The study of building a multi-modal imaging BI-RADS predictive model for breast lesions

XIE Yamie; ZHU Ying; CHAI Weimin; ZONG Shaoyun; ZHANG Xiaoxiao; ZHAN Weiwei

doi:10.19732/j.cnki.2096-6210.2021.05.007

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The study of building a multi-modal imaging BI-RADS predictive model for breast lesions

更新时间：2025-12-15

- The study of building a multi-modal imaging BI-RADS predictive model for breast lesions
- Vol. 30, Issue 5, Pages: 362-367(2021)
- 作者机构：
  
  1. 昆明理工大学医学院附属第一人民医院超声科,云南,昆明,650500
  2. 上海交通大学医学院附属瑞金医院超声科,上海,200025
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2021.05.007
  CLC：
- Published Online：28 October 2021，
  
  Published：28 October 2021
- 稿件说明：
移动端阅览
谢亚咩,朱樱,柴维敏,等. 对乳腺肿块构建多模态影像BI-RADS预测模型的研究[J]. 肿瘤影像学, 2021, 30(5): 362-367 https://doi.

org/10.19732/j.cnki.2096-6210.2021.05.007
谢亚咩,朱樱,柴维敏,等. 对乳腺肿块构建多模态影像BI-RADS预测模型的研究[J]. 肿瘤影像学, 2021, 30(5): 362-367 https://doi. DOI： 10.19732/j.cnki.2096-6210.2021.05.007.

org/10.19732/j.cnki.2096-6210.2021.05.007 DOI：

摘要

目的：

构建乳腺肿块的综合乳腺影像报告和数据系统（Breast Imaging Reporting and Data System，BI-RADS）预测模型，得到不同于单一影像的BI-RADS分类。

方法：

回顾并分析2019年8月2020年9月术前行超声、乳腺X线摄影及磁共振成像（magnetic resonance imaging，MRI）检查的患者图像，根据BI-RADS词典对肿块特征进行评估，取3种影像的最高BI-RADS类别为因变量，影像学特征及临床指征为自变量，根据多元logistic回归构建综合BI-RADS预测模型。

结果：

综合BI-RADS预测模型分类的阳性预测值（positive predictive value，PPV）在指南的参考范围内［3类（0.00%），4A类（9.61%），4B类（42.41%），4C类（88.18%），5类（97.19%）］，模型的受试者工作特征（receiver operating characteristic）曲线的曲线下面积（area under curve，AUC）为0.955。

结论：

将3种影像的BI-RADS词典联合肿块临床特征得到综合的BI-RADS模型是可行的，极大限度避免漏诊的发生。

Abstract

Objective:

To build a comprehensive Breast Imaging Reporting and Data System (BI-RADS) predictive model of breast lesions for getting BI-RADS classification that different from single imaging.

Methods:

The images of patients undergoing preoperative ultrasound

mammography and magnetic resonance imaging (MRI) examination from August 2019 to September 2020 were retrospectively analyzed. The tumor characteristics were evaluated according to the BI-RADS lexicon. The

highest BI-RADS category of the three images was taken as the dependent variable

and the imaging characteristics and clinical indications were taken as independent variables. A comprehensive BI-RADS predictive model was constructed based on multivariate logistic regression.

Results:

The positive predictive value (PPV) of the comprehensive BI-RADS predictive model category was within the reference range of the guide ［category 3 (0.00%)

category 4A (9.61%)

category 4B (42.41%)

category 4C (88.18%)

category 5 (97.19%)］

the area under curve (AUC) of the receiver operating characteristic (ROC) curve was 0.955.

Conclusion:

It is feasible to unify the BI-RADS lexicon of the three imaging and the clinical features of the patients to obtain the comprehensive BI-RADS model

which greatly avoids the incidence of misdiagnosis.

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