ArticleCureus2026
Incidence, Patterns, and Predictive Factors of Biochemical Failure Following Definitive Radiotherapy for Localized Prostate Cancer: A Retrospective Military Cohort Study.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDefinitive radiotherapy represents a cornerstone treatment for localized prostate cancer, offering curative potential comparable to surgery. However, biochemical failure remains a concern, signaling possible disease progression.
objectiveThis study aimed to evaluate the frequency, timing, and predictors of biochemical failure in patients receiving definitive radiotherapy for localized prostate cancer at a military oncology center.
methodsWe conducted a retrospective cohort study of 91 patients with localized or locally advanced prostate cancer (cT1-T3, N0-1, M0) treated with definitive radiotherapy at the Military Cancer Center in Jordan between 2020 and 2025. Biochemical failure was defined using the Phoenix criteria (prostate-specific antigen (PSA) nadir + 2 ng/mL). Biochemical recurrence-free survival was estimated using Kaplan-Meier analysis, and independent predictors of biochemical failure were identified using Cox proportional hazards regression.
resultsThe median age was 70 years, and the median pre-treatment PSA was 18.0 ng/mL. According to National Comprehensive Cancer Network (NCCN) risk stratification, 62/91 (68.1%) had high/very high-risk disease, 25/91 (27.5%) had intermediate-risk disease, and 4/91 (4.4%) had low-risk disease. Over a median follow-up of 36 months, 22 patients (24.2%) experienced biochemical failure, with a median time to failure of 24 months. The two-year and five-year biochemical recurrence-free survival rates were 84.6% and 70.3%, respectively. Multivariable analysis identified pre-treatment PSA greater than 20 ng/mL (hazard ratio (HR): 2.8; 95% confidence interval (CI): 1.4-5.6; p = 0.004) and PSA nadir greater than 0.1 ng/mL (HR: 3.2; 95% CI: 1.6-6.4; p = 0.001) as the strongest independent predictors of biochemical failure. Patients with PSA nadir > 0.1 ng/mL had a 3.2-fold higher hazard of biochemical failure compared to those with PSA nadir ≤ 0.1 ng/mL.
conclusionsDefinitive radiotherapy provides favorable biochemical control in this military cohort, with outcomes comparable to international benchmarks. Pre-treatment PSA and post-treatment PSA nadir independently predict biochemical failure, supporting their role in risk stratification and individualized follow-up planning. However, these findings require prospective validation in larger, multicenter cohorts before individualized surveillance strategies can be widely implemented.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.