Adjuvant Versus Salvage Radiotherapy for Patients With Adverse Pathological Findings Following Radical Prostatectomy: A Decision Analysis

Markov models for clinical decision-making in radiation oncology: A systematic review

The intrinsic stochasticity of patients' response to treatment is a major consideration for clinical decision-making in radiation therapy. Markov models are powerful tools to capture this stochasticity and render effective treatment decisions. This paper provides an overview of the Markov models for clinical decision analysis in radiation oncology. A comprehensive literature search was conducted within MEDLINE using PubMed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published from 2000 to 2023 were considered. Selected publications were summarized in two categories: (i) studies that compare two (or more) fixed treatment policies using Monte Carlo simulation and (ii) studies that seek an optimal treatment policy through Markov Decision Processes (MDPs). Relevant to the scope of this study, 61 publications were selected for detailed review. The majority of these publications (n = 56) focused on comparative analysis of two or more fixed treatment policies using Monte Carlo simulation. Classifications based on cancer site, utility measures and the type of sensitivity analysis are presented. Five publications considered MDPs with the aim of computing an optimal treatment policy; a detailed statement of the analysis and results is provided for each work. As an extension of Markov model-based simulation analysis, MDP offers a flexible framework to identify an optimal treatment policy among a possibly large set of treatment policies. However, the applications of MDPs to oncological decision-making have been understudied, and the full capacity of this framework to render complex optimal treatment decisions warrants further consideration.

Impact of Decipher on use of post‐operative radiotherapy: Individual patient analysis of two prospective registries

Objective

To assess the association between Genomic Classifier (GC)‐risk group and post‐radical prostatectomy treatment in clinical practice.

Methods

Two prospective observational cohorts of men with prostate cancer (PCa) who underwent RP in two referral centers and had GC testing post‐prostatectomy between 2013 and 2018 were included. The primary endpoint of the study was to assess the association between GC‐risk group and time to secondary therapy. Univariable (UVA) and multivariable (MVA) Cox proportional hazards models were constructed to assess the association between GC‐risk group and time to receipt of secondary therapy after RP, where secondary therapy is defined as receiving either RT or ADT after RP.

Results

A total of 398 patients are included in the analysis. Patients with high‐GC risk were more likely to receive any secondary therapy (OR: 6.84) compared to patients with low/intermediate‐GC risk. The proportion of high‐GC risk patients receiving RT at 2 years post‐RP was 31.5%, compared to only 6.3% among the low/intermediate‐GC risk patients.

Conclusion

This study demonstrates that physicians in routine practice used GC to identify high risk patients who might benefit the most from secondary treatment. As such, GC score was independent predictor of receipt of secondary treatment.

Abiraterone vs. docetaxel for metastatic hormone-sensitive prostate cancer: A microsimulation model

INTRODUCTION

Our aim was to determine whether androgen deprivation therapy (ADT) with abiraterone acetate (AA) or ADT with docetaxel chemotherapy (DC) resulted in improved quality-adjusted life years (QALYs) among men with de novo metastatic castration-sensitive prostate cancer (mCSPC) and the cost effectiveness of the preferred strategy using decision analytic techniques.

METHODS

A microsimulation model with a lifetime time horizon was constructed. Our primary outcome was QALYs. Secondary outcomes included cost, incremental cost effectiveness ratio (ICER), unadjusted overall survival (OS), rates of second- and third-line therapy, and adverse events. A systematic literature review was used to generate probabilities and utilities to populate the model. The base case was a 65-year-old patient with de novo mCSPC.

RESULTS

A total of 100 000 microsimulations were generated. Initial AA resulted in a gain of 0.45 QALYs compared to DC (3.36 vs. 2.91 QALYs) with an ICER of $276 251.82 per QALY gained with initial AA therapy. Median crude OS was 51 months with AA and 48 months with DC. Overall, 46.6% and 42.6% of patients received second-line therapy and 8.7% and 7.9% patients received third-line therapy in the AA and DC groups, respectively. Grade 3/4 adverse events were experienced in 17.6% of patients receiving initial AA and 22.3% of patients receiving initial DC.

CONCLUSIONS

Although ADT with AA results in a gain in QALYs and crude OS compared to DC, AA therapy is not a cost-effective treatment strategy to apply uniformly to all patients. The availability of AA as a generic medication may help to close this gap. The ultimate choice should be based on patient and tumor factors.