ClinicalTrials.gg
Active, not recruitingNCT07936175Updated Nov 13, 2026

Pathological Micro-Niches and Multimodal Imaging Macro-Niches for Predicting Clinically Significant Prostate Cancer and Metastasis: A Multicenter Study

An observational study in Prostate Cancer and Prostatic Neoplasms, sponsored by Guangxi Medical University. Active, not recruiting at 1 site in China. Open to male participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-11-13.

Sponsored by Guangxi Medical University · Observational

Updated Nov 13, 2026Newly registeredGo to Updates ↓

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Study type
Observational
Model
Cohort
Time perspective
Other
Enrollment
2,000
Ages
18 Years and older
Sex
Male
01

Where this study stands

Counted across the records on this site · 2026-11-13

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
12th of 106
2,000 · median 300
Started
2014
with 7 others
Observational
106 of 541
54 open now
Primary completion
2029-10
later than 317 of 341 live
Registered
539th of 541
by date

Among the 541 studies in Prostatic Neoplasms with a site in China, this one is the 12th largest by planned enrollment of the 106 observational ones that give a count, is one of 8 that started in 2014, is one of 106 observational ones, reaches primary completion later than 317 of the 341 other live ones, and was the 539th registered.

Prostatic Neoplasms

7,046 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: 18 Years and older · male · no healthy volunteers.

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02

Study summary

This is an investigator-initiated, multicenter, observational ambispective cohort study, predominantly retrospective, with an optional prospective non-interventional silent validation. The study tests a cross-scale hypothesis that the pathological micro-niche of the primary prostate tumor (tumor epithelium, invasive front, immune-rich or immune-poor regions, ECM/collagen remodeling, neurovascular neighborhoods, and peritumoral field effect) is associated at the patient level with the imaging macro-niche captured by multiparametric MRI (diffusion, perfusion, heterogeneity, local invasion) and PSMA PET/CT (receptor expression, whole-body lesion distribution, tumor burden). A pathology teacher model provides patient- or lesion-set-level niche representations only during model development; at deployment and validation, only MRI, PSMA PET/CT, and clinical information available before the prediction time point are used, with no voxel-level pathology-to-imaging registration. Under one master protocol with three pre-specified substudies, the system predicts: (A) patient- and lesion-level clinically significant prostate cancer (csPCa, ISUP Grade Group 2 or higher) from pre-biopsy MRI; (B) pathological pelvic lymph node metastasis (pN1) after radical prostatectomy with pelvic lymph node dissection, primarily from MRI; and (C) concurrent distant metastasis (M1) and subsequent distant metastasis in baseline M0 patients, primarily from PSMA PET/CT. Models are evaluated for discrimination, calibration, clinical net benefit, uncertainty, abstention, and stability across centers, scanners, tracers, and disease spectra, using locked multicenter external validation and prospective silent validation. Model outputs are never shown to clinicians or patients and do not alter care.

Read the detailed description

BACKGROUND: Prostate cancer is one of the most common malignancies in men; GLOBOCAN 2022 estimates it accounts for about 7.3% of new cancers globally, with a rising burden in Asia. Clinical management spans three linked but differently referenced decisions: identifying clinically significant prostate cancer (csPCa) at initial workup; estimating pelvic lymph node risk before curative treatment; and detecting concurrent distant metastasis and predicting subsequent metastasis in high-risk patients. Multiparametric MRI is a key pre-biopsy triage tool (PROMIS, PRECISION), but its performance depends on scan quality, parameters, reader experience, lesion visibility, and the reference standard; a negative MRI cannot fully exclude csPCa. Imaging sensitivity for microscopic nodal disease is limited, and extended pelvic lymph node dissection (ePLND) adds burden; even PSMA PET/CT has a patient-level sensitivity of roughly 40% for pathological pelvic nodes in a prospective phase 3 study, so a negative result cannot replace adequate pathological staging. PSMA PET/CT is more accurate than conventional imaging for initial high-risk staging (proPSMA), and PROMISE V2 provides a standardized reporting framework, but PSMA uptake is not tumor-specific and micro-lesions, low expression, tracer differences, and treatment can cause misclassification. Existing AI models often learn imaging phenotype alone without systematically using pathology microenvironment information; single-center random splits cause center and leakage bias; and diagnostic AI frequently emphasizes discrimination over calibration, net benefit, abstention, drift, and workflow integration.

HYPOTHESIS: The pathological micro-niche of the primary tumor is associated at the patient level with MRI and PSMA PET/CT macro-niches. Introducing pathology niche representations through patient-level knowledge transfer that does not require voxel-level registration can improve csPCa, pelvic node, and distant metastasis risk estimates, with gains reproducible in centers not involved in development and in a prospective silent cohort (discrimination, calibration, and net benefit), not merely higher internal AUROC.

DESIGN: Investigator-initiated, multicenter, observational ambispective cohort under one master protocol with three pre-specified substudies (A, B, C; C split into C1 and C2) sharing a patient master index, data dictionary, pathology niche dictionary, imaging quality control, and security framework, but with separate target populations, prediction time points, reference standards, primary endpoints, and sample sizes. Phase I is a retrospective continuous-case cohort for data audit, model development, internal validation, and center/time-window locked external validation. Phase II is an optional prospective non-interventional silent validation after model weights, input specifications, thresholds, and the statistical analysis plan are frozen; model results are not returned to the clinic and care is unchanged. A future clinical-impact trial or RCT is outside this protocol and requires separate ethics approval, consent, and prospective registration before first enrollment. The study never assigns exposure or treatment based on model predictions. A global patient-level split table is built first; patients assigned to external or prospective validation, and all their slides, images, follow-up-derived features, and pathology teacher representations, are excluded from every development fold. Multicenter development uses center- or center-time-window leave-out internal-external cross-validation, retaining at least one fully locked-out center or time cohort.

SUBSTUDIES: A (csPCa) enrolls patients who completed MRI before biopsy or surgery, using MRI plus pre-prediction clinical variables; reference standard tier 1 is whole-gland radical prostatectomy pathology without intervening therapy, tier 2 is qualified MRI-targeted plus systematic biopsy; negative biopsies with inadequate follow-up are not treated as true negatives, and lesion-level analysis is used only when MRI and pathology lesions can be reliably matched. B (pN1) enrolls patients before radical prostatectomy plus pelvic lymph node dissection, using MRI as primary input and PSMA PET/CT as an optional increment; the reference is pathological pN1, with high-confidence pN0 requiring a qualified dissection template, submitted regions, adequate node yield, and complete reporting; low-confidence pN0 are not equi-weighted with pN1. C1 (concurrent M1) uses an index staging image within a +/-90-day pre-treatment window, with PSMA PET/CT as primary input and a blinded multidisciplinary composite reference (pathology, sequential imaging, treatment response, follow-up); the same un-reviewed PSMA report is never the sole gold standard, and initial diagnosis versus recurrent re-staging are stratified. C2 (subsequent metastasis) follows baseline M0 patients from a pre-specified landmark to the first image- or pathology-confirmed distant metastasis, reporting metastasis-free survival, with baseline post-treatment handled as time-dependent and death as a competing event.

DATA AND MEASUREMENTS: Retrospective cases span January 2014 to September 2026 across 28 collaborating centers; prospective silent validation spans October 2026 to October 2029. Data come from electronic medical records, laboratory systems, PACS, pathology information systems, surgical/anesthesia records, cancer registries, death information, and approved follow-up. Cases are selected consecutively, never by outcome. Pathology slides are indexed by patient, specimen, slide, region, and patch; the pathology teacher uses only out-of-fold representations for patients unseen in its training fold. Images retain original DICOM for local QC and are de-identified before entering the research environment; MRI records sequence completeness, slice thickness, b-value, field strength, and vendor, and PSMA PET/CT is structured per PROMISE V2. Reference standards are adjudicated by personnel blinded to model output; difficult concurrent M1 cases are reviewed by an endpoint committee of at least two specialists, with consensus or third-party adjudication.

STATISTICAL ANALYSIS: R 4.4+ and Python 3.11+ are used with frozen dependencies, seeds, and code versions; full analysis, reference-complete, QC-passed, and prospective silent sets are defined separately. Continuous variables are summarized by mean/SD or median/IQR and categorical variables by counts/percentages; missing data use multiple imputation within training folds or in-model handling, with no outcome-informed imputation of predictors in external validation. Development uses pre-trained encoders, regularization, nested cross-validation, bootstrap, and center/time internal-external cross-validation, with out-of-fold pathology teacher outputs; pre-specified clinical-only, imaging-only, pathology-only, direct-supervision, patient-level distillation, and fusion models are compared with niche, modality, and clinical ablations. After freezing, models are validated once per center without re-tuning; discrimination, calibration intercept/slope, fixed-threshold performance, decision curves, abstention, and 95% CIs are reported, with clustered bootstrap for multi-lesion/multi-slide correlation and center-specific and random-effect summaries. Subsequent metastasis uses Cox, flexible parametric, or pre-specified machine-learning survival models, checking proportional hazards, with cumulative incidence and Fine-Gray or cause-specific hazards for competing death and time-dependent AUC, C-index, and dynamic Brier scores. Multiplicity across substudies uses a pre-specified hierarchy or false-discovery-rate control. Sensitivity analyses exclude treated cases, use only high-quality reference standards, stratify by PLND extent/node count, exclude inadequately followed negatives, perform leave-one-center-out validation, restrict to protocol-complete MRI/PET, and vary handling of indeterminate cases.

QUALITY CONTROL AND ETHICS: SOPs cover screening, data dictionary, DICOM/WSI de-identification, QC, reference adjudication, model freezing, analysis, transfer, privacy incidents, and incidental findings, with versioned changes. The retrospective component applies for a waiver of informed consent (but not of ethics review) because it uses existing clinical data and residual specimens without contact or change in care, with de-identification, data minimization, and access control; the prospective silent validation obtains written informed consent. Data are de-identified locally, with encrypted transfer, access approval, logs, and backup; identity mapping tables remain at the source centers. AI outputs are research variables only and are not returned to patients or clinicians; the study is not a patient registry, retains no research biospecimens, and does not share individual participant data.

03

Conditions studied

  • Prostate Cancer
  • Prostatic Neoplasms

Browse trials for

Keywords

  • pathological niche
  • imaging niche
  • multimodal imaging
  • multiparametric MRI
  • PSMA PET/CT
  • clinically significant prostate cancer
  • pelvic lymph node metastasis
  • distant metastasis
  • deep learning
  • cross-center validation
04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
Male
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

Men aged 18 years or older evaluated, diagnosed, or followed for suspected or confirmed prostate cancer at 28 collaborating centers across China. The retrospective continuous-case cohort spans January 2014 to September 2026; the optional prospective non-interventional silent validation spans October 2026 to October 2029. A patient may enter one or more nested substudy cohorts, but all data splits are made at the patient level. The entered enrollment is an anticipated pre-screening estimate to be confirmed by center feasibility screening.

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. Age 18 years or older.
  2. Evaluated, diagnosed, or followed for suspected or confirmed prostate cancer at a participating center.
  3. A unique patient-level study ID can be established and the temporal order of key examinations, pathology sampling, and treatment can be confirmed.
  4. At least one qualified imaging, pathology, clinical, or follow-up dataset required by a substudy is available.
  5. Data use conforms to ethical approval, , or the approved retrospective consent-waiver scope.

Exclusion criteria

Exclusion Criteria:

  1. Reliable de-duplication, or the before/after order of the prediction time point and endpoint, cannot be confirmed.
  2. Core imaging, pathology, or outcome data are damaged and cannot pass quality control.
  3. Required data use exceeds the original authorization and ethical approval has not been obtained.
  4. Use is prohibited by laws or regulations, collaboration agreements, or an ethical decision.
  5. For a specific substudy, the primary reference standard is missing or clear label leakage cannot be corrected.
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.

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.

05

Study design

Observational model
Cohort
Time perspective
Other
Enrollment
2,000 participants (estimated)
Patient registry
No

Groups and cohorts

  • Substudy A: csPCa Prediction Cohort

    Patients who completed MRI before biopsy or surgery; patient- and lesion-level clinically significant prostate cancer (Grade Group 2 or higher) is predicted from MRI and pre-prediction clinical variables, with tiered pathology reference standards.

  • Substudy B: pN1 Prediction Cohort

    Patients undergoing radical prostatectomy combined with pelvic lymph node dissection; pathological pN1 is predicted preoperatively, primarily from MRI with PSMA PET/CT as an optional incremental input.

  • Substudy C1: Concurrent M1 Prediction Cohort

    Patients within the +/-90-day pre-treatment baseline staging window; concurrent distant metastasis (M1) is predicted primarily from PSMA PET/CT and confirmed by a blinded multidisciplinary composite reference.

  • Substudy C2: Subsequent Metastasis Prediction Cohort

    Patients confirmed M0 at baseline who are followed from a pre-specified landmark for the first distant metastasis, with treatment as time-dependent and death as a competing event.

06

What researchers measure

Primary outcomes

  1. Patient-Level Clinically Significant Prostate Cancer (Grade Group 2 or Higher)

    Patient-level presence of clinically significant prostate cancer, defined as histologic ISUP Grade Group 2 or higher, adjudicated against the tiered reference standard (tier 1: whole-gland radical prostatectomy pathology without interval treatment; tier 2: qualified MRI-targeted plus systematic biopsy). Unit of measure: Percentage (%).

    Time frame: Periprocedurally, at biopsy or surgery

  2. Pathological Pelvic Lymph Node Metastasis (pN1)

    Presence of pathologically confirmed pelvic lymph node metastasis (pN1) after radical prostatectomy with pelvic lymph-node dissection; high-confidence pN0 cases are defined by documented dissection template, submitted regions, and node counts. Unit of measure: Percentage (%).

    Time frame: Perioperatively, at radical prostatectomy with pelvic lymph-node dissection

  3. Synchronous Distant Metastasis (M1) at Baseline Staging

    Synchronous M1 status within the baseline staging window (within 90 days of the index imaging and before systemic therapy), confirmed by a blinded multidisciplinary composite reference standard. Unit of measure: Percentage (%).

    Time frame: Baseline, within 90 days of the index staging imaging

07

Study locations

1 site
08

References and documents

Publications

  • Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P; DECIDE-AI expert group. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ. 2022 May 18;377:e070904. doi: 10.1136/bmj-2022-070904. PubMed 35584845 ↗
  • Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med. 2016 Jan 30;35(2):214-26. doi: 10.1002/sim.6787. Epub 2015 Nov 9. PubMed 26553135 ↗
  • Riley RD, Snell KIE, Archer L, Ensor J, Debray TPA, van Calster B, van Smeden M, Collins GS. Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study. BMJ. 2024 Jan 22;384:e074821. doi: 10.1136/bmj-2023-074821. PubMed 38253388 ↗
  • Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, Moons KGM, Collins G, van Smeden M. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020 Mar 18;368:m441. doi: 10.1136/bmj.m441. No abstract available. PubMed 32188600 ↗
  • Sounderajah V, Guni A, Liu X, Collins GS, Karthikesalingam A, Markar SR, Golub RM, Denniston AK, Shetty S, Moher D, Bossuyt PM, Darzi A, Ashrafian H; STARD-AI Steering Committee. The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence. Nat Med. 2025 Oct;31(10):3283-3289. doi: 10.1038/s41591-025-03953-8. Epub 2025 Sep 15. PubMed 40954311 ↗
  • Moons KGM, Damen JAA, Kaul T, Hooft L, Andaur Navarro C, Dhiman P, Beam AL, Van Calster B, Celi LA, Denaxas S, Denniston AK, Ghassemi M, Heinze G, Kengne AP, Maier-Hein L, Liu X, Logullo P, McCradden MD, Liu N, Oakden-Rayner L, Singh K, Ting DS, Wynants L, Yang B, Reitsma JB, Riley RD, Collins GS, van Smeden M. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025 Mar 24;388:e082505. doi: 10.1136/bmj-2024-082505. PubMed 40127903 ↗
  • Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378. PubMed 38626948 ↗
  • Wu S, Wang Y, Hong G, Luo Y, Lin Z, Shen R, Zeng H, Xu A, Wu P, Xiao M, Li X, Rao P, Yang Q, Feng Z, He Q, Jiang F, Xie Y, Liao C, Huang X, Chen R, Lin T. An artificial intelligence model for detecting pathological lymph node metastasis in prostate cancer using whole slide images: a retrospective, multicentre, diagnostic study. EClinicalMedicine. 2024 Apr 5;71:102580. doi: 10.1016/j.eclinm.2024.102580. eCollection 2024 May. PubMed 38618206 ↗
  • Cao J, Feng X, Liu R, Luo T, Yang L, Li H, Wang F, Lin P, Xiang Y, Yang J, Fu Y, Li F. A Transformer-Based Model Integrating Intratumoral Habitats and Peritumoral Radiomics for Detecting Pelvic Lymph Node Metastasis in Prostate Cancer. Acad Radiol. 2026 Mar;33(3):963-975. doi: 10.1016/j.acra.2025.10.059. Epub 2025 Nov 20. PubMed 41266221 ↗
  • Wessels F, Schmitt M, Krieghoff-Henning E, Jutzi T, Worst TS, Waldbillig F, Neuberger M, Maron RC, Steeg M, Gaiser T, Hekler A, Utikal JS, von Kalle C, Frohling S, Michel MS, Nuhn P, Brinker TJ. Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer. BJU Int. 2021 Sep;128(3):352-360. doi: 10.1111/bju.15386. Epub 2021 May 5. PubMed 33706408 ↗
  • 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 ↗
  • Wu H, Liu F, Yang Q, Chen H, Wang Y, Yang X, Xia P, Fang L, Li G, Yang J, Luo Y, Li J, Fang X, Yang X, Jiang H, Liu J, Yi Y, Chai X, Lu J, Wang J, Gao X, Chen C, Shao C, Bian Y. Automated MRI system for clinically significant prostate cancer detection development validation and real-world implementation. Nat Commun. 2025 Nov 23;16(1):11583. doi: 10.1038/s41467-025-66593-z. PubMed 41276524 ↗
  • Saha A, Bosma JS, Twilt JJ, van Ginneken B, Bjartell A, Padhani AR, Bonekamp D, Villeirs G, Salomon G, Giannarini G, Kalpathy-Cramer J, Barentsz J, Maier-Hein KH, Rusu M, Rouviere O, van den Bergh R, Panebianco V, Kasivisvanathan V, Obuchowski NA, Yakar D, Elschot M, Veltman J, Futterer JJ, de Rooij M, Huisman H; PI-CAI consortium. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol. 2024 Jul;25(7):879-887. doi: 10.1016/S1470-2045(24)00220-1. Epub 2024 Jun 11. PubMed 38876123 ↗
  • Fendler WP, Eiber M, Beheshti M, Bomanji J, Calais J, Ceci F, Cho SY, Fanti S, Giesel FL, Goffin K, Haberkorn U, Jacene H, Koo PJ, Kopka K, Krause BJ, Lindenberg L, Marcus C, Mottaghy FM, Oprea-Lager DE, Osborne JR, Piert M, Rowe SP, Schoder H, Wan S, Wester HJ, Hope TA, Herrmann K. PSMA PET/CT: joint EANM procedure guideline/SNMMI procedure standard for prostate cancer imaging 2.0. Eur J Nucl Med Mol Imaging. 2023 Apr;50(5):1466-1486. doi: 10.1007/s00259-022-06089-w. Epub 2023 Jan 5. PubMed 36604326 ↗
  • Seifert R, Emmett L, Rowe SP, Herrmann K, Hadaschik B, Calais J, Giesel FL, Reiter R, Maurer T, Heck M, Gafita A, Morris MJ, Fanti S, Weber WA, Hope TA, Hofman MS, Fendler WP, Eiber M. Second Version of the Prostate Cancer Molecular Imaging Standardized Evaluation Framework Including Response Evaluation for Clinical Trials (PROMISE V2). Eur Urol. 2023 May;83(5):405-412. doi: 10.1016/j.eururo.2023.02.002. Epub 2023 Mar 17. PubMed 36935345 ↗
  • Hofman MS, Lawrentschuk N, Francis RJ, Tang C, Vela I, Thomas P, Rutherford N, Martin JM, Frydenberg M, Shakher R, Wong LM, Taubman K, Ting Lee S, Hsiao E, Roach P, Nottage M, Kirkwood I, Hayne D, Link E, Marusic P, Matera A, Herschtal A, Iravani A, Hicks RJ, Williams S, Murphy DG; proPSMA Study Group Collaborators. Prostate-specific membrane antigen PET-CT in patients with high-risk prostate cancer before curative-intent surgery or radiotherapy (proPSMA): a prospective, randomised, multicentre study. Lancet. 2020 Apr 11;395(10231):1208-1216. doi: 10.1016/S0140-6736(20)30314-7. Epub 2020 Mar 22. PubMed 32209449 ↗
  • Hope TA, Eiber M, Armstrong WR, Juarez R, Murthy V, Lawhn-Heath C, Behr SC, Zhang L, Barbato F, Ceci F, Farolfi A, Schwarzenbock SM, Unterrainer M, Zacho HD, Nguyen HG, Cooperberg MR, Carroll PR, Reiter RE, Holden S, Herrmann K, Zhu S, Fendler WP, Czernin J, Calais J. Diagnostic Accuracy of 68Ga-PSMA-11 PET for Pelvic Nodal Metastasis Detection Prior to Radical Prostatectomy and Pelvic Lymph Node Dissection: A Multicenter Prospective Phase 3 Imaging Trial. JAMA Oncol. 2021 Nov 1;7(11):1635-1642. doi: 10.1001/jamaoncol.2021.3771. PubMed 34529005 ↗
  • 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 ↗
  • 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 ↗

Individual participant data

Plan to share: No

09

Timeline

1 registry update 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 Jan 2014 (actual). Primary completion is estimated for Oct 2029, 189 months after the start.

Of 540 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 189 months is above that.

Counting every sponsor, half of 649 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
Oct 29, 2026
First posted
Nov 13, 2026
StartActual
Jan 2014
Primary completionEst.
Oct 2029
CompletionEst.
Oct 2029
Last update
Nov 13, 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.
Registered
First appeared on the registry. No changes since
Nov 13, 2026
Show all 1 update
  1. Nov 13, 2026
    First appeared on the registry

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
NCT07936175
Lead sponsor
Guangxi Medical University
Responsible party
Fubo Wang (Doctor of Medicine, Guangxi Medical University) — Principal investigator
First posted
Nov 13, 2026
Start date
Jan 2014
Primary completion
Oct 2029 (estimated)
Completion
Oct 2029 (estimated)
Last update
Nov 13, 2026

Study contacts

Fubo Wang, MD
principal investigator · Guangxi Medical University

Oversight

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

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Questions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.

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Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.

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