Evidence map›Paper›PMID 38662208›Full record

ArticleAbdominal radiology (New York)2024

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

Qiong Li, Wei-Yue Xu, Na-Na Sun, Qiu-Xia Feng, Ya-Jun Hou, Zi-Tong Sang, Zhen-Ning Zhu, Yi-Cheng Hsu, Dominik Nickel, Hao Xu and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Deep Learning-accelerated MRI in Body and Chest.Journal of computer assisted tomography
    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

12 authors at 3 institutions in 2 countries.

Qiong Li *Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Wei-Yue Xu *Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Na-Na SunDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Qiu-Xia FengDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Ya-Jun HouDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Zi-Tong SangDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Zhen-Ning ZhuDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Yi-Cheng HsuMR Collaboration, Siemens Healthineers Ltd, Shanghai, China.
Dominik NickelMR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
Hao XuDepartment of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Yu-Dong ZhangDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xi-Sheng LiuDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. njmu_lxs@163.com.ORCID 0000-0002-6475-5483
Jiangsu Province Hospital · CNSiemens (China) · CNSiemens Healthineers (Germany) · DE

Funding

National Natural Science Foundation of China 82071919National Natural Science Foundation of China 82272082
6 · The paper itself

Abstract

purposeThe purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T

methods112 patients with GCs undergoing gastric MRI were prospectively enrolled between Aug 2022 and Dec 2022. Axial DLSB-T

resultsAgainst BLADE, DLSB reduced total acquisition time of T2WI from 495 min (mean 4:42 per patient) to 33.6 min (18 s per patient), with better overall image quality that produced 9.43-fold, 8.00-fold, and 18.31-fold IQS upgrading against BALDE, respectively, in three readers. In 69 measurable lesions, DLSB-T2WI had higher mean SNR and higher CNR than BLADE-T2WI. Among 71 patients with gastrectomy, DLSB-T2WI resulted in comparable accuracy to BLADE-T2WI in staging GCs (P > 0.05).

conclusionsDLSB-T

Indexed as

Deep LearningMagnetic Resonance ImagingNeoplasm StagingStomach NeoplasmsAdultAgedAged, 80 and overBreath HoldingFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedProspective StudiesReproducibility of ResultsSignal-To-Noise RatioGastric cancerImage qualityMagnetic resonance imagingStaging performanceT2-weighted imaging

Identifiers

PMID38662208
OpenAlexW4395463845

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.