Evidence map›Paper›PMID 38503998›Full record

ArticleJapanese journal of radiology2024

Investigation of imaging features in contrast-enhanced magnetic resonance imaging of benign and malignant breast lesions.

Kazunori Kubota, Tomoyuki Fujioka, Ukihide Tateishi, Mio Mori, Yuka Yashima, Emi Yamaga, Leona Katsuta, Ken Yamaguchi, Mitsuhiro Tozaki, Michiro Sasaki and 8 more

Open access · hybridAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Japanese journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Application of Machine Learning to Breast MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
    Review
  3. 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

18 authors at 11 institutions in 1 country.

Kazunori KubotaDepartment of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamiko-Shigaya, Koshigaya, Saitama, 343-8555, Japan.
Tomoyuki FujiokaDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8519, Japan. fjokmrad@tmd.ac.jp.ORCID http://orcid.org/0000-0002-7141-8901
Ukihide TateishiDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8519, Japan.
Mio MoriDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8519, Japan.
Yuka YashimaDepartment of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamiko-Shigaya, Koshigaya, Saitama, 343-8555, Japan.
Emi YamagaDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8519, Japan.
Leona KatsutaDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8519, Japan.
Ken YamaguchiDepartment of Radiology, Faculty of Medicine, Saga University, 5-1-1, Nabeshima, Saga City, Saga, 849-8501, Japan.
Mitsuhiro TozakiDepartment of Radiology, Sagara Hospital, 3-31 Matsubara-Cho, Kagoshima City, Kagoshima, 892-0833, Japan.
Michiro SasakiDepartment of Radiology, Sagara Hospital, 3-31 Matsubara-Cho, Kagoshima City, Kagoshima, 892-0833, Japan.
Takayoshi UematsuDivision of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Nagaizumi, Shizuoka, 411-8777, Japan.
Shuichi MonzawaDepartment of Diagnostic Radiology, Shinko Hospital, 1-4-47, Wakinohama-Cho, Chuo-Ku, Kobe City, Hyogo, 651-0072, Japan.
Ichiro IsomotoDepartment of Radiology, St. Francis Hospital, 9-20, Kominemachi, Nagasaki City, Nagasaki, 852-8125, Japan.
Mizuka SuzukiDepartment of Diagnostic Radiology, Tokyo Metropolitan Cancer and Infectious Disease Center, Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-Ku, Tokyo, 113-8677, Japan.
Hiroko SatakeDepartment of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan.
Hiroshi NakaharaDepartment of Radiology, Sagara Hospital Miyazaki, 2-112-1 Maruyama, Miyazaki City, Miyazaki, 880-0052, Japan.
Mariko GotoDepartment of Radiology, Kyoto Prefectural University of Medicine, 465 Kajii-Cho, Kamigyo-Ku, Kyoto City, 602-8566, Japan.
Mari KikuchiDepartment of Imaging Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-Ku, Tokyo, 135-8550, Japan.
Tokyo Medical and Dental University · JPNational Sagamihara Hospital · JPJapanese Foundation For Cancer Research · JPKyoto Prefectural University of Medicine · JPNagasaki Municipal Hospital · JPNagoya University · JPSaga University · JPShinko Hospital · JPShizuoka Cancer Center · JPTokyo Metropolitan Komagome Hospital · JPUniversity of Miyazaki Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to enhance the diagnostic accuracy of contrast-enhanced breast magnetic resonance imaging (MRI) using gadobutrol for differentiating benign breast lesions from malignant ones. Moreover, this study sought to address the limitations of current imaging techniques and criteria based on the Breast Imaging Reporting and Data System (BI-RADS). MATERIALS AND

methodsIn a multicenter retrospective study conducted in Japan, 200 women were included, comprising 100 with benign lesions and 100 with malignant lesions, all classified under BI-RADS categories 3 and 4. The MRI protocol included 3D fast gradient echo T1- weighted images with fat suppression, with gadobutrol as the contrast agent. The analysis involved evaluating patient and lesion characteristics, including age, size, location, fibroglandular tissue, background parenchymal enhancement (BPE), signal intensity, and the findings of mass and non-mass enhancement. In this study, univariate and multivariate logistic regression analyses were performed, along with decision tree analysis, to identify significant predictors for the classification of lesions.

resultsDifferences in lesion characteristics were identified, which may influence malignancy risk. The multivariate logistic regression model revealed age, lesion location, shape, and signal intensity as significant predictors of malignancy. Decision tree analysis identified additional diagnostic factors, including lesion margin and BPE level. The decision tree models demonstrated high diagnostic accuracy, with the logistic regression model showing an area under the curve of 0.925 for masses and 0.829 for non-mass enhancements.

conclusionThis study underscores the importance of integrating patient age, lesion location, and BPE level into the BI-RADS criteria to improve the differentiation between benign and malignant breast lesions. This approach could minimize unnecessary biopsies and enhance clinical decision-making in breast cancer diagnostics, highlighting the effectiveness of gadobutrol in breast MRI evaluations.

Indexed as

Breast NeoplasmsContrast MediaMagnetic Resonance ImagingOrganometallic CompoundsAdultAgedAged, 80 and overBreastDiagnosis, DifferentialFemaleHumansImage EnhancementImaging, Three-DimensionalJapanMiddle AgedReproducibility of ResultsContrast MediagadobutrolOrganometallic CompoundsBenign lesionsBIRADSBreast magnetic resonance imagingContrast-enhanced imagingGadobutrol

Identifiers

PMID38503998
PMCPMC11217097
OpenAlexW4393021521

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Registered trials

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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.