Using a new blood test to improve early diagnosis of prostate cancer

Application of a Model Based on Plasma cfDNA Fragmentomics in the Early Diagnosis of Prostate Cancer.

This study is testing a new blood test to see if it can help men with borderline PSA levels find out if they have serious prostate cancer before they get a biopsy.

Quick facts

• Study type: Observational
• Enrollment: 1100 (estimated)
• Ages: 18 Years to 80 Years
• Sex: Male
• Sponsor: Shanghai Changzheng Hospital (Academic / other)
• Drugs / interventions: chemotherapy, immunotherapy
• Locations: 1 site (Shanghai, Shanghai Municipality)
• Trial ID: NCT06509919 on ClinicalTrials.gov

What this trial studies

This observational study aims to develop a prostate cancer prediction model using machine learning based on profiles of cell-free DNA (cfDNA) obtained from blood samples. The study will focus on men with elevated prostate-specific antigen (PSA) levels in the 'gray zone' (4-10 ng/ml) who are scheduled for prostate biopsy. By analyzing cfDNA through whole genome sequencing, the researchers hope to distinguish between patients with clinically significant prostate cancer and those without. The efficacy of this prediction model will be validated against clinical and imaging evidence.

Who should consider this trial

Good fit: Ideal candidates are males aged 18-80 with PSA levels between 4-10 ng/ml who are scheduled for a prostate biopsy.

Not a fit: Patients with a prior diagnosis of any malignancy within the last 5 years or those who have received prior treatment for prostate cancer will not benefit from this study.

Why it matters

Potential benefit: If successful, this approach could lead to earlier and more accurate diagnosis of prostate cancer, potentially reducing unnecessary biopsies.

How similar studies have performed: While there have been studies exploring cfDNA in cancer diagnostics, this specific approach using machine learning for prostate cancer prediction is relatively novel.

Eligibility criteria

Inclusion Criteria:

1. Male, 18-80 years old;
2. PSA: 4-10ng/ml;
3. 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).

Exclusion Criteria:

1. Patients with a prior diagnosis of any malignancy within 5 years;
2. Patients who have undergone prior transurethral resection or enucleation of the prostate;
3. Patients who have received prior treatment for prostate cancer, including but not limited to endocrine therapy, targeted therapy, and immunotherapy;
4. Patients with long-term use of anticoagulant and antiplatelet aggregation drugs (anticoagulant discontinued for less than 1 week);
5. Received any form of oncological treatment, including surgery, radiotherapy/chemotherapy, endocrine therapy, targeted therapy, and immunotherapy prior to enrollment for blood sampling;
6. concurrently suffering from other serious systemic diseases that, in the opinion of the investigator, may interfere with the treatment, evaluation and compliance of this trial, including serious respiratory, circulatory, neurological, psychiatric, gastrointestinal, endocrine, immune, urinary and other systemic diseases;
7. Organ transplant recipients or prior non-autologous (allogeneic) bone marrow or stem cell transplant population;
8. Subjects who have had a blood transfusion 1 month prior to the blood draw;
9. Patients who are participating in other clinical trials, or who have participated in other clinical trials that have been completed less than 1 year ago;
10. Patients who, in the judgment of the investigator, are not suitable for participation in this clinical trial;
11. Patients who meet any of the above criteria may not be included as subjects.

Where this trial is running

Shanghai, Shanghai Municipality

• Changzheng hospital — Shanghai, Shanghai Municipality, China (Recruiting)

Study contacts

• Study coordinator: Shancheng Ren, MD,PhD

Conditions Prostate Cancer; Elevated PSA; Biopsy; Prostate cancer; Prostate neoplasms; cfDNA; Machine learning