- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT07760857
Investigation of an Intelligent Centre-adaptive Multi-modal Fusion Framework (Cad- MMFF) to Overcome Unnecessary Prostate Biopsies and Optimize MRI Utilization
August 7, 2026 updated by: NG Chi Fai, Chinese University of Hong Kong
Investigation of an Intelligent Centre-adaptive Multi-modal Fusion Framework (Cad- MMFF) to Overcome Unnecessary Prostate Biopsies and Optimize MRI Utilization: a Hybrid Retrospective-prospective Study
This study aims to investigate a novel Centre-Adaptive Multi-Modal Fusion Framework (Cad-MMFF) that integrates clinical, ultrasound, and MRI data to improve the detection of clinically significant prostate cancer (csPCa), reduce unnecessary biopsies, and optimize MRI utilization.
Study Overview
Status
Not yet recruiting
Conditions
Detailed Description
Approximately 400 Chinese men aged 50 years or above with suspected prostate cancer will be included.The study consists of retrospective model development and prospective model optimization and validation.
Two AI models will be developed: a Clinical-US model using clinical and ultrasound data, and a Clinical-US-MRI model incorporating MRI information.
The optimized models will subsequently be validated in an independent cohort.
Study Type
Observational
Enrollment (Estimated)
400
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Contact
- Name: Chi Fai NG, MBChB, FRCS (Surg), MD
- Phone Number: 852-3505-2625
- Email: ngcf@surgery.cuhk.edu.hk
Study Locations
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Hong Kong, Hong Kong
- Prince of Wales Hospital
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Sheung Shui, Hong Kong
- North District Hospital
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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
No
Sampling Method
Non-Probability Sample
Study Population
The entire cohort of this study will be targeted at patients who are suspected of PCa and indicated for ultrasound-guided and/or MRI-guided targeted prostate biopsy.
Brightness-modulated ultrasound data (longitudinal and transverse views with full prostate coverage), pathological outcomes (Gleason score, ISUP GG), pre-biopsy multi-parametric MRI scans, PI-RADS scores, patients demographics and clinical data, will be acquired from January 2023 to August 2026 retrospectively, and from September 2026 to March 2028 prospectively, from the existing database of the Picture Archiving and Communication System (PACS).
Informed consent from subjects will be sought only for prospectively enrolled patients, while there will be no interventions involved given its nature of retrospective analysis
Description
Inclusion Criteria:
- Ethnically Chinese men aged ≥ 50
- Suspicion of PCa [elevated PSA (>4-ng/ml) or abnormal DRE/PHI] and indicated for prostate biopsy
- Obtained clinical consent (for prospectively enrolled patients only)
Exclusion Criteria:
- Prior biopsy/treatments for PCa
- History of genitourinary cancer
- Poor image quality
- Incomplete imaging scans/clinical data/pathological results
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
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Area under Receiver-Operating-Curve of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time Frame: Through study completion, an average of 1 year
|
Through study completion, an average of 1 year
|
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The number of unnecessary biopsy rate decreased of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time Frame: Through study completion, an average of 1 year
|
Through study completion, an average of 1 year
|
|
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Proportion of MRI safely saved of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time Frame: Through study completion, an average of 1 year
|
Through study completion, an average of 1 year
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Net-Benefit of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time Frame: Through study completion, an average of 1 year
|
By Decision Curve analysis
|
Through study completion, an average of 1 year
|
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Missed rate of clinically significant prostate cancer of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time Frame: Through study completion, an average of 1 year
|
Through study completion, an average of 1 year
|
|
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Detection rate of indolent prostate cancer of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time Frame: Through study completion, an average of 1 year
|
Through study completion, an average of 1 year
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Collaborators
Investigators
- Principal Investigator: Chi Fai NG, MBChB, FRCS(Ed), MD, Chinese University of Hong Kong
- Principal Investigator: Edmond Sai Kit LAM, PhD, The Hong Kong Polytechnic 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
- Ahmed 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.
- Kasivisvanathan V, Rannikko AS, Borghi M, Panebianco V, Mynderse LA, Vaarala MH, Briganti A, Budaus L, Hellawell G, Hindley RG, Roobol MJ, Eggener S, Ghei M, Villers A, Bladou F, Villeirs GM, Virdi J, Boxler S, Robert G, Singh PB, Venderink W, Hadaschik BA, Ruffion A, Hu JC, Margolis D, Crouzet S, Klotz L, Taneja SS, Pinto P, Gill I, Allen C, Giganti F, Freeman A, Morris S, Punwani S, Williams NR, Brew-Graves C, Deeks J, Takwoingi Y, Emberton M, Moore CM; PRECISION Study Group Collaborators. MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis. N Engl J Med. 2018 May 10;378(19):1767-1777. doi: 10.1056/NEJMoa1801993. Epub 2018 Mar 18.
- Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. J Biomed Inform. 2014 Apr;48:193-204. doi: 10.1016/j.jbi.2014.02.013. Epub 2014 Feb 26.
- Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates. Eur Urol. 2020 Jan;77(1):38-52. doi: 10.1016/j.eururo.2019.08.005. Epub 2019 Sep 5.
- Chiu PK, Roobol MJ, Nieboer D, Teoh JY, Yuen SK, Hou SM, Yiu MK, Ng CF. Adaptation and external validation of the European randomised study of screening for prostate cancer risk calculator for the Chinese population. Prostate Cancer Prostatic Dis. 2017 Mar;20(1):99-104. doi: 10.1038/pcan.2016.57. Epub 2016 Nov 29.
- 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.
- Presti JC Jr, O'Dowd GJ, Miller MC, Mattu R, Veltri RW. Extended peripheral zone biopsy schemes increase cancer detection rates and minimize variance in prostate specific antigen and age related cancer rates: results of a community multi-practice study. J Urol. 2003 Jan;169(1):125-9. doi: 10.1016/S0022-5347(05)64051-7.
- Josefsson A, Mansson M, Kohestani K, Spyratou V, Wallstrom J, Hellstrom M, Lilja H, Vickers A, Carlsson SV, Godtman R, Hugosson J. Performance of 4Kscore as a Reflex Test to Prostate-specific Antigen in the GOTEBORG-2 Prostate Cancer Screening Trial. Eur Urol. 2024 Sep;86(3):223-229. doi: 10.1016/j.eururo.2024.04.037. Epub 2024 May 20.
- Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021 Feb;18(2):203-211. doi: 10.1038/s41592-020-01008-z. Epub 2020 Dec 7.
- TRANSFORM Trial: Trial of Randomised Approaches for National Screening FOR Men (TRANSFORM) [Internet]. ISRCTN registry; 2025 [cited 2026 Mar 28]. ISRCTN13801649.
- Centre for Health Protection. Prostate cancer [Internet]. Hong Kong: Department of Health; 2026 [cited 2026 Mar 28]. Available from: https://www.chp.gov.hk/en/healthtopics/content/25/5781.html
- Wu D, Lawhern VJ, Gordon S, Lance BJ, Lin C. Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR). IEEE Trans Fuzzy Syst. 2017;25(6):1522-35. doi:10.1109/TFUZZ.2016.2633379
- Yue G, Wei P, Zhou T, Song Y, Zhao C, Wang T, Lei B. Specificity-Aware Federated Learning With Dynamic Feature Fusion Network for Imbalanced Medical Image Classification. IEEE J Biomed Health Inform. 2024 Nov;28(11):6373-6383. doi: 10.1109/JBHI.2023.3319516. Epub 2024 Nov 6.
- Jahanandish H, Sang S, Li CX, Vesal S, Bhattacharya I, Lee JH, et al. Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-Center Study [Preprint]. arXiv:2502.00146v1. 2025. doi:10.48550/arXiv.2502.00146
- Wei P, Zhou T, Liu W, et al. FedPDN: Personalized federated learning with inter-class similarity constraint for medical image classification through parameter decoupling. IEEE Trans Instrum Meas. 2025; 74:1-13, Art no. 4001213, doi: 10.1109/TIM.2025.3527597
- Hao Y, Qiu Z, Holmes J, Lockenhoff CE, Liu W, Ghassemi M, Kalantari S. Large language model integrations in cancer decision-making: a systematic review and meta-analysis. NPJ Digit Med. 2025 Jul 17;8(1):450. doi: 10.1038/s41746-025-01824-7.
- 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.
- Sun YK, Zhou BY, Miao Y, Shi YL, Xu SH, Wu DM, Zhang L, Xu G, Wu TF, Wang LF, Yin HH, Ye X, Lu D, Han H, Xiang LH, Zhu XX, Zhao CK, Xu HX; China Alliance of Multi-Center Clinical Study for Ultrasound (Ultra-Chance). Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study. EClinicalMedicine. 2023 Jun 9;60:102027. doi: 10.1016/j.eclinm.2023.102027. eCollection 2023 Jun.
- Correction: Prospective evaluation of artificial intelligence (AI) applications for use in cancer pathways following diagnosis: a systematic review. BMJ Oncol. 2025 May 15;4(1):e000255corr1. doi: 10.1136/bmjonc-2023-000255corr1. eCollection 2025.
- Teoh JY, Leung CH, Wang MH, Chiu PK, Yee CH, Ng CF, Wong MC. The cost-effectiveness of prostate health index for prostate cancer detection in Chinese men. Prostate Cancer Prostatic Dis. 2020 Dec;23(4):615-621. doi: 10.1038/s41391-020-0243-1. Epub 2020 Jun 30.
- Gomez Rivas J, Gomez Davila P, Tarrazo Antelo AM, Corujo Quinteiro M, Gomez Amorin A, Rodriguez Alonso A, Vilaseca JM, Lopez H, Salazar JP, Borque-Fernando A, Moreno-Sierra J, Collen S, Beyer K, Helleman J, Roobol MJ, van Poppel H; en representacion de PRAISE-U Consortium. The future of prostate cancer screening in the European Union: PRAISE-U project. Actas Urol Esp (Engl Ed). 2026 Apr;50(3):501919. doi: 10.1016/j.acuroe.2026.501919. Epub 2026 Jan 24. English, Spanish.
- Nedelcu A, Oerther B, Benkendorff A, Dieckbreder S, Schwarzer G, Agrotis G, Schoots IG, El Matine R, Eisenblaetter M, Sigle A, Engel H, Bamberg F, Benndorf M. PI-RADS Version 2.1 for Prostate MRI Interpretation: Associations of Study Quality and Cancer Detection Metrics-A Systematic Review and Meta-Analysis. AJR Am J Roentgenol. 2026 Feb;226(2):e2533583. doi: 10.2214/AJR.25.33583. Epub 2026 Feb 11.
- Teoh JY, Yuen SK, Tsu JH, Wong CK, Ho BSh, Ng AT, Ma WK, Ho KL, Yiu MK. Prostate cancer detection upon transrectal ultrasound-guided biopsy in relation to digital rectal examination and prostate-specific antigen level: what to expect in the Chinese population? Asian J Androl. 2015 Sep-Oct;17(5):821-5. doi: 10.4103/1008-682X.144945.
- Teoh JY, Yuen SK, Tsu JH, Wong CK, Ho BS, Ng AT, Ma WK, Ho KL, Yiu MK. The performance characteristics of prostate-specific antigen and prostate-specific antigen density in Chinese men. Asian J Androl. 2017 Jan-Feb;19(1):113-116. doi: 10.4103/1008-682X.167103.
- Chiu PKF, Chu WCW, Cho CMC, Teoh YCJ, Ng CF. Prostate Health Index for risk stratification before magnetic resonance imaging: abridged secondary publication. Hong Kong Med J. 2025 Oct;31 Suppl 7(5):38-40. No abstract available.
- Hansen NL, Kesch C, Barrett T, Koo B, Radtke JP, Bonekamp D, Schlemmer HP, Warren AY, Wieczorek K, Hohenfellner M, Kastner C, Hadaschik B. Multicentre evaluation of targeted and systematic biopsies using magnetic resonance and ultrasound image-fusion guided transperineal prostate biopsy in patients with a previous negative biopsy. BJU Int. 2017 Nov;120(5):631-638. doi: 10.1111/bju.13711. Epub 2016 Dec 21.
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)
September 1, 2026
Primary Completion (Estimated)
September 1, 2026
Study Completion (Estimated)
March 31, 2030
Study Registration Dates
First Submitted
August 2, 2026
First Submitted That Met QC Criteria
August 7, 2026
First Posted (Actual)
August 12, 2026
Study Record Updates
Last Update Posted (Actual)
August 12, 2026
Last Update Submitted That Met QC Criteria
August 7, 2026
Last Verified
August 1, 2026
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- CRE-2026.384
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
NO
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
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