Evidence map›Paper›PMID 35119914›Full record

ReviewThe British journal of radiology2022

Quality in MR reporting of the prostate – improving acquisition, the role of AI and future perspectives

Liang Wang, Daniel J Margolis, Min Chen, Xinming Zhao, Qiubai Li, Zhenghan Yang, Jie Tian, Zhenchang Wang

Erratum issuedOpen access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
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

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. Innovations in prostate cancer: introductory editorial.The British journal of radiology · 2022
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 2 countries.

Liang WangDepartment of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China.ORCID 0000-0003-3141-2609
Daniel J MargolisDepartment of Radiology, Weill Cornell Medicine/ New York Presbyterian, New York, United States.
Min ChenDepartment of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Xinming ZhaoDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Qiubai LiDepartment of Radiology, University of Iowa, Roy Carver College of Medicine, Iowa, United States.
Zhenghan YangDepartment of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China.
Jie TianBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China.
Zhenchang WangDepartment of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China.
Capital Medical University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNChinese Academy of Sciences · CNCornell University · USUniversity of Iowa · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The high quality of MRI reporting of the prostate is the most critical component of the service provided by a radiologist. Prostate MRI structured reporting with PI-RADS v. 2.1 has been proven to improve consistency, quality, guideline-based care in the management of prostate cancer. There is room for improved accuracy of prostate mpMRI reporting, particularly as PI-RADS core criteria are subjective for radiologists. The application of artificial intelligence may support radiologists in interpreting MRI scans. This review addresses the quality of prostate multiparametric MRI (mpMRI) structured reporting (include improvements in acquisition using artificial intelligence) in terms of size of prostate gland, imaging quality, lesion location, lesion size, TNM staging, sector map, and discusses the future prospects of quality in MR reporting.

Indexed as

Artificial IntelligenceMagnetic Resonance ImagingHumansImage Interpretation, Computer-AssistedMaleNeoplasm StagingProstatic NeoplasmsQuality ImprovementReproducibility of Results

Identifiers

PMID35119914
PMCPMC8978223
OpenAlexW4210720979

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.