Exploring the Relation of AS Schedules and PCa Mortality

Exploring the relation of active surveillance schedules and prostate cancer mortality (2024, Full Text)

"Abstract
Background
Active surveillance (AS), where treatment is deferred until cancer progression is detected by a biopsy, is acknowledged as a way to reduce overtreatment in prostate cancer. However, a consensus on the frequency of taking biopsies while in AS is lacking. In former studies to optimize biopsy schedules, the delay in progression detection was taken as an evaluation indicator and believed to be associated with the long-term outcome, prostate cancer mortality. Nevertheless, this relation was never investigated in empirical data. Here, we use simulated data from a microsimulation model to fill this knowledge gap.

Methods
In this study, the established MIcrosimulation SCreening Analysis model was extended with functionality to simulate the AS procedures. The biopsy sensitivity in the model was calibrated on the Canary Prostate Cancer Active Surveillance Study (PASS) data, and four (tri-yearly, bi-yearly, PASS, and yearly) AS programs were simulated. The relation between detection delay and prostate cancer mortality was investigated by Cox models.

Results
The biopsy sensitivity of progression detection was found to be 50%. The Cox models show a positive relation between a longer detection delay and a higher risk of prostate cancer death. A 2-year delay resulted in a prostate cancer death risk of 2.46%–2.69% 5 years after progression detection and a 10-year risk of 5.75%–5.91%. A 4-year delay led to an approximately 8% greater 5-year risk and an approximately 25% greater 10-year risk.

Conclusion
The detection delay is confirmed as a surrogate for prostate cancer mortality. A cut-off for a “safe” detection delay could not be identified."

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Djin

Mumbo said...
These articles sound like researchers are asking AI questions and it is generating the reports. I suggest they ask the same question of three different AI systems and then have another AI system analyze the results from them and prepare a summary report.

The analysis was done by a Monte Carlo simulation program (MISCAN) that's been around for some time and still in use. The R statistical programming language was used for the curve-fitting.

The MISCAN simulation program for the evaluation of screening for disease (1985)

It's good someone is asking these questions.

Djin