Evidence map›Paper›PMID 35186739›Full record

SynthesisFrontiers in oncology2022

The Diagnostic Performance of Machine Learning-Based Radiomics of DCE-MRI in Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Meta-Analysis.

Jing Zhang, Longchao Li, Xia Zhe, Min Tang, Xiaoling Zhang, Xiaoyan Lei, Li Zhang

Open access · goldAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 9 pooled it
5.8field-weighted citation impact, top 3% 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

26 citing papers in PubMed, 9 syntheses or guidelines pooled it, 41 citations in OpenAlex.

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  3. Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
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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

7 authors at 2 institutions in 1 country.

Jing ZhangDepartment of MRI, Shaanxi Provincial People's Hospital, Xi'an, China.
Longchao LiDepartment of MRI, Shaanxi Provincial People's Hospital, Xi'an, China.
Xia ZheDepartment of MRI, Shaanxi Provincial People's Hospital, Xi'an, China.
Min TangDepartment of MRI, Shaanxi Provincial People's Hospital, Xi'an, China.
Xiaoling ZhangDepartment of MRI, Shaanxi Provincial People's Hospital, Xi'an, China.
Xiaoyan LeiDepartment of MRI, Shaanxi Provincial People's Hospital, Xi'an, China.
Li ZhangDepartment of MRI, Shaanxi Provincial People's Hospital, Xi'an, China.
Shanxi Medical University · CNShaanxi Provincial People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe aim of this study was to perform a meta-analysis to evaluate the diagnostic performance of machine learning(ML)-based radiomics of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) DCE-MRI in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis(SLNM) in breast cancer.

methodsEnglish and Chinese databases were searched for original studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) were used to assess the methodological quality of the included studies. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were used to summarize the diagnostic accuracy. Spearman's correlation coefficient and subgroup analysis were performed to investigate the cause of the heterogeneity.

resultsThirteen studies (1618 participants) were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.82 (0.75, 0.87), 0.83 (0.74, 0.89), 21.56 (10.60, 43.85), and 0.89 (0.86, 0.91), respectively. The meta-analysis showed significant heterogeneity among the included studies. There was no threshold effect in the test. The result of subgroup analysis showed that ML, 3.0 T, area of interest comprising the ALN, being manually drawn, and including ALNs and combined sentinel lymph node (SLN)s and ALNs groups could slightly improve diagnostic performance compared to deep learning, 1.5 T, area of interest comprising the breast tumor, semiautomatic scanning, and the SLN, respectively.

conclusionsML-based radiomics of DCE-MRI has the potential to predict ALNM and SLNM accurately. The heterogeneity of the ALNM and SLNM diagnoses included between the studies is a major limitation.

Indexed as

axillary lymph node metastasisbreast cancerdynamic contrast-enhanced magnetic resonance imagingmachine learningmeta-analysisradiomics

Identifiers

PMID35186739
PMCPMC8854258
OpenAlexW4210502221

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.