ArticleNPJ systems biology and applications2025
Evaluating PSA dynamics for predicting androgen deprivation failure with a patient specific prostate cancer model.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Beyond prostate-specific antigen: ctDNA as an early compass for treatment adaptation in metastatic prostate cancer.Signal transduction and targeted therapy · 2026Article
- Mathematical Biomarkers of Adaptive Therapy Outcomes in Prostate Cancer.JAMA oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Prostate cancer is the second leading cause of cancer-related death among American men, with a new diagnosis made every 2 min in the United States. Advanced cases are commonly treated with androgen deprivation therapy (ADT). Despite its effectiveness, treatment failure remains inevitable for many patients, necessitating better predictive tools for clinical management of disease. This study presents a data-driven mathematical modeling approach that integrates patient-specific prostate-specific antigen (PSA) time-course data with experimentally measured PSA expression rates to improve the prediction of ADT failure. Our findings suggest that post-nadir PSA dynamics, rather than initial decline, hold greater prognostic value and can inform PSA monitoring schedules. By employing virtual clones of individual patients, our model integrates routinely collected PSA measurements to dynamically predict ADT failure probabilities at future clinic visits. If implemented in clinical practice, this personalized framework could empower oncologists to make proactive, informed treatment decisions and guide timely interventions.
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Registered trials
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