Evidence map›Paper›PMID 38238017›Full record

ArticleKorean journal of radiology2024

Automated Versus Handheld Breast Ultrasound for Evaluating Axillary Lymph Nodes in Patients With Breast Cancer.

Sun Mi Kim, Mijung Jang, Bo La Yun, Sung Ui Shin, Jiwon Rim, Eunyoung Kang, Eun-Kyu Kim, Hee-Chul Shin, So Yeon Park, Bohyoung Kim

Open access · hybridAbstract read
In one paragraph

Article in Korean journal of radiology, 2024. 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
0.9field-weighted citation impact, top 29% 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, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. 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

10 authors at 4 institutions in 1 country.

Sun Mi KimDepartment of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.ORCID 0000-0003-0899-3580
Mijung JangDepartment of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea. mjjang74@gmail.com.ORCID 0000-0001-9619-6877
Bo La YunDepartment of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.ORCID 0000-0002-5457-7847
Sung Ui ShinDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.ORCID 0000-0001-6049-8419
Jiwon RimDepartment of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea.ORCID 0000-0001-5278-6496
Eunyoung KangDepartment of Surgery, Daerim St. Mary's Hospital, Seoul, Republic of Korea.ORCID 0000-0002-2591-1594
Eun-Kyu KimDepartment of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.ORCID 0000-0003-1318-0939
Hee-Chul ShinDepartment of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.ORCID 0000-0001-6512-2218
So Yeon ParkDepartment of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.ORCID 0000-0002-0299-7268
Bohyoung KimDivision of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea.ORCID 0000-0002-2183-5651
Seoul National University Bundang Hospital · KRCHA University Bundang Medical Center · KRHankuk University of Foreign Studies · KRThe Catholic University of Korea Seoul St. Mary's Hospital · KR

Funding

SNUBH 14-2020-0017
6 · The paper itself

Abstract

objectiveAutomated breast ultrasound (ABUS) is a relevant imaging technique for early breast cancer diagnosis and is increasingly being used as a supplementary tool for mammography. This study compared the performance of ABUS and handheld ultrasound (HHUS) in detecting and characterizing the axillary lymph nodes (LNs) in patients with breast cancer. MATERIALS AND

methodsWe retrospectively reviewed the medical records of women with recently diagnosed early breast cancer (≤ T2) who underwent both ABUS and HHUS examinations for axilla (September 2017-May 2018). ABUS and HHUS findings were compared using pathological outcomes as reference standards. Diagnostic performance in predicting any axillary LN metastasis and heavy nodal-burden metastases (i.e., ≥ 3 LNs) was evaluated. The ABUS-HHUS agreement for visibility and US findings was calculated.

resultsThe study included 377 women (53.1 ± 11.1 years). Among 385 breast cancers in 377 patients, 101 had axillary LN metastases and 30 had heavy nodal burden metastases. ABUS identified benign-looking or suspicious axillary LNs (average, 1.4 ± 0.8) in 246 axillae (63.9%, 246/385). According to the per-breast analysis, the sensitivity, specificity, positive and negative predictive values, and accuracy of ABUS in predicting axillary LN metastases were 43.6% (44/101), 95.1% (270/284), 75.9% (44/58), 82.6% (270/327), and 81.6% (314/385), respectively. The corresponding results for HHUS were 41.6% (42/101), 95.1% (270/284), 75.0% (42/56), 82.1% (270/329), and 81.0% (312/385), respectively, which were not significantly different from those of ABUS (

conclusionAlthough ABUS showed limited sensitivity in diagnosing axillary LN metastasis in early breast cancer, it was still useful as the performance was comparable to that of HHUS.

Indexed as

Breast NeoplasmsAxillaFemaleHumansLymphatic MetastasisLymph NodesRetrospective StudiesSensitivity and SpecificityUltrasonography, MammaryAutomated breast ultrasound systemAxillary lymph nodeBreast cancerDiagnostic performanceUltrasound

Identifiers

PMID38238017
PMCPMC10831296
OpenAlexW4391006614

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.