Multimodal Imaging and Digital Pathology for Prostate Cancer Prediction

NCT07614256 | Recruiting DMC

• 1 other identifier: GXMU-PCa-AI-KY20260016
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

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

Outcome Measures

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.

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

Interventions

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.

Eligibility Criteria

• Age: 40 Years - 90 Years
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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).

You may not qualify if:

• 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).

Sponsors & Collaborators

• Guangxi Medical University: lead

Study Sites (1)

Liuzhou People's Hospital Affiliated to Guangxi Medical University

Liuzhou, Guangxi, 545006, China

RECRUITING

Related Publications (13)

• 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.

  PMID: 40897909 RESULT

• 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.

  PMID: 39880746 RESULT

• 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.

  PMID: 34245943 RESULT

• 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.

  PMID: 40067105 RESULT

• 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.

  PMID: 40512493 RESULT

• 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.

  PMID: 31492650 RESULT

• 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.

  PMID: 38217298 RESULT

• 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.

  PMID: 31022301 RESULT

• 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.

  PMID: 39547898 RESULT

• 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.

  PMID: 28177964 RESULT

• 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.

  PMID: 29653628 RESULT

• 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.

  PMID: 39668103 RESULT

• 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.

  PMID: 38572751 RESULT

MeSH Terms

Conditions

Prostatic Neoplasms; Disease

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Fubo Wang, MD

  Guangxi Medical University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Caigou Shi, MD

CONTACT

+86 13677729003; shicaigou@sr.gxmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 12 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor, Doctoral Tutor of Medical Science

Study Record Dates

• First Submitted: May 13, 2026
• First Posted: May 29, 2026
• Study Start: May 30, 2025
• Primary Completion (Estimated): June 30, 2030
• Study Completion (Estimated): December 31, 2030
• Last Updated: May 29, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)