Clinical trial · Observational
Intelligent Screening and Precision Diagnosis of Prostate Cancer Based on Multimodal Data
Prospective Validation of an AI-Assisted Multimodal Imaging-Pathology Fusion System for Precision Diagnosis and Biopsy Guidance in Patients With Suspected Prostate Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This project aims to develop a precision screening and diagnostic solution for prostate cancer based on multimodal artificial intelligence, focusing on addressing the diagnostic challenge in patients within the PSA "gray zone" of 4-10 ng/mL. The project will integrate multidimensional information including ctDNA liquid biopsy, routine laboratory data, and prostate ultrasound images to develop three models: a ctDNA-based multimodal AI prediction model, a routine laboratory data-assisted decision model, and an ultrasound image AI-assisted diagnostic model. On this basis, a multimodal AI fusion decision system will be established to automatically generate individualized risk assessment reports and diagnostic recommendations. Additionally, a closed-loop mechanism of "clinical use - data feedback - model optimization" will be constructed to continuously iterate model parameters using pathological gold standards, thereby improving predictive accuracy in our hospital population. The project will form a generalizable precision diagnostic workflow, reduce unnecessary biopsies in "gray zone" patients, and provide an implementable in-hospital solution for precision medicine in prostate cancer.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Prostate Cancer With PSA Gray Zone | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (2)
- label
- Training Set
- description
- Approximately 400 cases. This group will be used to develop and internally validate the three specialized models: (1) ctDNA multimodal AI prediction model, (2) routine laboratory data-assisted decision model, and (3) prostate ultrasound image AI-assisted diagnostic model. Five-fold cross-validation will be used for algorithm comparison and hyperparameter tuning.
- label
- Validation Set
- description
- Approximately 100 cases. This independent validation set will be used to evaluate the diagnostic performance of the multimodal fusion decision system. Sensitivity, specificity, positive predictive value, negative predictive value, and AUC will be calculated using pathological results as the gold standard. DeLong test will be used to compare AUC with PSA alone. Decision curve analysis (DCA) will be used to evaluate clinical net benefit. Subgroup analysis will be performed for the PSA 4-10 ng/mL gray zone.
Primary outcomes (1)
- measure
- Area Under the Curve (AUC) of the multimodal AI fusion diagnostic system
- timeFrame
- Measured after all participants have completed biopsy and obtained pathological diagnosis (approximately within the 2-year study period).
- description
Eligibility
Eligibility (as posted)
- Sex
- Male
- Minimum age
- 45 Years
Show eligibility criteria text
Inclusion Criteria: 1. Age ≥45 years, male 2. Presenting with abnormal serum PSA (≥4 ng/mL), abnormal digital rectal examination, or suspicious lesions on prostate ultrasound 3. Undergoing prostate biopsy with definitive pathological results 4. Signed informed consent Exclusion Criteria: 1. Previously diagnosed with prostate cancer and receiving surgery, radiotherapy, or endocrine therapy 2. With other malignancies 3. Critical missing clinical data (e.g., missing PSA value, incomplete ultrasound report)
References
Publications (16)
- BACKGROUNDWang X, Xie Y, Zheng X, Liu B, Chen H, Li J, Ma X, Xiang J, Weng G, Zhu W, Wang G, Fang Y, Cheng H, Xie L. A prospective multi-center randomized comparative trial evaluating outcomes of transrectal ultrasound (TRUS)-guided 12-core systematic biopsy, mpMRI-targeted 12-core biopsy, and artificial intelligence ultrasound of prostate (AIUSP) 6-core targeted biopsy for prostate cancer diagnosis. World J Urol. 2023 Mar;41(3):653-662. doi: 10.1007/s00345-022-04086-0. Epub 2022 Jul 19. PMID 35852595
- BACKGROUNDAhmed 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. PMID 28110982
- BACKGROUNDGai W, Sun K. Epigenetic Biomarkers in Cell-Free DNA and Applications in Liquid Biopsy. Genes (Basel). 2019 Jan 9;10(1):32. doi: 10.3390/genes10010032. PMID 30634483
- BACKGROUNDHe WS, Bishop KS. The potential use of cell-free-circulating-tumor DNA as a biomarker for prostate cancer. Expert Rev Mol Diagn. 2016 Aug;16(8):839-52. doi: 10.1080/14737159.2016.1197121. Epub 2016 Jun 20. PMID 27254598
- BACKGROUNDJones PA, Laird PW. Cancer epigenetics comes of age. Nat Genet. 1999 Feb;21(2):163-7. doi: 10.1038/5947. PMID 9988266
- BACKGROUNDHamdy FC, Donovan JL, Lane JA, Mason M, Metcalfe C, Holding P, Davis M, Peters TJ, Turner EL, Martin RM, Oxley J, Robinson M, Staffurth J, Walsh E, Bollina P, Catto J, Doble A, Doherty A, Gillatt D, Kockelbergh R, Kynaston H, Paul A, Powell P, Prescott S, Rosario DJ, Rowe E, Neal DE; ProtecT Study Group. 10-Year Outcomes after Monitoring, Surgery, or Radiotherapy for Localized Prostate Cancer. N Engl J Med. 2016 Oct 13;375(15):1415-1424. doi: 10.1056/NEJMoa1606220. Epub 2016 Sep 14.