目的:探讨超快序列是否能提高常规动态对比增强磁共振成像(DCE-MRI)术前乳腺MRI鉴别其他可疑病变(ASL)的诊断效能。
方法:回顾性数据库搜索确定了在2020年6月至2021年7月期间接受术前乳腺DCE-MRI超快序列的668例连续患者。其中,来自98例乳腺癌患者的107例ASL(36例多灶性,42多中心,和29对侧)进行了鉴定。临床,病态,常规MRI检查结果,并收集了超快序列衍生参数。将超快序列衍生参数添加到临床的预测模型,病态,常规MRI检查结果在内部进行开发和验证。进行了决策曲线分析和净分类指数统计。构造了一个列线图。
结果:超快模型增加峰值增强时间,增强时间,与包括年龄在内的常规模型相比,最大斜率显示接收器工作特性曲线下的面积显着增加,人表皮生长因子受体2表达指标癌,癌症指数的大小,指示癌症的病变类型,ASL的位置,和ASL的大小(0.92vs.0.82;p=0.002)。决策曲线分析表明,超快模型比常规模型具有更高的总体净收益。超快模型的净再分类指数为23.3%(p=0.001)。
结论:超快序列衍生参数与临床,病态,和常规MRI检查结果可以帮助鉴别术前乳腺MRI上的ASL。
结论:我们的基于超快序列衍生参数的预测模型和列线图可以帮助放射科医师在术前乳腺MRI上区分ASLs。
结论:超快MRI可以减少背景实质增强,并可能提高对其他可疑病变(ASLs)的诊断准确性。ASL的位置,更大尺寸的ASL,最大斜率较高与恶性ASL相关。超快模型和列线图可以帮助术前区分其他恶性肿瘤。
OBJECTIVE: 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.
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).
CONCLUSIONS: 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.
CONCLUSIONS: Our prediction model and nomogram that was based on ultrafast sequence-derived parameters could help radiologists differentiate ASLs on preoperative breast MRI.
CONCLUSIONS: 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.