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NCT Number: NCT07614256

Multimodal Imaging and Digital Pathology for Prostate Cancer Prediction

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.

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Key information

Age range

40 year–90 year

Sex eligibility

Male

Study type

Observational

Primary location

Liuzhou People's Hospital Affiliated to Guangxi Medical University

Liuzhou, Guangxi, 545006, China

Location status: Recruiting

Location contact

Caigou Shi, MD

CONTACT

[email protected]

+86 13677729003

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

No Intervention: Observational Cohort

Other

This is an observational study. No new treatment, drug, device, or procedure is being administered to participants. Only standard-of-care clinical data, imaging, and pathology records are collected and analyzed.

Primary outcomes

  1. Area Under the Receiver Operating Characteristic Curve (AUC) for predicting clinically significant prostate cancer (csPCa)

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Caigou Shi, MD

CONTACT

[email protected]

+86 13677729003

Sponsors and collaborators

Lead sponsor

Guangxi Medical University

Other

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2030
Study completion
2030
First posted
May 29, 2026
Registry last updated
May 29, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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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