Anwar Ahmed

MSc.; Ph.D.

Uniformed Services University

Assistant Professor

PLCO (Learn more about this study)

PLCO-1141

Jan 11, 2023

Research gaps in PSA tests and race-specific risk factors

he PSA test, adopted by medical institutions, is currently the primary screening tool for prostate cancer. This test measures the amount of PSA in the blood. A high value of the PSA indicates a high risk of prostate cancer. Specifically, a PSA level higher than 4 ng/ml has been used as a standard cutoff value for the abnormal test, however prostate cancer has been detected in cases with lower PSA levels. Less than 4 ng/ml (Thompson IM et al., 2004) PSA levels between 4 and 10 ng/mL have been found in individuals with no prostate cancer (Thompson IM et al., 2004). PSA levels could increase with age or infections (Gustafsson O., et al., 1998). In response to the poor discriminatory accuracy of the PSA, researchers have sought to develop risk prediction models to better identify high-risk prostate cancer and reduce unnecessary biopsy tests and overdiagnosis. One study utilized prostate cancer biopsy data, and integrated PSA and several risk factors to improve clinical utility prostate cancer (Thompson et al 2006). A clinical utility of the Prostate Health Index (phi) was developed to detect aggressive prostate cancer (Loeb, Stacy, et al. 2017). The discriminatory accuracy of this model has an area under the curve of 0.746. A urine-based risk prediction model, MyProstateScore (MPS), combines PSA with urine T2:ERG and PCA3 scores (Tosoian et al., 2015). The proposed research shall address important research gaps by establishing the accuracy of prostate cancer screening tests, developing risk prediction models in different populations, and contributing toward knowledge of prostate cancer race-specific risk factors.

1)
To develop a risk prediction model by combining demographic, clinical, PSA, and laboratory data to improve screening accuracy of prostate cancer in a cohort of subjects with biopsy diagnoses ( 1-no evidence of malignancy vs. 2-prostate cancer).

2)

Use the sequential testing approaches to assess the performance of various combinations in detecting prostate cancer:
• Age & PSA
• BMI & PSA
• PSA test 1 & PSA test 2 (repeated data)

3)

Use Multiple Factor Analysis to integrate demographic/clinical data, biomarkers isolated from blood samples, biomarkers isolated from urine samples, and genetic analysis to describe a cohort of subjects with biopsy diagnoses ( 1-no evidence of malignancy vs. 2-prostate cancer).