NDLI: Illustration of association between change in prostate-specific antigen (PSA) values and time to tumor status after treatment for prostate cancer patients: a joint modelling approach

Content Provider	Springer Nature : BioMed Central
Author	Liaqat, Madiha Kamal, Shahid Fischer, Florian
Abstract	Background Prostate cancer (PCa) is the most prevalent tumor in men, and Prostate-Specific Antigen (PSA) serves as the primary marker for diagnosis, recurrence, and disease-free status. PSA levels post-treatment guide physicians in gauging disease progression and tumor status (low or high). Clinical follow-up relies on monitoring PSA over time, forming the basis for dynamic prediction. Our study proposes a joint model of longitudinal PSA and time to tumor shrinkage, incorporating baseline variables. The research aims to assess tumor status post-treatment for dynamic prediction, utilizing joint assessment of PSA measurements and time to tumor status. Methods We propose a joint model for longitudinal PSA and time to tumor shrinkage, taking into account baseline BMI and post-treatment factors, including external beam radiation therapy (EBRT), androgen deprivation therapy (ADT), prostatectomy, and various combinations of these interventions. The model employs a mixed-effect sub-model for longitudinal PSA and an event time sub-model for tumor shrinkage. Results Results emphasize the significance of baseline factors in understanding the relationship between PSA trajectories and tumor status. Patients with low tumor status consistently exhibit low PSA values, decreasing exponentially within one month post-treatment. The correlation between PSA levels and tumor shrinkage is evident, with the considered factors proving to be significant in both sub-models. Conclusions Compared to other treatment options, ADT is the most effective in achieving a low tumor status, as evidenced by a decrease in PSA levels after months of treatment. Patients with an increased BMI were more likely to attain a low tumor status. The research enhances dynamic prediction for PCa patients, utilizing joint analysis of PSA and time to tumor shrinkage post-treatment. The developed model facilitates more effective and personalized decision-making in PCa care.
Related Links	https://bmcurol.biomedcentral.com/counter/pdf/10.1186/s12894-023-01374-8.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14712490
DOI	10.1186/s12894-023-01374-8
Journal	BMC Urology
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-12-06
Access Restriction	Open
Subject Keyword	Urology Internal Medicine Prostate cancer Prostate-specific antigen PSA Time to Tumor status Joint modelling Dynamic prediction
Content Type	Text
Resource Type	Article
Subject	Urology Reproductive Medicine
Journal Impact Factor	1.7/2023
5-Year Journal Impact Factor	2/2023

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