An observational study in Prostate Cancer With PSA Gray Zone, sponsored by Guangxi Medical University. Active, not recruiting at 1 site in China. Open to male participants aged 45 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-06-17.
Sponsored by Guangxi Medical University · Observational
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.
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.
6,370 studies on the registry are indexed under Prostatic Neoplasms; 1,400 are open to participants now.
This study's planned enrollment of 500 is above the median of 200 across 1,181 observational studies indexed under Prostatic Neoplasms.
Browse Prostatic Neoplasms studies →Guangxi Medical University is the lead sponsor of 68 studies on the registry; 14 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Exclusion Criteria:
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.
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.
Area Under the Curve (AUC) of the multimodal AI fusion diagnostic system
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.
Time frame: Measured after all participants have completed biopsy and obtained pathological diagnosis (approximately within the 2-year study period).
Sensitivity and Specificity of the Multimodal AI Fusion Diagnostic System
The sensitivity and specificity of the fusion model in detecting clinically significant prostate cancer, using pathological biopsy results as the gold standard.
Time frame: Measured after all participants have completed biopsy and obtained pathological diagnosis (approximately within the 2-year study period).
This study is active, not recruiting, as verified in May 2026. You cannot join it, but the record below documents what was studied.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Guangxi Medical University