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Prognostic and predictive value of machine-learning based gene signatures and pathomics features in patients with prostate cancer after radical resection

Prognostic and predictive value of machine-learning based gene signatures and pathomics features in patients with prostate cancer after radical resection

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400085748
Enrollment
Unknown
Registered
2024-06-18
Start date
2024-07-01
Completion date
Unknown
Last updated
2024-07-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

prostate cancer

Interventions

Patients with prostate cancer were divided into recurrence group and non-recurrence group according to whether there was recurrence (including biochemical recurrence, local recurrence and distant meta

Sponsors

The First Affiliated Hospital of Guangzhou Medical University
Lead Sponsor

Eligibility

Sex/Gender
Male
Age
18 Years to 90 Years

Inclusion criteria

Inclusion criteria: Patients diagnosed with prostate cancer who have medical records and specimens retained in the center

Exclusion criteria

Exclusion criteria: a. Non-radical specimens, b. Missing 3 serial section specimens, c. Missing follow-up information, d. Contemporaneous combination with other malignancies or various factors that may affect the outcome of this study.

Design outcomes

Primary

MeasureTime frame
tPSA;—

Secondary

MeasureTime frame
Imaging Examination Results;—

Countries

China

Contacts

Public ContactZhao Zhigang

Department of Urology, The First Affiliated Hospital of Guangzhou Medical University

zgzhaodr@126.com+86 132 5024 8497

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026