Evidence map›Paper›PMID 30783148›Full record

ArticleScientific reports2019

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI.

Xiaoyu Cui, Nian Wang, Yue Zhao, Shuo Chen, Songbai Li, Mingjie Xu, Ruimei Chai

Open access · goldAbstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 6 pooled it
9.6field-weighted citation impact, top 1% 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

50 citing papers in PubMed, 6 syntheses or guidelines pooled it, 82 citations in OpenAlex.

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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 3 institutions in 1 country.

Xiaoyu CuiNortheastern University, Sino-Dutch Biomedical and Information Engineering School, Wisdom Street, Shenyang, 110819, China.
Nian WangNortheastern University, Sino-Dutch Biomedical and Information Engineering School, Wisdom Street, Shenyang, 110819, China.
Yue ZhaoNortheastern University, Sino-Dutch Biomedical and Information Engineering School, Wisdom Street, Shenyang, 110819, China.
Shuo ChenNortheastern University, Sino-Dutch Biomedical and Information Engineering School, Wisdom Street, Shenyang, 110819, China.
Songbai LiRadiology Department, The first hospital of China Medical University, 155# North Nanjing street, Heping district, Shenyang, 110001, China.
Mingjie XuNortheastern University, Sino-Dutch Biomedical and Information Engineering School, Wisdom Street, Shenyang, 110819, China.
Ruimei ChaiRadiology Department, The first hospital of China Medical University, 155# North Nanjing street, Heping district, Shenyang, 110001, China. wn1535522188@163.com.
Northeastern University · CNChina Medical University · CNFirst Hospital of China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accurate and noninvasive preoperative prediction of the state of the axillary lymph nodes is significant for breast cancer staging, therapy and the prognosis of patients. In this study, we analyzed the possibility of axillary lymph node metastasis directly based on Magnetic Resonance Imaging (MRI) of the breast in cancer patients. After mass segmentation and feature analysis, the SVM, KNN, and LDA three classifiers were used to distinguish the axillary lymph node state in 5-fold cross-validation. The results showed that the effect of the SVM classifier in predicting breast axillary lymph node metastasis was significantly higher than that of the KNN classifier and LDA classifier. The SVM classifier performed best, with the highest accuracy of 89.54%, and obtained an AUC of 0.8615 for identifying the lymph node status. Each feature was analyzed separately and the results showed that the effect of feature combination was obviously better than that of any individual feature on its own.

Indexed as

Magnetic Resonance ImagingPreoperative PeriodAdultBreast NeoplasmsFemaleHumansLymphatic MetastasisMiddle Aged

Identifiers

PMID30783148
PMCPMC6381163
OpenAlexW2941490313

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.