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Multimodal Imaging and Digital Pathology for Prostate Cancer Prediction

2026年5月25日 更新者:Fubo Wang、Guangxi Medical University

A Multicenter Study of a Deep Learning Model Based on Spatial Registration of Multimodal Imaging and Digital Pathology for Predicting Clinically Significant Prostate Cancer

This is a multicenter observational study. A deep learning model integrated with multimodal imaging and digital pathology spatial registration is built based on preoperative multiparametric magnetic resonance imaging, transrectal ultrasound and postoperative digital pathological whole slide images. The study is designed to achieve accurate prediction of clinically significant prostate cancer and non-invasive risk stratification. Unnecessary prostate biopsy and overdiagnosis can be reduced to support the optimization of clinical diagnosis and treatment strategies.

研究概览

详细说明

This prospective and retrospective multicenter observational study enrolls patients with suspected prostate cancer who receive standardized preoperative multiparametric magnetic resonance imaging, transrectal ultrasound examination, followed by prostate biopsy or radical prostatectomy. Complete clinical data including age, BMI, prostate specific antigen indicators, PI-RADS v2.1 scores, Gleason score and ISUP grading are collected from all eligible participants.

Biomechanically constrained non-rigid spatial registration technique is applied to achieve precise alignment between preoperative multimodal images and postoperative digital pathological whole slide images using high-quality multicenter datasets. A transformer-based multimodal deep learning fusion model is developed to analyze correlations between macroscopic imaging features and microscopic pathological heterogeneity, thereby establishing an interpretable artificial intelligence framework for clinically significant prostate cancer prediction.

Comprehensive model validation is conducted via internal cross-validation, external multicenter independent verification and international public datasets. Decision curve analysis and clinical impact curve are applied to assess clinical applicability. The model serves as an intelligent auxiliary tool to refine biopsy strategies, avoid redundant puncture and excessive treatment, and facilitate early precise diagnosis and risk stratification of prostate cancer.

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。

学习联系方式

学习地点

    • Guangxi
      • Liuzhou、Guangxi、中国、545006
        • 招聘中
        • Liuzhou People's Hospital Affiliated to Guangxi Medical University
        • 接触:

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

This is a prospective and retrospective multicenter cohort study. The study population consists of consecutive male subjects aged 40-90 years who are scheduled to undergo or have undergone prostate biopsy or radical prostatectomy, with complete standard-of-care preoperative multiparametric MRI (mpMRI), transrectal ultrasound (TRUS) images, and corresponding pathological diagnosis results. The collected data include:

  1. Preoperative mpMRI and TRUS images
  2. Digital whole-slide images of prostate biopsy specimens
  3. Digital whole-slide images of radical prostatectomy specimens (if performed) The prospective cohort will include newly enrolled subjects who provide written informed consent, while the retrospective cohort will include historical subjects with complete imaging, pathology slide, and clinical data from participating centers.

描述

Inclusion Criteria:

  1. Subjects who are scheduled to undergo or have undergone prostate biopsy or radical prostatectomy.
  2. Subjects who have completed standard-of-care preoperative multiparametric MRI (mpMRI) and transrectal ultrasound (TRUS) examinations.
  3. Subjects with complete pathological diagnosis results available.
  4. Age between 40 and 90 years.
  5. Able and willing to provide written informed consent (for prospective cohort participants only).

Exclusion Criteria:

  1. Prior history of pelvic radiation therapy or radical prostatectomy.
  2. Incomplete or poor-quality mpMRI or TRUS images (e.g., motion artifacts, insufficient sequences).
  3. Concurrent other primary malignant tumors.
  4. Severe systemic diseases that may affect the evaluation of the prostate.
  5. Subjects with incomplete clinical or pathological data.
  6. Contraindications to MRI examination (e.g., incompatible metallic implants, severe claustrophobia).

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

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
Area Under the Receiver Operating Characteristic Curve (AUC) for predicting clinically significant prostate cancer (csPCa)
大体时间:Baseline (at the time of imaging/pathology data collection)
The diagnostic performance of the multimodal deep learning model in predicting clinically significant prostate cancer using preoperative imaging data from this prospective and retrospective multicenter cohort. The AUC will be calculated to evaluate the model's discriminative ability.
Baseline (at the time of imaging/pathology data collection)

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:Fubo Wang, MD、Guangxi Medical University

出版物和有用的链接

负责输入研究信息的人员自愿提供这些出版物。这些可能与研究有关。

一般刊物

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)

2025年5月30日

初级完成 (估计的)

2030年6月30日

研究完成 (估计的)

2030年12月31日

研究注册日期

首次提交

2026年5月13日

首先提交符合 QC 标准的

2026年5月25日

首次发布 (实际的)

2026年5月29日

研究记录更新

最后更新发布 (实际的)

2026年5月29日

上次提交的符合 QC 标准的更新

2026年5月25日

最后验证

2026年5月1日

更多信息

与本研究相关的术语

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

This study does not have a plan to share individual participant data due to institutional and ethical restrictions.

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

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