SynthesisJournal of medical Internet research2025
Artificial Intelligence-Enabled Imaging for Predicting Preoperative Extraprostatic Extension in Prostate Cancer: Systematic Review and Meta-Analysis.
Synthesis in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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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.
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Who cites it
2 citing papers in PubMed.
- Multimodal artificial intelligence for prostate cancer imaging: workflow-relevant fusion of mpMRI, PSMA PET, ultrasound, and clinical data for diagnosis, local staging, and treatment personalization.Abdominal radiology (New York) · 2026Review
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) techniques, particularly those using machine learning and deep learning to analyze multimodal imaging data, have shown considerable promise in enhancing preoperative prediction of extraprostatic extension (EPE) in prostate cancer. Objective: This meta-analysis compares the diagnostic performance of AI-enabled imaging techniques with that of radiologists for predicting preoperative EPE in prostate cancer. Methods: We conducted a systematic literature search in PubMed, Embase, and Web of Science up to September 2025, following PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy) guidelines. Studies applying AI techniques to predict EPE using multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen positron emission tomography (PSMA PET) imaging were included. Sensitivity, specificity, and area under the curve (AUC) for both internal and external validation sets were extracted and pooled using a bivariate random effects model. Study quality was assessed using the modified Quality Assessment of Diagnostic Performance Studies (QUADAS-2) tool. Results: A total of 21 studies were included in the analysis. For internal validation sets in patient-based analyses, mpMRI-based AI demonstrated a pooled sensitivity of 0.77 (95% CI 0.71-0.82), specificity of 0.71 (95% CI 0.64-0.78), and AUC of 0.81 (95% CI 0.77-0.84). In external validation, mpMRI-based AI achieved a sensitivity of 0.66 (95% CI 0.43-0.84), specificity of 0.80 (95% CI 0.64-0.90), and AUC of 0.80 (95% CI 0.77-0.84). In comparison, radiologists achieved a pooled sensitivity of 0.69 (95% CI 0.60-0.76), specificity of 0.73 (95% CI 0.66-0.78), and AUC of 0.77 (95% CI 0.73-0.80). Statistical comparisons between mpMRI-based AI and radiologists showed no significant difference in sensitivity (Z=1.61; P=.10), specificity (Z=0.43; P=.67). Conversely, the AUC of mpMRI-based AI was significantly higher than that of PSMA PET-based (Z=2.77; P=.01). PSMA PET-based AI showed moderate performance with sensitivity of 0.73 (95% CI 0.65-0.80), specificity of 0.61 (95% CI 0.30-0.85), and AUC of 0.74 (95% CI 0.70-0.77) in internal validation, and in external validation, it demonstrated sensitivity of 0.77 (95% CI 0.57-0.89) and specificity of 0.50 (95% CI 0.22-0.78), demonstrating no significant advantage over radiologists. Conclusions: mpMRI-based AI demonstrated improved diagnostic performance for preoperative prediction of EPE in prostate cancer compared to conventional radiological assessment, achieving higher AUC. However, PSMA PET-based AI models currently offer no significant advantage over either mpMRI-based AI or radiologists. Limitations include the retrospective design and high heterogeneity, which may introduce bias and affect generalizability. Larger, more diverse cohorts are essential for confirming these findings and optimizing the integration of AI in clinical practice.
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Registered trials
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 Socratic graph.