AI-based Prediction of Prostate Cancer Metastasis Using Biopsy Pathology
Development and Validation of an AI-Based Metastasis Prediction Model Using Prostate Cancer Biopsy Pathology
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.
Studieoversigt
Status
Betingelser
Intervention / Behandling
Undersøgelsestype
Tilmelding (Anslået)
Kontakter og lokationer
Studiesteder
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Hunan
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Changsha, Hunan, Kina, 410008
- Xiangya Hospital, Central South University
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Deltagelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Prøveudtagningsmetode
Studiebefolkning
Beskrivelse
Inclusion Criteria:
- Male patients aged between 18 and 90 years; Patients who underwent prostate biopsy due to elevated prostate-specific antigen (PSA), abnormal digital rectal examination (DRE), or abnormal imaging findings, were pathologically diagnosed with prostate cancer, and had available prostate biopsy pathology specimens; Patients who underwent radical prostatectomy with extended pelvic lymph node dissection (ePLND) or pelvic lymph node dissection (PLND), with definitive pathological information regarding lymph node metastasis; Patients who underwent PSMA PET/CT, MRI, bone scintigraphy, or prostate MRI capable of identifying regional lymph node metastasis or distant metastasis; Adequate cardiac, pulmonary, hepatic, and renal function; Eastern Cooperative Oncology Group (ECOG) performance status of 0-1; Expected survival time greater than 1 year; Written informed consent signed by the patient or legally authorized representative.
Exclusion Criteria:
- History of other malignancies; Severe dysfunction of major organs, including cardiac, pulmonary, hepatic, or renal insufficiency, or an expected survival time of less than 1 year; Prostate biopsy pathology specimens with inadequate whole-slide image scanning quality or failure of quality control assessment; Patients with prostate cancer diagnosed from transurethral resection specimens.
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Kohorter og interventioner
Gruppe / kohorte |
Intervention / Behandling |
|---|---|
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Prostate Cancer Cohort
Patients with prostate cancer undergoing biopsy pathology assessment for development and validation of an AI-based metastasis prediction model.
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Artificial intelligence-assisted analysis of prostate cancer biopsy pathology specimens for prediction of lymph node and distant metastasis risk.
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Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
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Prediction of Regional Lymph Node Metastasis
Tidsramme: Baseline
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Assessment of the ability of the artificial intelligence-based model using prostate cancer biopsy pathology to predict regional lymph node metastasis.
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Baseline
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Samarbejdspartnere og efterforskere
Efterforskere
- Ledende efterforsker: Yi Cai, Xiangya Hospital Central South University Department of Urology
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Primær færdiggørelse (Anslået)
Studieafslutning (Anslået)
Datoer for studieregistrering
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Andre undersøgelses-id-numre
- 2026010083_4
Plan for individuelle deltagerdata (IPD)
Planlægger du at dele individuelle deltagerdata (IPD)?
IPD-planbeskrivelse
Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter
Studerer et amerikansk FDA-reguleret lægemiddelprodukt
Studerer et amerikansk FDA-reguleret enhedsprodukt
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