Evidence map›Paper›PMID 41272268›Full record

SynthesisProstate cancer and prostatic diseases2026

AI-aided diagnostic performance for prostate MRI: systematic review and meta-analysis.

Xin-Ru Xie, Ying Hou, Shuai Shan, Rui Zhi, Chen-Jiang Wu, Yi-Fan Xia, Wei Xi, Zhen Li, Yu-Dong Zhang

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Prostate cancer and prostatic diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. The evolving PI-RADS paradigm.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Review
  3. Review
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Xin-Ru XieDepartment of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, PR China.
Ying HouDepartment of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, PR China.
Shuai ShanDepartment of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, PR China.
Rui ZhiDepartment of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, PR China.
Chen-Jiang WuDepartment of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, PR China.ORCID http://orcid.org/0000-0003-1728-4380
Yi-Fan XiaDepartment of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, PR China.
Wei XiShanghai Normal Medical Technology Co, Ltd, Putuo, Shanghai, PR China.
Zhen Li *Department of Radiology, Tongji Hospital Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
Yu-Dong Zhang *Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, PR China. zhangyd3895@njmu.edu.cn.ORCID http://orcid.org/0000-0002-2811-7513

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82272082National Natural Science Foundation of China (National Science Foundation of China) 82302308
6 · The paper itself

Abstract

backgroundAI is increasingly integrated within prostate cancer diagnosis pathway. PURPOSE: To provide estimates of diagnostic accuracy of AI assistance for clinically significant prostate cancer (csPCa) via MRI. MATERIALS AND

methodsA systematic search of PubMed, Embase, Cochrane, Scopus and Web of Science from January 2017 to October 2024 was performed for studies on the diagnostic utility of AI for prostate MRI. Diagnostic performance metrics were synthesized through hierarchical summary receiver operating characteristic modeling with random-effects assumptions. Specially, to test inferiority and potential superiority of AI, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), cancer detection rate (CDR), and accuracy was pairwisely compared between AI and radiologists in study level using odds ratios (ORs) with Z-statistics.

results7398 patients from 29 studies with AI-vs-human pairwise comparison were included. When acting as an assistant to human readers, AI demonstrated superior performance compared to stand-alone human readers in diagnosing csPCa via MRI, specifically with higher sensitivity (86.5% vs 82.6%, P = 0.001), specificity (57.8% vs 50.0%, P = 0.028), PPV (64.3% vs 58.9%, P = 0.001), and NPV (82.9% vs 76.5%, P = 0.001) while maintaining comparable CDR (40.5% vs 38.6%, P = 0.093). When used as standalone readers, AI exhibited higher specificity (58.7% vs 48.7%, P = 0.026) but at the cost of reduced sensitivity (87.2% vs 90.1%, P = 0.017). Subgroup analysis indicated that readers of varying experience levels could all improve their diagnostic performance with AI assistance.

conclusionIntegrating AI as an assistant in csPCa diagnostic workflows could enhance accuracy, particularly for less experienced readers. CLINICAL TRIAL REGISTRATION INFORMATION: Trial Name: The efficiency comparison of radiologists with or without assistance of artificial intelligence in prostate cancer diagnosis: a meta-analysis. Registration date: April 17, 2024. REGISTRATION NUMBER: CRD42024533016. Registration information available at: https://www.crd.york.ac.uk/PROSPERO/ .

Indexed as

Artificial IntelligenceDiagnosis, Computer-AssistedMagnetic Resonance ImagingProstatic NeoplasmsHumansMale

Identifiers

PMID41272268

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.