Statement, we present the results and conclusions of Movember's prostate cancer global landscape analysis. Methods The Movember prostate cancer landscape analysis commenced in April 2017. A sequential research design was used, in which insights gathered from stakeholder

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Active surveillance: a review of risk-based, dynamic monitoring

Cited by 11 publications

References 26 publications

Personalized Decision Making for Biopsies in Prostate Cancer Active Surveillance Programs

Personalized Decision Making for Biopsies in Prostate Cancer Active Surveillance Programs

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

The Movember Prostate Cancer Landscape Analysis: an assessment of unmet research needs

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