Evidence map›Paper›PMID 30328211›Full record

ArticleJournal of magnetic resonance imaging : JMRI2019

Feasibility and Diagnostic Performance of Voxelwise Computed Diffusion-Weighted Imaging in Breast Cancer.

Jiejie Zhou, Endong Chen, Huazhi Xu, Qiong Ye, Jiance Li, Shuxin Ye, Qinyuan Cheng, Liang Zhao, Min-Ying Su, Meihao Wang

Open access · greenAbstract read
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 17% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
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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

10 authors at 3 institutions in 2 countries.

Jiejie ZhouDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, P.R. China.
Endong ChenDepartment of Thyroid and Breast Surgery, First Affiliated Hospital of Wenzhou Medical University, P.R. China.
Huazhi XuDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, P.R. China.
Qiong YeDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, P.R. China.
Jiance LiDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, P.R. China.
Shuxin YeDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, P.R. China.
Qinyuan ChengDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, P.R. China.
Liang ZhaoDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, P.R. China.
Min-Ying SuTu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California, USA.
Meihao WangDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, P.R. China.
Wenzhou Medical University · CNFirst Affiliated Hospital of Wenzhou Medical University · CNUniversity of California, Irvine · US

Funding

Univ.of Calif., Irvine Cancer Center Support GrantP30CA062203 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Christine Hui · 1994 to 2026
$57.9M
Prediction of Neoadjuvant Chemo Pathological Response Using MRI MarkersR01CA127927 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI SU, MIN-YING L. · 2008 to 2017
$5.0M
Mammographic Density and Metabolic Genotyping for Predicting Cancer PrognosisR21CA208938 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI CHEN, JEON-HOR, SU, MIN-YING L. · 2017 to 2018
$370k
Natural Science Foundation of Zhejiang LY14H180006NCI NIH HHS P30 CA062203NCI NIH HHS R01 CA127927NCI NIH HHS R21 CA208938
6 · The paper itself

Abstract

backgroundConventional diffusion-weighted imaging (DWI) with high b-values may improve lesion conspicuity, but with a low signal intensity and thus a low signal-to-noise ratio (SNR). The voxelwise computed DWI (vcDWI) may generate high-quality images with a strong lesion signal and low background. PURPOSE: To evaluate the feasibility and diagnostic performance of vcDWI. STUDY TYPE: Retrospective. POPULATION: In all, 67 patients with 72 lesions, 33 malignant and 39 benign. FIELD STRENGTH/SEQUENCE: 3T, including T ASSESSMENT: Computed DWI (cDWI) with high b-values of 1500, 2000, 2500 s/mm STATISTICAL TESTS: Agreement among the three readers was evaluated by the intraclass correlation coefficient. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic performance based on reading of mDWI, cDWIs, vcDWI, and the measured ADC values.

resultsvcDWI provided the best lesion conspicuity compared with mDWI and cDWIs (P < 0.005). For overall image quality, vcDWI was significantly better than cDWI (P < 0.005), but not significantly better compared with mDWI for two readers (P = 0.037 and P = 0.013) and significantly worse for the third reader (P < 0.005). Background signal suppression was the best on cDWI LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Indexed as

Diffusion Magnetic Resonance ImagingAdolescentAdultAgedAlgorithmsArea Under CurveBiopsyBreastBreast NeoplasmsContrast MediaFeasibility StudiesFemaleHumansImage Processing, Computer-AssistedMiddle AgedObserver VariationContrast Mediabreast cancer diagnosiscomputed diffusion weighted imaging (cDWI)diffusion weighted imaging (DWI)voxelwise cDWI (vcDWI

Identifiers

PMID30328211
PMCPMC6690367
OpenAlexW2896161475

What OpenQuestion holds

Textmetadata
LicenceTDM
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.