Evidence map›Paper›PMID 30701328›Full record

ArticleEuropean radiology2019

Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer.

Lu Han, Yongbei Zhu, Zhenyu Liu, Tao Yu, Cuiju He, Wenyan Jiang, Yangyang Kan, Di Dong, Jie Tian, Yahong Luo

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Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
119citing papers in PubMed, 5 pooled it
–field-weighted citation impact
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.

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3 · Its place in the literature

Who cites it

119 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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59 more citing papers are in PubMed but not listed here.

4 · The record

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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.

Lu HanCancer Hospital of China Medical University, Shenyang, 110042, China.
Yongbei ZhuCAS Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China.
Zhenyu LiuCAS Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China.
Tao YuCancer Hospital of China Medical University, Shenyang, 110042, China.
Cuiju HeCancer Hospital of China Medical University, Shenyang, 110042, China.
Wenyan JiangCancer Hospital of China Medical University, Shenyang, 110042, China.
Yangyang KanCancer Hospital of China Medical University, Shenyang, 110042, China.
Di DongCAS Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China. di.dong@ia.ac.cn.
Jie TianCAS Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China.
Yahong LuoCancer Hospital of China Medical University, Shenyang, 110042, China. Luoyahong8888@hotmail.com.ORCID http://orcid.org/0000-0002-0241-3007

Funding

National Key R&D Program of China 2017YFA0205200, 2017YFC1308700, 2017YFC1308701, 2017YFC1309100, 2016YFC0103803National Natural Science Foundation of China 81771924, 81501616, 81671851, and 81527805Special Fund for  Research in the Public Interest of China 201402020the Beijing Municipal Science and Technology Commission Z171100000117023, Z161100002616022the Science and Technology Service Network Initiative of the Chinese Academy of Sciences KFJ-SW-STS-160
6 · The paper itself

Abstract

objectiveTo develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients.

methodsPreoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram.

resultsThe radiomic signature based on 12 LN status-related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79).

conclusionsWe developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancer patients. KEY POINTS: • ALNM is an important factor affecting breast cancer patients' treatment and prognosis. • Traditional imaging examinations have limited value for evaluating axillary LNs status. • We developed a radiomic nomogram based on MR imagings to predict LN metastasis.

Indexed as

AxillaBreast NeoplasmsLymphatic MetastasisLymph NodesMagnetic Resonance ImagingNomogramsAdultAgedFemaleHumansMiddle AgedPrognosisRetrospective StudiesAxillary lymph node metastasisBreast cancerMRIPreoperative predictionRadiomics

Identifiers

PMID30701328

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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.