ClinicalTrials.gg
Not yet recruitingNCT07760857Updated Oct 22, 2026

Investigation of an Intelligent Centre-adaptive Multi-modal Fusion Framework (Cad- MMFF) to Overcome Unnecessary Prostate Biopsies and Optimize MRI Utilization

An observational study in Prostate Cancer (Diagnosis), sponsored by Chinese University of Hong Kong. Not yet recruiting at 2 sites in Hong Kong. Open to male participants aged 45 Years and older. Per ClinicalTrials.gov, last updated 2026-10-22.

Sponsored by Chinese University of Hong Kong · Observational

Updated Oct 22, 2026Start date movedPrimary completion movedGo to Updates ↓
Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
400
Ages
45 Years and older
Sex
Male
01

Study summary

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.

Read the 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.

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Conditions studied

  • Prostate Cancer (Diagnosis)

Keywords

  • Modelling
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In context

Prostatic Neoplasms

7,046 studies on the registry are indexed under Prostatic Neoplasms; 1,580 are open to participants now.

This study's planned enrollment of 400 is above the median of 200 across 1,310 observational studies indexed under Prostatic Neoplasms.

Browse Prostatic Neoplasms studies →

Lead sponsor

Chinese University of Hong Kong is the lead sponsor of 1,454 studies on the registry; 474 are open to participants now.

Of its 6 completed or terminated interventional studies of FDA-regulated products, 0 (0%) have results posted.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
45 Years and older
Sexes eligible
Male
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

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

  • Ethnically Chinese men aged ≥ 50
  • Suspicion of PCa [elevated PSA (>4-ng/ml) or abnormal DRE/PHI] and indicated for prostate
  • Obtained clinical consent (for prospectively enrolled patients only)

Exclusion criteria

Exclusion Criteria:

  • Prior /treatments for PCa
  • History of genitourinary cancer
  • Poor image quality
  • Incomplete imaging scans/clinical data/pathological results
Terms in these criteria, in plain language(1)

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.

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Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
400 participants (estimated)
Patient registry
No
06

What researchers measure

Primary outcomes

  1. 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

  2. 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

  3. 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

  4. Net-Benefit of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)

    By Decision Curve analysis

    Time frame: Through study completion, an average of 1 year

  5. 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

  6. 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

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Study locations

2 sites
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References and documents

Publications

  • 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
  • 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. PubMed 27897172 ↗
  • 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. PubMed 24582925 ↗
  • 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. PubMed 37751333 ↗
  • 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. PubMed 40676129 ↗
  • 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 ↗
  • 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. PubMed 37333662 ↗
  • 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. PubMed 33288961 ↗
  • 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. PubMed 40519221 ↗
  • 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. PubMed 32606435 ↗
  • 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. PubMed 41587605 ↗
  • 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. PubMed 38772787 ↗
  • 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. PubMed 29552975 ↗
  • 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. PubMed 28110982 ↗
  • 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. PubMed 41090646 ↗
  • 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 ↗
  • 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. PubMed 25652619 ↗
  • 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. PubMed 26620456 ↗
  • 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. PubMed 41199721 ↗
  • 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. PubMed 27862869 ↗
  • 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. PubMed 12478119 ↗
  • 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. PubMed 31493960 ↗

Individual participant data

Plan to share: No

09

Timeline

1 registry update since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reachedDate before it movedWhere the date was before it movedSimilar 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

Start is estimated for Jan 2027; primary completion for Dec 2029, 35 months later.

Of 541 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 29 and three quarters within 162. This study's planned 35 months is below that.

Counting every sponsor, half of 650 studies reached it within 60 months; a quarter within 29 and three quarters within 141.

Since we started tracking on Sep 25, 2026, the dates moved once, on Oct 22, 2026: start Sep 2026 → Jan 2027 and primary completion Sep 2026 → Dec 2029.

Earlier changes are on the record history on ClinicalTrials.gov ↗

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

Registered
Aug 2, 2026
First posted
Aug 12, 2026
StartEst.
Jan 1, 2027was Sep 2026
Primary completionEst.
Dec 31, 2029was Sep 2026
CompletionEst.
Mar 31, 2030
Last update
Oct 22, 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.
Start date
Sep 1, 2026→Jan 1, 2027
Oct 22, 2026
Primary completion
Sep 1, 2026→Dec 31, 2029
Oct 22, 2026
Show all 1 update
  1. Oct 22, 2026
    Start date Sep 1, 2026→Jan 1, 2027
    Primary completion Sep 1, 2026→Dec 31, 2029
    + 1 other change: verification date

From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗

10

Registry details

Key details

Study ID
NCT07760857
Responsible party
NG Chi Fai (Professor, Chinese University of Hong Kong) — Principal investigator
First posted
Aug 12, 2026
Start date
Jan 1, 2027 (estimated)
Primary completion
Dec 31, 2029 (estimated)
Completion
Mar 31, 2030 (estimated)
Last update
Oct 22, 2026

Study contacts

Chi Fai NG, MBChB, FRCS (Surg), MD
Contact
ngcf@surgery.cuhk.edu.hk
852-3505-2625
Chi Fai NG, MBChB, FRCS(Ed), MD
principal investigator · Chinese University of Hong Kong
Edmond Sai Kit LAM, PhD
principal investigator · The Hong Kong Polytechnic University

Oversight

FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

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