Evidence map›Paper›PMID 35083535›Full record

ArticleJournal of medical ultrasonics (2001)2022

Evaluation of an MRI/US fusion technique for the detection of non-mass enhancement of breast lesions detected by MRI yet occult on conventional B-mode second-look US.

Manami Goto, Shogo Nakano, Masayuki Saito, Hirona Banno, Yukie Ito, Mirai Ido, Takahito Ando, Junko Kousaka, Kimihito Fujii, Kojiro Suzuki

Abstract read
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In one paragraph

Article in Journal of medical ultrasonics (2001), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 19% 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

6 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Artificial intelligence diagnosis based on breast ultrasound imaging.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022
    Article
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 1 institution in 1 country.

Manami GotoDivision of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi, 480-1195, Japan.
Shogo NakanoDivision of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi, 480-1195, Japan. snakano1@aichi-med-u.ac.jp.ORCID http://orcid.org/0000-0003-2794-1823
Masayuki SaitoDivision of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi, 480-1195, Japan.
Hirona BannoDivision of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi, 480-1195, Japan.
Yukie ItoDivision of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi, 480-1195, Japan.
Mirai IdoDivision of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi, 480-1195, Japan.
Takahito AndoDivision of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi, 480-1195, Japan.
Junko KousakaDivision of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi, 480-1195, Japan.
Kimihito FujiiDivision of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi, 480-1195, Japan.
Kojiro SuzukiDepartment of Radiology, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi, 480-1195, Japan.
Aichi Medical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of this study was to verify the utility of second-look ultrasound (US) using real-time virtual sonography (RVS), a magnetic resonance imaging (MRI)/US fusion technique, in identifying MRI-detected breast lesions with non-mass enhancement (NME).

methodsConsecutive patients who had one or more NME lesions detected by MRI yet occult on the subsequent second-look US in conventional B (cB)-mode imaging were enrolled in the study between June 2015 and April 2020. Supine MRI of the lesions was performed and, using its data, second-look US using RVS was performed.

resultsTwenty patients with 21 NME lesions were included. The overall median lesion size on prone MRI was 23 mm (range, 5-63 mm). Supine MRI identified all the 21 NME lesions, and second-look US using RVS successfully detected 18 (86%) of them. RVS-guided biopsy was performed for histopathological evaluation, showing that nine of the 18 lesions were benign and the other nine malignant. Of the nine malignant lesions, two (22%) were invasive cancer and seven (78%) were ductal carcinoma in situ. In four of five patients who underwent prone MRI for preoperative evaluation, the diagnosis was benign and surgery was conducted as originally planned. In the other patient, the diagnosis was malignant and contralateral breast-conserving surgery was added. Three (14%) of the 21 NME lesions had no RVS correlates and were judged to be benign after 24-month follow-up.

conclusionThe results suggest that second-look US using RVS helps identify MRI-detected NME lesions that are occult on cB-mode second-look US.

Indexed as

Breast NeoplasmsCarcinoma, Intraductal, NoninfiltratingBiopsyBreastFemaleHumansMagnetic Resonance ImagingRetrospective StudiesSensitivity and SpecificityUltrasonographyUltrasonography, MammaryBreast cancerMRI-detected lesionNon-mass enhancementReal-time virtual sonographySecond-look US

Identifiers

PMID35083535
OpenAlexW4210816059

What OpenQuestion holds

Textmetadata
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.