ClinicalTrials.gg
RecruitingNCT07614256Updated May 29, 2026

Multimodal Imaging and Digital Pathology for Prostate Cancer Prediction

An observational study in Prostate Cancer (Diagnosis) and Clinically Significant Prostate Cancer, sponsored by Guangxi Medical University. Recruiting at 1 site in China. Open to male participants aged 40 Years to 90 Years. Per ClinicalTrials.gov, last updated 2026-05-29.

Sponsored by Guangxi Medical University · Observational

From the registry’s dates

  • Started May 2025; still recruiting 1 year 5 months later.

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Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
3,000
Ages
40 Years to 90 Years
Sex
Male
01

Where this study stands

Counted across the records on this site · 2026-11-14

Among 541 studies in Prostatic Neoplasms with a site in China — 281 open to participants now, 342 under way, 51 of them phase 3. Prostatic Neoplasms · China.

Planned enrollment
5th of 106
3,000 · median 300
Started
2025
with 77 others
Observational
106 of 541
54 open now
Primary completion
2030-06
later than 329 of 341 live
Registered
496th of 541
by date

Among the 541 studies in Prostatic Neoplasms with a site in China, this one is the 5th largest by planned enrollment of the 106 observational ones that give a count, is one of 78 that started in 2025, is one of 106 observational ones, reaches primary completion later than 329 of the 341 other live ones, and was the 496th registered.

Prostatic Neoplasms

7,047 studies on the registry are indexed under Prostatic Neoplasms; 1,579 are open to participants now.

Browse Prostatic Neoplasms studies →

Open to participants now

281 of the 541 studies in Prostatic Neoplasms with a site in China are open now. Recruiting first, the newest first:

276 more — sign in →

All 281 open studies →

A study can be open while its sites in China have not opened yet or have stopped. Only those recruiting at a site in China →

Who these studies take

Of the 1,228 Prostatic Neoplasms studies recruiting now, 927 (75%) set no upper age limit and 76 (6%) stop at 75 or younger; 67 take children under 18; 82 take healthy volunteers.

This study: 40 Years to 90 Years · male · no healthy volunteers.

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Watch this landscapeDownload CSVNarrow it in search →With an account: a weekly mail of what changed among 541 studies in Prostatic Neoplasms with a site in China, or all of them as a file.
02

Study summary

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.

Read the detailed description

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.

03

Conditions studied

  • Prostate Cancer (Diagnosis)
  • Clinically Significant Prostate Cancer

Keywords

  • Prostate cancer
  • Clinically significant prostate cancer
  • Multiparametric Magnetic Resonance Imaging
  • Transrectal ultrasound
  • Digital pathology
  • Deep learning
  • Artificial intelligence
  • Spatial registration
  • Risk stratification
04

Who can participate

Ages eligible
40 Years to 90 Years
Sexes eligible
Male
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

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.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  1. Subjects who are scheduled to undergo or have undergone prostate 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 (for prospective cohort participants only).

Exclusion criteria

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. to MRI examination (e.g., incompatible metallic implants, severe claustrophobia).
Terms in these criteria, in plain language(3)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

Biopsy and archival tissue

A biopsy takes a small piece of tissue to examine. “Archival tissue” is a sample already taken, for example at diagnosis, and kept by the hospital.

Whether a stored sample will do depends on when it was taken, how much there is and what the study needs. Ask whether the study accepts your stored sample or needs a new biopsy.

Informed consent

Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.

You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.

Contraindication

A reason a treatment, test or procedure should not be given to a particular person, such as an allergy, another illness or a medicine they take.

05

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
3,000 participants (estimated)
Target follow-up
12 Years
Patient registry
Yes

Interventions

  • OtherNo Intervention: Observational Cohort

    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.

06

What researchers measure

Primary outcomes

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

    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.

    Time frame: Baseline (at the time of imaging/pathology data collection)

07

Study locations

1 of 1 sites recruiting
  • Liuzhou People's Hospital Affiliated to Guangxi Medical University
    Liuzhou, Guangxi 545006, China
    Recruiting
08

References and documents

Publications

  • Shao L, Liang C, Yan Y, Zhu H, Jiang X, Bao M, Zang P, Huang X, Zhou H, Nie P, Wang L, Li J, Zhang S, Ren S. An MRI-pathology foundation model for noninvasive diagnosis and grading of prostate cancer. Nat Cancer. 2025 Oct;6(10):1621-1637. doi: 10.1038/s43018-025-01041-x. Epub 2025 Sep 2. PubMed 40897909 ↗
  • Rusu M, Jahanandish H, Vesal S, Li CX, Bhattacharya I, Venkataraman R, Zhou SR, Kornberg Z, Sommer ER, Khandwala YS, Hockman L, Zhou Z, Choi MH, Ghanouni P, Fan RE, Sonn GA. ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies. Eur Urol Oncol. 2025 Apr;8(2):477-485. doi: 10.1016/j.euo.2024.12.012. Epub 2025 Jan 28. PubMed 39880746 ↗
  • Saha A, Hosseinzadeh M, Huisman H. End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction. Med Image Anal. 2021 Oct;73:102155. doi: 10.1016/j.media.2021.102155. Epub 2021 Jun 29. PubMed 34245943 ↗
  • Lee YJ, Moon HW, Choi MH, Eun Jung S, Park YH, Lee JY, Kim DH, Eun Rha S, Kim SH, Lee KW, Choi YJ, Lee YS, Lee W, Lee S, Grimm R, von Busch H, Han D, Lou B, Kamen A. MRI-based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter Prospective Study. Radiology. 2025 Mar;314(3):e232788. doi: 10.1148/radiol.232788. PubMed 40067105 ↗
  • Twilt JJ, Saha A, Bosma JS, Padhani AR, Bonekamp D, Giannarini G, van den Bergh R, Kasivisvanathan V, Obuchowski N, Yakar D, Elschot M, Veltman J, Futterer J, Huisman H, de Rooij M; PI-CAI Consortium. AI-Assisted vs Unassisted Identification of Prostate Cancer in Magnetic Resonance Images. JAMA Netw Open. 2025 Jun 2;8(6):e2515672. doi: 10.1001/jamanetworkopen.2025.15672. PubMed 40512493 ↗
  • Goel S, Shoag JE, Gross MD, Al Hussein Al Awamlh B, Robinson B, Khani F, Baltich Nelson B, Margolis DJ, Hu JC. Concordance Between Biopsy and Radical Prostatectomy Pathology in the Era of Targeted Biopsy: A Systematic Review and Meta-analysis. Eur Urol Oncol. 2020 Feb;3(1):10-20. doi: 10.1016/j.euo.2019.08.001. Epub 2019 Sep 4. PubMed 31492650 ↗
  • Pham THN, Schulze-Hagen MF, Rahnama'i MS. Targeted multiparametric magnetic resonance imaging/transrectal ultrasound-guided (mpMRI/TRUS) fusion prostate biopsy versus systematic random prostate biopsy: A comparative real-life study. Cancer Rep (Hoboken). 2024 Feb;7(2):e1962. doi: 10.1002/cnr2.1962. Epub 2024 Jan 12. PubMed 38217298 ↗
  • Drost FH, Osses DF, Nieboer D, Steyerberg EW, Bangma CH, Roobol MJ, Schoots IG. Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst Rev. 2019 Apr 25;4(4):CD012663. doi: 10.1002/14651858.CD012663.pub2. PubMed 31022301 ↗
  • Moliere S, Hamzaoui D, Ploussard G, Mathieu R, Fiard G, Baboudjian M, Granger B, Roupret M, Delingette H, Renard-Penna R. A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging. Eur Urol Oncol. 2025 Aug;8(4):1182-1202. doi: 10.1016/j.euo.2024.11.001. Epub 2024 Nov 14. PubMed 39547898 ↗
  • Epstein JI, Amin MB, Reuter VE, Humphrey PA. Contemporary Gleason Grading of Prostatic Carcinoma: An Update With Discussion on Practical Issues to Implement the 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am J Surg Pathol. 2017 Apr;41(4):e1-e7. doi: 10.1097/PAS.0000000000000820. PubMed 28177964 ↗
  • Zeng H, Chen W, Zheng R, Zhang S, Ji JS, Zou X, Xia C, Sun K, Yang Z, Li H, Wang N, Han R, Liu S, Li H, Mu H, He Y, Xu Y, Fu Z, Zhou Y, Jiang J, Yang Y, Chen J, Wei K, Fan D, Wang J, Fu F, Zhao D, Song G, Chen J, Jiang C, Zhou X, Gu X, Jin F, Li Q, Li Y, Wu T, Yan C, Dong J, Hua Z, Baade P, Bray F, Jemal A, Yu XQ, He J. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries. Lancet Glob Health. 2018 May;6(5):e555-e567. doi: 10.1016/S2214-109X(18)30127-X. PubMed 29653628 ↗
  • Schafer EJ, Laversanne M, Sung H, Soerjomataram I, Briganti A, Dahut W, Bray F, Jemal A. Recent Patterns and Trends in Global Prostate Cancer Incidence and Mortality: An Update. Eur Urol. 2025 Mar;87(3):302-313. doi: 10.1016/j.eururo.2024.11.013. Epub 2024 Dec 11. PubMed 39668103 ↗
  • Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263. doi: 10.3322/caac.21834. Epub 2024 Apr 4. PubMed 38572751 ↗

Study documents

PDFs hosted by the registry, ClinicalTrials.gov.

Individual participant data

Plan to share: No — This study does not have a plan to share individual participant data due to institutional and ethical restrictions.

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reachedSimilar studies: half by the dot; the band is the middle halfHalf of similar studies reached it by the dot; the band is the middle half, from a quarter to three quarters

Started May 2025 (actual). Primary completion is estimated for Jun 2030, 61 months after the start.

Of 542 non-industry observational studies under Prostatic Neoplasms, half reached primary completion within 67 months of starting (the median), counting those still running; a quarter within 30 and three quarters within 162. This study's planned 61 months is close to that.

Counting every sponsor, half of 651 studies reached it within 60 months; a quarter within 29 and three quarters within 141.

How these figures are worked out, and which studies are left out of them.

Registered
May 13, 2026
First posted
May 29, 2026
StartActual
May 30, 2025
Primary completionEst.
Jun 30, 2030
CompletionEst.
Dec 31, 2030
Last update
May 29, 2026
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on May 29, 2026, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT07614256
Lead sponsor
Guangxi Medical University
Responsible party
Fubo Wang (Professor, Doctoral Tutor of Medical Science, Guangxi Medical University) — Principal investigator
First posted
May 29, 2026
Start date
May 30, 2025
Primary completion
Jun 30, 2030 (estimated)
Completion
Dec 31, 2030 (estimated)
Last update
May 29, 2026

Study contacts

Caigou Shi, MD
Contact
shicaigou@sr.gxmu.edu.cn
+86 13677729003
Fubo Wang, MD
principal investigator · Guangxi Medical University

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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