Evidence map›Paper›PMID 37265799›Full record

ArticleFrontiers in oncology2023

Association between ultrasound BI-RADS signs and molecular typing of invasive breast cancer.

Qiao-Hong Pan, Zheng-Pin Zhang, Liu-Yi Yan, Ning-Rui Jia, Xin-Yu Ren, Bei-Ke Wu, Yu-Bing Hao, Zhi-Fang Li

Abstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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.

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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
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

8 authors.

Qiao-Hong PanDepartment of Ultrasound, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
Zheng-Pin ZhangDepartment of Ultrasound, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
Liu-Yi YanDepartment of Ultrasound, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
Ning-Rui JiaDepartment of Ultrasound, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
Xin-Yu RenSchool of Public Health, Shanxi Medical University, Taiyuan, China.
Bei-Ke WuSchool of Public Health, Shanxi Medical University, Taiyuan, China.
Yu-Bing HaoSchool of Public Health, Shanxi Medical University, Taiyuan, China.
Zhi-Fang LiDepartment of Preventive Medicine, Changzhi Medical College, Changzhi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To explore the correlation between ultrasound images and molecular typing of invasive breast cancer, so as to analyze the predictive value of preoperative ultrasound for invasive breast cancer. Methods: 302 invasive breast cancer patients were enrolled in Heping Hospital affiliated to Changzhi Medical College in Shanxi, China during 2020 to 2022. All patients accepted ultrasonic and pathological examination, and all pathological tissues received molecular typing with immunohistochemical (IHC) staining. The relevance between different molecular typings and ultrasonic image, pathology were evaluated. Results: Univariate analysis: among the four molecular typings, there were significant differences in tumor size, shape, margin, lymph node and histological grade ( Conclusion: There are differences in the ultrasound manifestations of different molecular typings of breast cancer, and ultrasound features can be used as a potential imaging index to provide important information for the precise diagnosis and treatment of breast cancer.

Indexed as

BI-RADSbreast neoplasmsmolecular typingsonographic featuresultrasonography

Identifiers

PMID37265799
PMCPMC10230953

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.