Intelligent Screening and Precision Diagnosis of Prostate Cancer Based on Multimodal Data

June 2, 2026 updated by: Fubo Wang, Guangxi Medical University

Prospective Validation of an AI-Assisted Multimodal Imaging-Pathology Fusion System for Precision Diagnosis and Biopsy Guidance in Patients With Suspected Prostate Cancer

This project aims to develop a precision screening and diagnostic solution for prostate cancer based on multimodal artificial intelligence, focusing on addressing the diagnostic challenge in patients within the PSA "gray zone" of 4-10 ng/mL. The project will integrate multidimensional information including ctDNA liquid biopsy, routine laboratory data, and prostate ultrasound images to develop three models: a ctDNA-based multimodal AI prediction model, a routine laboratory data-assisted decision model, and an ultrasound image AI-assisted diagnostic model. On this basis, a multimodal AI fusion decision system will be established to automatically generate individualized risk assessment reports and diagnostic recommendations. Additionally, a closed-loop mechanism of "clinical use - data feedback - model optimization" will be constructed to continuously iterate model parameters using pathological gold standards, thereby improving predictive accuracy in our hospital population. The project will form a generalizable precision diagnostic workflow, reduce unnecessary biopsies in "gray zone" patients, and provide an implementable in-hospital solution for precision medicine in prostate cancer.

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

Background: Prostate cancer (PCa) is the second most common malignancy in men worldwide. In China, the average annual growth rate of PCa incidence is as high as 7.2%. Current diagnostic pathways rely on transrectal ultrasound (TRUS)-guided prostate biopsy. However, serum PSA, the main decision-making indicator for biopsy, is not cancer-specific and has severely insufficient specificity. Many men with elevated PSA undergo unnecessary invasive biopsies. The diagnostic challenge is particularly prominent in the PSA "gray zone" of 4-10 ng/mL.

Objectives: This study aims to develop a precision screening and diagnostic solution for prostate cancer based on multimodal artificial intelligence, focusing on addressing the diagnostic challenge in patients within the PSA gray zone. Specific objectives include: (1) improving screening efficiency to quickly identify high-risk individuals and avoid over-examination; (2) solving the diagnostic gray zone problem; (3) reducing unnecessary biopsies through non-invasive or minimally invasive precision tools; and (4) achieving personalized management through risk stratification.

Study Design: Prospective enrollment of suspected prostate cancer patients. Total sample size is no less than 500 cases, divided into training set (approximately 400 cases) and validation set (approximately 100 cases) at an 8:2 ratio.

Eligibility Criteria:

Inclusion criteria: (1) age ≥45 years, male; (2) presenting with abnormal serum PSA (≥4 ng/mL), abnormal digital rectal examination, or suspicious lesions on prostate ultrasound; (3) undergoing prostate biopsy with definitive pathological results; (4) signed informed consent.

Exclusion criteria: (1) previously diagnosed with prostate cancer and receiving surgery, radiotherapy, or endocrine therapy; (2) with other malignancies; (3) critical missing clinical data (e.g., missing PSA value, incomplete ultrasound report).

Study Interventions/Assessments: All enrolled patients complete the following data collection: (1) serum PSA and free PSA; (2) routine laboratory tests including complete blood count, liver and kidney function; (3) transrectal or transabdominal prostate ultrasound with images stored in DICOM format and prostate volume recorded; (4) post-prostate massage urine for ctDNA methylation target detection; (5) digital rectal examination results, age, family history, medical history; (6) pathological diagnosis results from biopsy as gold standard.

Models to be Developed:

Tool 1 - ctDNA multimodal AI prediction model: using ctDNA methylation results combined with age, PSA, and prostate volume. Logistic regression and random forest will be compared.

Tool 2 - Routine laboratory data-assisted decision model: integrating structured data including complete blood count, liver and kidney function, PSA, free PSA, age, and prostate volume. XGBoost and LightGBM with LASSO feature reduction will be used.

Tool 3 - Prostate ultrasound image AI-assisted diagnostic model: using convolutional neural network (ResNet or DenseNet architecture) for deep learning modeling. The model outputs lesion probability heatmaps and malignancy probability scores.

Multimodal Fusion Strategy: The three model outputs will be combined according to preset fusion rules to generate comprehensive risk stratification (low/moderate/high concern). Diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and AUC of the fusion solution will be calculated using pathological results as gold standard. The AUC will be compared with that of PSA alone using DeLong test. Stratified analysis will be performed for the PSA 4-10 ng/mL gray zone subgroup. Decision curve analysis (DCA) will be used to evaluate clinical net benefit.

Closed-loop Optimization: All pathological results will be periodically returned to the model management system in a de-identified manner, and quarterly iterative optimization of the three specialized models and fusion rules will be conducted.

Study Duration: May 2026 to May 2028 (approximately 2 years).

Funding: This is a hospital-level research project with an application fund of 50,000 RMB.

Study Type

Observational

Enrollment (Estimated)

500

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

The study population consists of male patients aged ≥45 years with suspected prostate cancer, presenting with abnormal serum PSA (≥4 ng/mL), abnormal digital rectal examination, or suspicious lesions on prostate ultrasound, who are scheduled to undergo prostate biopsy. Participants will be prospectively enrolled from patients presenting to the hospital for PSA abnormality, lower urinary tract symptoms, or active screening.

The total planned sample size is no less than 500 cases, divided into a training set (approximately 400 cases) and a validation set (approximately 100 cases) at an 8:2 ratio.

Excluded are patients with prior diagnosis of prostate cancer receiving active treatment, those with other malignancies, and those with critical missing clinical data.

Description

Inclusion Criteria:

  1. Age ≥45 years, male
  2. Presenting with abnormal serum PSA (≥4 ng/mL), abnormal digital rectal examination, or suspicious lesions on prostate ultrasound
  3. Undergoing prostate biopsy with definitive pathological results
  4. Signed informed consent

Exclusion Criteria:

  1. Previously diagnosed with prostate cancer and receiving surgery, radiotherapy, or endocrine therapy
  2. With other malignancies
  3. Critical missing clinical data (e.g., missing PSA value, incomplete ultrasound report)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Training Set
Approximately 400 cases. This group will be used to develop and internally validate the three specialized models: (1) ctDNA multimodal AI prediction model, (2) routine laboratory data-assisted decision model, and (3) prostate ultrasound image AI-assisted diagnostic model. Five-fold cross-validation will be used for algorithm comparison and hyperparameter tuning.
Validation Set
Approximately 100 cases. This independent validation set will be used to evaluate the diagnostic performance of the multimodal fusion decision system. Sensitivity, specificity, positive predictive value, negative predictive value, and AUC will be calculated using pathological results as the gold standard. DeLong test will be used to compare AUC with PSA alone. Decision curve analysis (DCA) will be used to evaluate clinical net benefit. Subgroup analysis will be performed for the PSA 4-10 ng/mL gray zone.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area Under the Curve (AUC) of the multimodal AI fusion diagnostic system
Time Frame: Measured after all participants have completed biopsy and obtained pathological diagnosis (approximately within the 2-year study period).
The AUC of the fusion model in distinguishing clinically significant prostate cancer from non-cancer or indolent cancer, using pathological biopsy results as the gold standard.
Measured after all participants have completed biopsy and obtained pathological diagnosis (approximately within the 2-year study period).

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity and Specificity of the Multimodal AI Fusion Diagnostic System
Time Frame: Measured after all participants have completed biopsy and obtained pathological diagnosis (approximately within the 2-year study period).
The sensitivity and specificity of the fusion model in detecting clinically significant prostate cancer, using pathological biopsy results as the gold standard.
Measured after all participants have completed biopsy and obtained pathological diagnosis (approximately within the 2-year study period).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Guangxi Medical University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

June 1, 2026

Primary Completion (Estimated)

June 1, 2028

Study Completion (Estimated)

June 1, 2028

Study Registration Dates

First Submitted

June 2, 2026

First Submitted That Met QC Criteria

June 2, 2026

First Posted (Actual)

June 8, 2026

Study Record Updates

Last Update Posted (Actual)

June 8, 2026

Last Update Submitted That Met QC Criteria

June 2, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • GXMU-IRB-APR-2026-034

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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