Evidence map›Paper›PMID 36177910›Full record

Trial reportThoracic cancer2022

Comparison of ultrasound and mammography for early diagnosis of breast cancer among Chinese women with suspected breast lesions: A prospective trial.

Yingjiao Wang, Yuechong Li, Yu Song, Chang Chen, Zhe Wang, Linrong Li, Mohan Liu, Guanmo Liu, Yali Xu, Yidong Zhou and 2 more

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in Thoracic cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
7.0field-weighted citation impact, top 2% 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

42 citing papers in PubMed, 54 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

12 authors at 1 institution in 1 country.

Yingjiao WangDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.ORCID 0000-0003-1370-3605
Yuechong LiDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Yu SongDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Chang ChenDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Zhe WangDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Linrong LiDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Mohan LiuDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Guanmo LiuDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Yali XuDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Yidong ZhouDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Qiang SunDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.ORCID 0000-0002-2556-1803
Songjie ShenDepartment of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.ORCID 0000-0003-2510-1054
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUltrasound is more widely used than mammography for early diagnosis of breast cancer in China as most Chinese women have small and dense mammary glands. This study compared the diagnostic performance of ultrasound and mammography for breast cancer among Chinese women with suspected breast lesions.

methodsFrom November 2019 to November 2021, we compared the results of ultrasound and mammography for breast lesion diagnosis in 2737 consecutive participants with suspected breast lesions; all patients underwent biopsies. We measured the sensitivity, specificity, and diagnostic accuracy separately.

resultsAmong the 2737 participants, 2844 breast lesions were detected, including 1935 (68.0%) breast cancers and 909 (32.0%) benign lesions. Of the breast cancers, ultrasound detected 1851 (95.7%), whereas mammography detected 1527 (78.9%). The sensitivity of ultrasound for breast cancer diagnosis was significantly higher than that of mammography (95.7% vs. 78.9%, p < 0.001), whereas the specificity was significantly lower than that of mammography (42.9% vs. 62.3%, p < 0.001). The receiver operating characteristic curves revealed that ultrasound was more accurate in detecting breast cancer than mammography (76.8% vs. 71.3%, p < 0.001). Age, body mass index, and breast density did not influence ultrasound sensitivity and accuracy.

conclusionsUltrasound is more sensitive and accurate than mammography and detects more breast cancers with a lower specificity.

Indexed as

Breast NeoplasmsEarly Detection of CancerFemaleHumansMammographyProspective StudiesSensitivity and SpecificityUltrasonography, Mammarybreast cancerearly diagnosismammographyultrasound

Identifiers

PMID36177910
PMCPMC9663682
OpenAlexW4298087619

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.