Application of a Model Based on Plasma cfDNA Fragmentomics in the Early Diagnosis of Prostate Cancer.
The goal of this diagnostic test is to obtain multiple cell-free DNA （cfDNA） fragment profiles of subjects by whole genome sequencing based on plasma cfDNA, build a prostate cancer prediction model by machine learning, and to validate the efficacy of this model in patients who need to undergo needle prostate biopsy base on their prostate-specific antigen（PSA） or clinical or imaging evidence. Therefore, this study aims to explore the efficacy of this prostate cancer prediction model in distinguishing between patients with PSA gray zone (4-10 ng/ml) in the diagnosis of prostate cancer and patients with clinically significant prostate cancer.
• Male, 18-80 years old;
• PSA: 4-10ng/ml;
• Patients scheduled for prostate biopsy：
⁃ fPSA（free PSA）/PSA \< 0.16 or PSAD（PSA density） \> 0.15 (ng/mL/cm³) or PSAV（PSA velocity） \> 0.75 (ng/mL/year) ② positive DRE （digital rectal examination） ③suspicious positive lesions on ultrasound/MRI).