Prostate Cancer With PSA Gray ZoneNCT07631806
Intelligent Screening and Precision Diagnosis of Prostate Cancer Based on Multimodal Data
An observational study of Prostate Cancer With PSA Gray Zone, run by Guangxi Medical University at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 45 years and older, men onlyHealthy volunteers can take part
- Conditions the study is for
- Prostate Cancer With PSA Gray Zone
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 May 2026to 1 May 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 45 years and older
- Men only
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- Age ≥45 years, male
- Presenting with abnormal serum PSA (≥4 ng/mL), abnormal digital rectal examination, or suspicious lesions on prostate ultrasound
- Undergoing prostate biopsy with definitive pathological results
- Signed informed consent
Who cannot join
- Previously diagnosed with prostate cancer and receiving surgery, radiotherapy, or endocrine therapy
- With other malignancies
- Critical missing clinical data (e.g., missing PSA value, incomplete ultrasound report)
Trial locations and contacts
1 site in 1 country.
China1 site
Guangxi Medical University First Affiliated Hospital
Nan'ning, Guangxi
About this study
Official title: 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.
Read the 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.
- Conditions
- Prostate Cancer With PSA Gray Zone
- Keywords
- Prostate cancer, Multimodal data / Multimodal artificial intelligence, PSA gray zone (4-10 ng/mL), ctDNA liquid biopsy, Artificial intelligence (AI) diagnosis, Prostate ultrasound, Unnecessary biopsy reduction, DNA methylation, Transrectal ultrasound (TRUS), Convolutional neural network (ResNet/DenseNet), Precision medicine
- Medical subject headings
- Prostatic Neoplasms, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Measured after all participants have completed biopsy and obtained pathological diagnosis (approximately within the 2-year study period).
Secondary outcomes (1)
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.
Measured: Measured after all participants have completed biopsy and obtained pathological diagnosis (approximately within the 2-year study period).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jun 2026
- Study start1 May 2026
- Primary completion1 May 2028 (estimated)
- Study completion1 May 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (16)Papers the sponsor has linked to this study.
- Other trials for Prostate Cancer With PSA Gray ZoneStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Guangxi Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Fubo Wang, Guangxi Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07631806
- Sponsor's protocol ID
- GXMU-IRB-APR-2026-034
- Status last verified
- May 2026
- First submitted
- 2 Jun 2026
- Last updated
- 17 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Wang X, Xie Y, Zheng X, Liu B, Chen H, Li J, Ma X, Xiang J, Weng G, Zhu W, Wang G, Fang Y, Cheng H, Xie L. A prospective multi-center randomized comparative trial evaluating outcomes of transrectal ultrasound (TRUS)-guided 12-core systematic biopsy, mpMRI-targeted 12-core biopsy, and artificial intelligence ultrasound of prostate (AIUSP) 6-core targeted biopsy for prostate cancer diagnosis. World J Urol. 2023 Mar;41(3):653-662. doi: 10.1007/s00345-022-04086-0. Epub 2022 Jul 19.
PubMed 35852595Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, Collaco-Moraes Y, Ward K, Hindley RG, Freeman A, Kirkham AP, Oldroyd R, Parker C, Emberton M; PROMIS study group. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017 Feb 25;389(10071):815-822. doi: 10.1016/S0140-6736(16)32401-1. Epub 2017 Jan 20.
PubMed 28110982Gai W, Sun K. Epigenetic Biomarkers in Cell-Free DNA and Applications in Liquid Biopsy. Genes (Basel). 2019 Jan 9;10(1):32. doi: 10.3390/genes10010032.
PubMed 30634483He WS, Bishop KS. The potential use of cell-free-circulating-tumor DNA as a biomarker for prostate cancer. Expert Rev Mol Diagn. 2016 Aug;16(8):839-52. doi: 10.1080/14737159.2016.1197121. Epub 2016 Jun 20.
PubMed 27254598Jones PA, Laird PW. Cancer epigenetics comes of age. Nat Genet. 1999 Feb;21(2):163-7. doi: 10.1038/5947.
PubMed 9988266Hamdy FC, Donovan JL, Lane JA, Mason M, Metcalfe C, Holding P, Davis M, Peters TJ, Turner EL, Martin RM, Oxley J, Robinson M, Staffurth J, Walsh E, Bollina P, Catto J, Doble A, Doherty A, Gillatt D, Kockelbergh R, Kynaston H, Paul A, Powell P, Prescott S, Rosario DJ, Rowe E, Neal DE; ProtecT Study Group. 10-Year Outcomes after Monitoring, Surgery, or Radiotherapy for Localized Prostate Cancer. N Engl J Med. 2016 Oct 13;375(15):1415-1424. doi: 10.1056/NEJMoa1606220. Epub 2016 Sep 14.
PubMed 27626136Salman JW, Schoots IG, Carlsson SV, Jenster G, Roobol MJ. Prostate Specific Antigen as a Tumor Marker in Prostate Cancer: Biochemical and Clinical Aspects. Adv Exp Med Biol. 2015;867:93-114. doi: 10.1007/978-94-017-7215-0_7.
PubMed 26530362Abraham NE, Mendhiratta N, Taneja SS. Patterns of repeat prostate biopsy in contemporary clinical practice. J Urol. 2015 Apr;193(4):1178-84. doi: 10.1016/j.juro.2014.10.084. Epub 2014 Oct 18.
PubMed 25444971Serag H, Banerjee S, Saeb-Parsy K, Irving S, Wright K, Stearn S, Doble A, Gnanapragasam VJ. Risk profiles of prostate cancers identified from UK primary care using national referral guidelines. Br J Cancer. 2012 Jan 31;106(3):436-9. doi: 10.1038/bjc.2011.596. Epub 2012 Jan 12.
PubMed 22240787Toren P, Razik R, Trachtenberg J. Catastrophic sepsis and hemorrhage following transrectal ultrasound guided prostate biopsies. Can Urol Assoc J. 2010 Feb;4(1):E12-4. doi: 10.5489/cuaj.785.
PubMed 20174484Anderson E, Leahy O, Cheng AC, Grummet J. Risk factors for infection following prostate biopsy - a case control study. BMC Infect Dis. 2015 Dec 23;15:580. doi: 10.1186/s12879-015-1328-7.
PubMed 26700859Cornford P, van den Bergh RCN, Briers E, Van den Broeck T, Brunckhorst O, Darraugh J, Eberli D, De Meerleer G, De Santis M, Farolfi A, Gandaglia G, Gillessen S, Grivas N, Henry AM, Lardas M, van Leenders GJLH, Liew M, Linares Espinos E, Oldenburg J, van Oort IM, Oprea-Lager DE, Ploussard G, Roberts MJ, Rouviere O, Schoots IG, Schouten N, Smith EJ, Stranne J, Wiegel T, Willemse PM, Tilki D. EAU-EANM-ESTRO-ESUR-ISUP-SIOG Guidelines on Prostate Cancer-2024 Update. Part I: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2024 Aug;86(2):148-163. doi: 10.1016/j.eururo.2024.03.027. Epub 2024 Apr 13.
PubMed 38614820Han B, Zheng R, Zeng H, Wang S, Sun K, Chen R, Li L, Wei W, He J. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024 Feb 2;4(1):47-53. doi: 10.1016/j.jncc.2024.01.006. eCollection 2024 Mar.
PubMed 39036382Kimura T, Egawa S. Epidemiology of prostate cancer in Asian countries. Int J Urol. 2018 Jun;25(6):524-531. doi: 10.1111/iju.13593. Epub 2018 May 8.
PubMed 29740894Kratzer TB, Mazzitelli N, Star J, Dahut WL, Jemal A, Siegel RL. Prostate cancer statistics, 2025. CA Cancer J Clin. 2025 Nov-Dec;75(6):485-497. doi: 10.3322/caac.70028. Epub 2025 Sep 2.
PubMed 40892160
Study at a glance
From the registry record, last updated by the sponsor on 17 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Validation of an AI-Assisted Multimodal Imaging-Pathology Fusion System for Precision Diagnosis and Biopsy Guidance in Patients With Suspected Prostate Cancer
- ClinicalTrials.gov ID
- NCT07631806
- Acronym / sponsor ID
- GXMU-IRB-APR-2026-034
- Conditions
- Prostate Cancer With PSA Gray Zone
- Keywords
- Prostate cancer, Multimodal data / Multimodal artificial intelligence, PSA gray zone (4-10 ng/mL), ctDNA liquid biopsy, Artificial intelligence (AI) diagnosis, Prostate ultrasound, Unnecessary biopsy reduction, DNA methylation
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Guangxi Medical UniversityUniversity, hospital or other
- Study start
- 1 May 2026 (actual)
- Primary completion
- 1 May 2028 (estimated)
- Study completion
- 1 May 2028 (estimated)
- First posted
- 8 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 45 years and older
- Sex
- Male only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- Age ≥45 years, male
- Presenting with abnormal serum PSA (≥4 ng/mL), abnormal digital rectal examination, or suspicious lesions on prostate ultrasound
- Undergoing prostate biopsy with definitive pathological results
- Signed informed consent
Key exclusion criteria (3 listed)
- Previously diagnosed with prostate cancer and receiving surgery, radiotherapy, or endocrine therapy
- With other malignancies
- Critical missing clinical data (e.g., missing PSA value, incomplete ultrasound report)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
1 secondary outcome
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).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Jun 2026.
- Background: PubMed 35852595Wang X, Xie Y, Zheng X, Liu B, Chen H, Li J, Ma X, Xiang J, Weng G, Zhu W, Wang G, Fang Y, Cheng H, Xie L. A prospective multi-center randomized comparative trial evaluating outcomes of transrectal ultrasound (TRUS)-guided 12-core systematic biopsy, mpMRI-targeted 12-core biopsy, and artificial intelligence ultrasound of prostate (AIUSP) 6-core targeted biopsy for prostate cancer diagnosis. World J Urol. 2023 Mar;41(3):653-662. doi: 10.1007/s00345-022-04086-0. Epub 2022 Jul 19.
- Background: PubMed 28110982Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, Collaco-Moraes Y, Ward K, Hindley RG, Freeman A, Kirkham AP, Oldroyd R, Parker C, Emberton M; PROMIS study group. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017 Feb 25;389(10071):815-822. doi: 10.1016/S0140-6736(16)32401-1. Epub 2017 Jan 20.
- Background: PubMed 30634483Gai W, Sun K. Epigenetic Biomarkers in Cell-Free DNA and Applications in Liquid Biopsy. Genes (Basel). 2019 Jan 9;10(1):32. doi: 10.3390/genes10010032.
- Background: PubMed 27254598He WS, Bishop KS. The potential use of cell-free-circulating-tumor DNA as a biomarker for prostate cancer. Expert Rev Mol Diagn. 2016 Aug;16(8):839-52. doi: 10.1080/14737159.2016.1197121. Epub 2016 Jun 20.
- Background: PubMed 9988266Jones PA, Laird PW. Cancer epigenetics comes of age. Nat Genet. 1999 Feb;21(2):163-7. doi: 10.1038/5947.
- Background: PubMed 27626136Hamdy FC, Donovan JL, Lane JA, Mason M, Metcalfe C, Holding P, Davis M, Peters TJ, Turner EL, Martin RM, Oxley J, Robinson M, Staffurth J, Walsh E, Bollina P, Catto J, Doble A, Doherty A, Gillatt D, Kockelbergh R, Kynaston H, Paul A, Powell P, Prescott S, Rosario DJ, Rowe E, Neal DE; ProtecT Study Group. 10-Year Outcomes after Monitoring, Surgery, or Radiotherapy for Localized Prostate Cancer. N Engl J Med. 2016 Oct 13;375(15):1415-1424. doi: 10.1056/NEJMoa1606220. Epub 2016 Sep 14.
- Background: PubMed 26530362Salman JW, Schoots IG, Carlsson SV, Jenster G, Roobol MJ. Prostate Specific Antigen as a Tumor Marker in Prostate Cancer: Biochemical and Clinical Aspects. Adv Exp Med Biol. 2015;867:93-114. doi: 10.1007/978-94-017-7215-0_7.
- Background: PubMed 25444971Abraham NE, Mendhiratta N, Taneja SS. Patterns of repeat prostate biopsy in contemporary clinical practice. J Urol. 2015 Apr;193(4):1178-84. doi: 10.1016/j.juro.2014.10.084. Epub 2014 Oct 18.
- Background: PubMed 22240787Serag H, Banerjee S, Saeb-Parsy K, Irving S, Wright K, Stearn S, Doble A, Gnanapragasam VJ. Risk profiles of prostate cancers identified from UK primary care using national referral guidelines. Br J Cancer. 2012 Jan 31;106(3):436-9. doi: 10.1038/bjc.2011.596. Epub 2012 Jan 12.
- Background: PubMed 20174484Toren P, Razik R, Trachtenberg J. Catastrophic sepsis and hemorrhage following transrectal ultrasound guided prostate biopsies. Can Urol Assoc J. 2010 Feb;4(1):E12-4. doi: 10.5489/cuaj.785.
- Background: PubMed 26700859Anderson E, Leahy O, Cheng AC, Grummet J. Risk factors for infection following prostate biopsy - a case control study. BMC Infect Dis. 2015 Dec 23;15:580. doi: 10.1186/s12879-015-1328-7.
- Background: PubMed 38614820Cornford P, van den Bergh RCN, Briers E, Van den Broeck T, Brunckhorst O, Darraugh J, Eberli D, De Meerleer G, De Santis M, Farolfi A, Gandaglia G, Gillessen S, Grivas N, Henry AM, Lardas M, van Leenders GJLH, Liew M, Linares Espinos E, Oldenburg J, van Oort IM, Oprea-Lager DE, Ploussard G, Roberts MJ, Rouviere O, Schoots IG, Schouten N, Smith EJ, Stranne J, Wiegel T, Willemse PM, Tilki D. EAU-EANM-ESTRO-ESUR-ISUP-SIOG Guidelines on Prostate Cancer-2024 Update. Part I: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2024 Aug;86(2):148-163. doi: 10.1016/j.eururo.2024.03.027. Epub 2024 Apr 13.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Prostate Cancer With PSA Gray Zone, run by Guangxi Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 17 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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).). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07631806, which also lists 16 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Jun 2026.