Trial reportBMC medicine2024
Comparison of MRI artificial intelligence-guided cognitive fusion-targeted biopsy versus routine cognitive fusion-targeted prostate biopsy in prostate cancer diagnosis: a randomized controlled trial.
Trial report in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06362291 (Comparison of MRI Artificial Intelligence-guided Cognitive Fusion-targeted Biopsy Versus Routine Cognitive Fusion-targeted Prostate Biopsy in Prostate Cancer Diagnosis), which is not on this map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Comparison of MRI Artificial Intelligence-guided Cognitive Fusion-targeted Biopsy Versus Routine Cognitive Fusion-targeted Prostate Biopsy in Prostate Cancer Diagnosis: a Prospective Randomized Controlled Trial
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of radiomics for detecting and characterising upper tract urothelial carcinoma (UTUC): a systematic review.World journal of urology · 2026Pooled it
- Comprehensive Evaluation of Targeted and Perilesional Biopsy in Biopsy-Naïve Patients With Prostate Positive Magnetic Resonance Imaging: PERI-PRO Noninferiority Randomized Controlled Trial.The Journal of urology · 2026Trial
- Autonomous AI in prostate cancer: the road ahead towards clinical implementation.Abdominal radiology (New York) · 2026Review
- Transperineal cognitive versus trans-rectal fusion biopsy under local anesthesia: multicenter outcomes in detection rates, complications, and feasibility.International urology and nephrology · 2026Article
- Artificial Intelligence for Prognostic Modelling and Adaptive Treatment Monitoring in Radiation Oncology.Cureus · 2026Review
- A retrospective study comparing multimodal fusion biopsy and conventional cognitive fusion biopsy for diagnosing prostate cancer.BMC urology · 2026Article
- Day surgery mode of multi-modal image AI fusion targeted transperineal biopsy technique using electromagnetic navigation tracking system under local anesthesia.BMC urology · 2026Article
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- Applications and challenges of utilizing digital pathology and AI-enabled workflows in clinical trials.Journal of pathology informatics · 2026Review
- Decade-long landscape of transrectal ultrasound (TRUS) in prostate cancer research: trends, collaborations, and emerging frontiers.Journal of ultrasound · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Transperineal Free-Hand Prostate Fusion Biopsy with AI-Driven Auto-Contouring: First Results of a Prospective Study.Cancers · 2025Article
- Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review.EClinicalMedicine · 2025Article
- Advances in imaging and artificial intelligence for precision diagnosis and biopsy guidance in prostate cancer.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCognitive fusion MRI-guided targeted biopsy (cTB) has been widely used in the diagnosis of prostate cancer (PCa). However, cTB relies heavily on the operator's experience and confidence in MRI readings. Our objective was to compare the cancer detection rates of MRI artificial intelligence-guided cTB (AI-cTB) and routine cTB and explore the added value of using AI for the guidance of cTB.
methodsThis was a prospective, single-institution randomized controlled trial (RCT) comparing clinically significant PCa (csPCa) and PCa detection rates between AI-cTB and cTB. A total of 380 eligible patients were randomized to the AI-cTB group (n = 191) or the cTB group (n = 189). The AI-cTB group underwent AI-cTB plus systematic biopsy (SB) and the cTB group underwent routine cTB plus SB. The primary outcome was the detection rate of csPCa. The reference standard was the pathological results of the combination of TB (AI-cTB/cTB) and SB. Comparisons of detection rates of csPCa and PCa between groups were performed using the chi-square test or Fisher's exact test.
resultsThe overall csPCa and PCa detection rates of the whole inclusion cohort were 58.8% and 61.3%, respectively. The csPCa detection rates of TB combined with SB in the AI-cTB group were significantly greater than those in the cTB group at both the patient level (58.64% vs. 46.56%, p = 0.018) and per-lesion level (61.47% vs. 47.79%, p = 0.004). Compared with cTB, the AI-cTB could detect a greater proportion of patients with csPCa at both the per-patient level (69.39% vs. 49.71%, p < 0.001) and per-lesion level (68.97% vs. 48.57%, p < 0.001). Multivariate logistic analysis indicated that compared with the cTB, the AI-cTB significantly improved the possibility of detecting csPCa (p < 0.001).
conclusionsAI-cTB effectively improved the csPCa detection rate. This study successfully integrated AI with TB in the routine clinical workflow and provided a research paradigm for prospective AI-integrated clinical studies.
trial registrationClinicalTrials.gov, NCT06362291.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.