ReviewCureus2025
Artificial Intelligence Across the Prostate Cancer Pathway: Screening, Imaging, Pathology, and Biomarkers.
Review in Cureus, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Review
- Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has made great changes to prostate cancer screening and early detection across biomarkers, imaging, and pathology. On micro-ultrasound (micro-US), AI improves discrimination and raises specificity at comparable sensitivity versus clinical models, while multimodal magnetic resonance imaging-transrectal US (MRI-TRUS) AI achieves higher specificity at matched sensitivity. Liquid-biopsy programs combine fragmentomics with ctDNA and cell-free mRNA interpreted by AI, enabling noninvasive risk stratification and clinical feasibility. In imaging, AI for MRI matches or exceeds expert radiologists in large reader studies and MRI benchmarks; commercial tools show robust patient- and lesion-level performance. Quantitative pipelines (e.g., automated tissue-composition metrics) aid equivocal Prostate Imaging-Reporting and Data System (PI-RADS 3) lesions with PSA density, and AI-derived intraprostatic tumor volume offers independent prognostic value. Multimodal fusion of MRI with TRUS boosts detection, and automated prostate-specific membrane antigen (PSMA) PET/CT algorithms quantify tumor burden and support longitudinal response tracking. In pathology, clinical-grade AI automates cancer detection and Gleason grading, cutting reading time, ancillary tests, and second-opinion requests, while supporting integrative prognostic models. Downstream, AI accelerates radiotherapy planning, guides focal therapies and surgical margins, personalizes systemic therapy, and enables early post-treatment monitoring. Translation still requires rigorous, prospective, multi-site validation.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.