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Ultrafast sequence-based prediction model and nomogram to differentiate additional suspicious lesions on preoperative breast MRI.
Kim, Haejung; Chi, Sang Ah; Kim, Kyunga; Han, Boo-Kyung; Ko, Eun Young; Choi, Ji Soo; Lee, Jeongmin; Kim, Myoung Kyoung; Ko, Eun Sook.
Afiliação
  • Kim H; Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Chi SA; Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea.
  • Kim K; Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea.
  • Han BK; Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.
  • Ko EY; Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Choi JS; Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Lee J; Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Kim MK; Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Ko ES; Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.
Eur Radiol ; 2024 Jul 17.
Article em En | MEDLINE | ID: mdl-39014088
ABSTRACT

OBJECTIVES:

To 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

METHODS:

A 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.

RESULTS:

The 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).

CONCLUSION:

A 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Eur Radiol Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Eur Radiol Ano de publicação: 2024 Tipo de documento: Article