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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.

研究概览

研究类型

观察性的

注册 (估计的)

3000

联系人和位置

本节提供了进行研究的人员的详细联系信息,以及有关进行该研究的地点的信息。

学习地点

    • Hunan
      • Changsha、Hunan、中国、410008
        • Xiangya Hospital, Central South University

参与标准

研究人员寻找符合特定描述的人,称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 成人
  • 年长者

接受健康志愿者

不

取样方法

非概率样本

研究人群

The study population consists of patients with pathologically confirmed prostate cancer from multiple participating hospitals led by Xiangya Hospital. Eligible patients underwent prostate biopsy with available biopsy pathology specimens and relevant clinical and imaging data for assessment of lymph node and distant metastasis. Retrospective clinical and pathological data will be collected for development and validation of an artificial intelligence-based metastasis prediction model.

描述

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.

学习计划

本节提供研究计划的详细信息,包括研究的设计方式和研究的衡量标准。

研究是如何设计的?

设计细节

队列和干预

团体/队列
干预/治疗
Prostate Cancer Cohort
Patients with prostate cancer undergoing biopsy pathology assessment for development and validation of an AI-based metastasis prediction model.
Artificial intelligence-assisted analysis of prostate cancer biopsy pathology specimens for prediction of lymph node and distant metastasis risk.

研究衡量的是什么?

主要结果指标

结果测量
措施说明
大体时间
Prediction of Regional Lymph Node Metastasis
大体时间:Baseline
Assessment of the ability of the artificial intelligence-based model using prostate cancer biopsy pathology to predict regional lymph node metastasis.
Baseline

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

调查人员

  • 首席研究员:Yi Cai、Xiangya Hospital Central South University Department of Urology

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查,以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (实际的)

2026年1月8日

初级完成 (估计的)

2026年8月1日

研究完成 (估计的)

2026年8月1日

研究注册日期

首次提交

2026年6月16日

首先提交符合 QC 标准的

2026年6月16日

首次发布 (实际的)

2026年6月22日

研究记录更新

最后更新发布 (实际的)

2026年6月22日

上次提交的符合 QC 标准的更新

2026年6月16日

最后验证

2026年5月1日

更多信息

与本研究相关的术语

其他研究编号

  • 2026010083_4

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)?

不

IPD 计划说明

Except for essential information, no other information will be shared to protect patient privacy.

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索,没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求,请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改,我们的网站上也会自动更新.

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