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基于超快速序列的预测模型和列线图用于鉴别术前乳腺MRI上的其他可疑病变。

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

作者信息

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

机构信息

Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.

Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea.

出版信息

Eur Radiol. 2025 Jan;35(1):188-201. doi: 10.1007/s00330-024-10931-0. Epub 2024 Jul 17.

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.

摘要

目的

探讨超快序列是否能提高传统动态对比增强磁共振成像(DCE-MRI)在术前乳腺MRI中鉴别额外可疑病变(ASL)的诊断性能。

材料与方法

通过回顾性数据库检索,确定了2020年6月至2021年7月期间连续接受超快序列术前乳腺DCE-MRI检查的668例患者。其中,从98例乳腺癌患者中识别出107个ASL(36个多灶性、42个多中心性和29个对侧性)。收集临床、病理、传统MRI表现以及超快序列衍生参数。开发了一个将超快序列衍生参数添加到临床、病理和传统MRI表现中的预测模型,并在内部进行了验证。进行决策曲线分析和净重新分类指数统计。构建了列线图。

结果

与包括年龄、索引癌的人表皮生长因子受体2表达、索引癌大小、索引癌病变类型、ASL位置和ASL大小的传统模型相比,添加达峰时间、强化时间和最大斜率的超快模型在受试者工作特征曲线下面积显著增加(0.92对0.82;p = 0.002)。决策曲线分析表明,超快模型的总体净效益高于传统模型。超快模型的净重新分类指数为23.3%(p = 0.001)。

结论

超快序列衍生参数与临床、病理和传统MRI表现相结合有助于术前乳腺MRI中ASL的鉴别。

临床相关性声明

我们基于超快序列衍生参数的预测模型和列线图可帮助放射科医生在术前乳腺MRI中鉴别ASL。

关键点

超快MRI可减少背景实质强化,并可能提高对额外可疑病变(ASL)的诊断准确性。ASL的位置、较大的ASL大小和较高的最大斜率与恶性ASL相关。超快模型和列线图有助于术前鉴别其他恶性肿瘤。

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