# The Impact of Endpoint Definitions on Predictors of Progression in Active Surveillance for Early Prostate Cancer

## TL;DR

This study shows that how prostate cancer progression is defined affects which factors predict it, with PSA density being the most reliable predictor across different definitions.

## Contribution

The study demonstrates that PSA density is a consistent predictor of progression across varying endpoint definitions in prostate cancer active surveillance.

## Key findings

- PSA density consistently predicted progression events across different endpoint definitions.
- Biopsy and imaging metrics did not consistently improve progression prediction when added to baseline variables.
- Progression endpoint definitions significantly influence which variables are identified as predictors.

## Abstract

Active surveillance (AS) is a critically important management strategy for men with favourable-prognosis prostate cancer. However, there is no standardised or internationally agreed-upon definition of a disease progression endpoint for when AS should stop. This has led to uncertainty regarding which baseline variables reliably predict progression. In the literature, there has also been a multitude of proposed biopsy and imaging metrics that are purported to predict AS progression. We utilised the STRATified CANcer Surveillance (STRATCANs) prospective AS database to assess the utility of different clinicopathological variables in predicting progression and against different contemporary AS endpoint definitions. In this study, we found that the AS endpoint definition appears to determine which variables predict progression. Neither the addition of biopsy nor imaging metrics added consistent incremental value in better predicting progression events. PSA density, in contrast, consistently predicted progression events across different endpoint definitions.

Background/Objectives: There is conflicting data on which factors predict progression events in active surveillance for early prostate cancer. Here, we explored the value of different clinicopathological variables and whether progression endpoint definitions impact predictive utility. Methods: Clinicopathological variables were extracted from the STRATified CANcer Surveillance (STRATCANs) prospective AS database and included biopsy features (core positivity, cancer core length, and percentage core involvement) and MRI features (Likert score, lesion size, and location), as well as baseline PSA density [PSAd] and Cambridge Prognostic Group (CPG). These were tested against AS endpoint definitions of (1) progression to ≥CPG3, (2) any pathological progression and two definitions from the literature, (3) ≥GG3 or change to treatment, and (4) ≥GG4, metastasis or cancer-related mortality. Predictors were assessed using regression analysis. Results: Data from 296 men were included (median age, 66; follow-up, 5 years). Progression per definition (1–4) occurred in 46 (15.5%), 54 (18.2%), 84 (28.4%), and 10 (3.4%) men. In univariate analysis using Definition 1, no biopsy parameter was independently predictive of progression, while the MRI Likert score (p = 0.02) was the only significant imaging parameter. For Definition 2, core positivity (p = 0.003) and MRI Likert score (p = 0.01) were significant predictors in univariate analyses, while for Definition 3, tumour core length (p = 0.005), core positivity (p = 0.002), and MRI Likert score (p = 0.003) were all predictive in univariate analyses. In multivariate analysis, however, the only consistent independent predictor was PSAd, regardless of endpoint definition. No variables predicted Definition 4 progression. Conclusions: AS endpoint selection appears to define which variables predict progression. Using progression to ≥CPG 3 as an unambiguous AS endpoint, neither biopsy nor MRI variables added incremental value in predicting progression. PSAd, however, appears to be a robust and independent generalisable progression predictor.