Evidence map›Paper›PMID 39014088›Full record

ArticleEuropean radiology2025

Ultrafast sequence-based prediction model and nomogram to differentiate additional suspicious lesions on preoperative breast MRI.

Haejung Kim, Sang Ah Chi, Kyunga Kim, Boo-Kyung Han, Eun Young Ko, Ji Soo Choi, Jeongmin Lee, Myoung Kyoung Kim, Eun Sook Ko

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Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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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.

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Abbreviated and Ultrafast Dynamic Contrast-enhanced (DCE) MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
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4 · The record

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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

9 authors.

Haejung KimDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Sang Ah ChiBiomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea.
Kyunga KimBiomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea.
Boo-Kyung HanDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Eun Young KoDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Ji Soo ChoiDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Jeongmin LeeDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Myoung Kyoung KimDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Eun Sook KoDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. mathilda0330@gmail.com.ORCID http://orcid.org/0000-0002-0399-7956

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate whether ultrafast sequence improves the diagnostic performance of conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating additional suspicious lesions (ASLs) on preoperative breast MRI. MATERIALS AND

methodsA retrospective database search identified 668 consecutive patients who underwent preoperative breast DCE-MRI with ultrafast sequence between June 2020 and July 2021. Among these, 107 ASLs from 98 patients with breast cancer (36 multifocal, 42 multicentric, and 29 contralateral) were identified. Clinical, pathological, conventional MRI findings, and ultrafast sequence-derived parameters were collected. A prediction model that adds ultrafast sequence-derived parameters to clinical, pathological, and conventional MRI findings was developed and validated internally. Decision curve analysis and net reclassification index statistics were performed. A nomogram was constructed.

resultsThe ultrafast model adding time to peak enhancement, time to enhancement, and maximum slope showed a significantly increased area under the receiver operating characteristic curve compared with the conventional model which includes age, human epidermal growth factor receptor 2 expression of index cancer, size of index cancer, lesion type of index cancer, location of ASL, and size of ASL (0.92 vs. 0.82; p = 0.002). The decision curve analysis showed that the ultrafast model had a higher overall net benefit than the conventional model. The net reclassification index of ultrafast model was 23.3% (p = 0.001).

conclusionA combination of ultrafast sequence-derived parameters with clinical, pathological, and conventional MRI findings can aid in the differentiation of ASL on preoperative breast MRI. CLINICAL RELEVANCE STATEMENT: Our prediction model and nomogram that was based on ultrafast sequence-derived parameters could help radiologists differentiate ASLs on preoperative breast MRI. KEY POINTS: Ultrafast MRI can diminish background parenchymal enhancement and possibly improve diagnostic accuracy for additional suspicious lesions (ASLs). Location of ASL, larger size of ASL, and higher maximum slope were associated with malignant ASL. The ultrafast model and nomogram can help preoperatively differentiate additional malignancies.

Indexed as

Breast NeoplasmsContrast MediaMagnetic Resonance ImagingNomogramsAdultAgedBreastDiagnosis, DifferentialFemaleHumansMiddle AgedPreoperative CareRetrospective StudiesContrast MediaBreast neoplasmMagnetic resonance imagingNomogramsUltrafast

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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.