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预测乳腺癌患者MRI检测病变恶性程度的评分系统:诊断性能及对二次超声检查的影响

Scoring System to Predict Malignancy for MRI-Detected Lesions in Breast Cancer Patients: Diagnostic Performance and Effect on Second-Look Ultrasonography.

作者信息

Kwon Young Geol, Park Ah Young

出版信息

Taehan Yongsang Uihakhoe Chi. 2020 Mar;81(2):379-394. doi: 10.3348/jksr.2020.81.2.379. Epub 2020 Mar 31.

DOI:10.3348/jksr.2020.81.2.379
PMID:36237381
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9431816/
Abstract

PURPOSE

To design a scoring system to predict malignancy of additional MRI-detected lesions in breast cancer patients.

MATERIALS AND METHODS

Eighty-six lesions (64 benign and 22 malignant) detected on preoperative MRI of 68 breast cancer patients were retrospectively included. The clinico-radiologic features were correlated with the histopathologic results using the Student's -test, Fisher's exact test, and logistic regression analysis. The scoring system was designed based on the significant predictive features of malignancy, and its diagnostic performance was compared with that of the Breast Imaging-Reporting and Data System (BI-RADS) category.

RESULTS

Lesion size ≥ 8 mm ( < 0.001), location in the same quadrant as the primary cancer ( = 0.005), delayed plateau kinetics ( = 0.010), T2 isointense ( = 0.034) and hypointense ( = 0.024) signals, and irregular mass shape ( = 0.028) were associated with malignancy. In comparison with the BI-RADS category, the scoring system based on these features with suspicious non-mass internal enhancement increased the diagnostic performance (area under the receiver operating characteristic curve: 0.918 vs. 0.727) and detected three false-negative cases. With this scoring system, 22 second-look ultrasound examinations (22/66, 33.3%) could have been avoided.

CONCLUSION

The scoring system based on the lesion size, location relative to the primary cancer, delayed kinetic features, T2 signal intensity, mass shape, and non-mass internal enhancement can provide a more accurate approach to evaluate MRI-detected lesions in breast cancer patients.

摘要

目的

设计一种评分系统,以预测乳腺癌患者MRI检测到的其他病变的恶性程度。

材料与方法

回顾性纳入68例乳腺癌患者术前MRI检测到的86个病变(64个良性病变和22个恶性病变)。使用学生t检验、Fisher精确检验和逻辑回归分析,将临床放射学特征与组织病理学结果进行相关性分析。基于恶性肿瘤的显著预测特征设计评分系统,并将其诊断性能与乳腺影像报告和数据系统(BI-RADS)分类进行比较。

结果

病变大小≥8mm(P<0.001)、与原发癌位于同一象限(P = 0.005)、延迟平台期动力学(P = 0.010)、T2等信号(P = 0.034)和低信号(P = 0.024)以及不规则肿块形状(P = 0.028)与恶性肿瘤相关。与BI-RADS分类相比,基于这些特征并伴有可疑非肿块内部强化的评分系统提高了诊断性能(受试者操作特征曲线下面积:0.918对0.727),并检测到3例假阴性病例。使用该评分系统,可以避免22次二次超声检查(22/66,33.3%)。

结论

基于病变大小、相对于原发癌的位置、延迟动力学特征、T2信号强度、肿块形状和非肿块内部强化的评分系统,可以为评估乳腺癌患者MRI检测到的病变提供更准确的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb7d/9431816/5fde0743f7e1/jksr-81-379-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb7d/9431816/11c27010b55d/jksr-81-379-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb7d/9431816/39972ec326bd/jksr-81-379-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb7d/9431816/5fde0743f7e1/jksr-81-379-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb7d/9431816/11c27010b55d/jksr-81-379-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb7d/9431816/39972ec326bd/jksr-81-379-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb7d/9431816/5fde0743f7e1/jksr-81-379-g003.jpg

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本文引用的文献

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