Evidence map›Paper›PMID 34930038›Full record

ArticleThe British journal of radiology2022

Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer.

Wei-Mei Ma, Jiao Li, Shuang-Gang Chen, Pei-Qiang Cai, Shen Chen, Jie-Ting Chen, Chun-Yan Zhou, Ni He, Yaopan Wu

Open access · bronzeAbstract read
In one paragraph

Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 26% 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

4 citing papers in PubMed, 18 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

9 authors at 1 institution in 1 country.

Wei-Mei MaDepartment of Radiology, the Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong Province, People's Republic of China, China.
Jiao LiDepartment of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong Province, People's Republic of China, China.
Shuang-Gang ChenDepartment of Oncology, Yuebei People's Hospital, Shantou University Medical College, Shaoguan, Guangdong Province, People's Republic of China, China.
Pei-Qiang CaiDepartment of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong Province, People's Republic of China, China.
Shen ChenDepartment of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong Province, People's Republic of China, China.
Jie-Ting ChenDepartment of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong Province, People's Republic of China, China.
Chun-Yan ZhouDepartment of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong Province, People's Republic of China, China.
Ni HeDepartment of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong Province, People's Republic of China, China.
Yaopan WuDepartment of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong Province, People's Republic of China, China.ORCID 0000-0001-8551-2459
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer.

methodsOverall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test.

resultsAs regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (

conclusionCE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage. ADVANCES IN KNOWLEDGE: CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

Indexed as

Breast NeoplasmsBreastCone-Beam Computed TomographyFemaleHumansMammographyNeoplasm StagingPrognosisReproducibility of ResultsRetrospective Studies

Identifiers

PMID34930038
PMCPMC9153710
OpenAlexW4200398994

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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.