Xiangya Hospital, Central South University
Changsha, Hunan, 410008, China
NCT Number: NCT07660276
This observational study aims to develop and validate an artificial intelligence-based model using prostate cancer biopsy pathology to predict lymph node metastasis and distant metastasis in patients with prostate cancer. The main questions it aims to answer are:
Can artificial intelligence-assisted analysis of prostate cancer biopsy pathology accurately predict lymph node metastasis? Can the model accurately predict distant metastasis and assess metastatic risk in patients with prostate cancer?
Researchers aim to evaluate whether the model can provide additional information for clinical decision-making and surgical planning.
Participants will:
Provide prostate biopsy pathology specimens and related clinical information; Undergo assessment of lymph node and distant metastatic status based on clinical and imaging data; Be included in the development and validation of the artificial intelligence prediction model.
Interested in participating?
Request Info18 year–90 year
Male
Observational
Changsha, Hunan, 410008, China
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Artificial intelligence-assisted analysis of prostate cancer biopsy pathology specimens for prediction of lymph node and distant metastasis risk.
Time frame: Baseline
Assessment of the ability of the artificial intelligence-based model using prostate cancer biopsy pathology to predict regional lymph node metastasis.
Xiangya Hospital of Central South University
Other
Development and Validation of an AI-Based Metastasis Prediction Model Using Prostate Cancer Biopsy Pathology
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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