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基于三维容积超声的BI-RADS 4类乳腺结节诊断列线图。

A nomogram for diagnosis of BI-RADS 4 breast nodules based on three-dimensional volume ultrasound.

作者信息

Jiang Xianping, Chen Chen, Yao Jincao, Wang Liping, Yang Chen, Li Wei, Ou Di, Jin Zhiyan, Liu Yuanzhen, Peng Chanjuan, Wang Yifan, Xu Dong

机构信息

Department of Ultrasound, Shengzhou People's Hospital (Shengzhou Branch of the First Affiliated Hospital of Zhejiang University School of Medicine, the Shengzhou Hospital of Shaoxing University), Shengzhou, 312400, China.

Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, No.1 East Banshan Road, Gongshu District, Hangzhou, Zhejiang, 310022, China.

出版信息

BMC Med Imaging. 2025 Feb 14;25(1):48. doi: 10.1186/s12880-025-01580-w.

Abstract

OBJECTIVES

The classification of malignant breast nodules into four categories according to the Breast Imaging Reporting and Data System (BI-RADS) presents significant variability, posing challenges in clinical diagnosis. This study investigates whether a nomogram prediction model incorporating automated breast ultrasound system (ABUS) can improve the accuracy of differentiating benign and malignant BI-RADS 4 breast nodules.

METHODS

Data were collected for a total of 257 nodules with breast nodules corresponding to BI-RADS 4 who underwent ABUS examination and for whom pathology results were obtained from January 2019 to August 2022. The participants were divided into a benign group (188 cases) and a malignant group (69 cases) using a retrospective study method. Ultrasound imaging features were recorded. Logistic regression analysis was used to screen the clinical and ultrasound characteristics. Using the results of these analyses, a nomogram prediction model was established accordingly.

RESULTS

Age, distance between nodule and nipple, calcification and C-plane convergence sign were independent risk factors that enabled differentiation between benign and malignant breast nodules (all P < 0.05). A nomogram model was established based on these variables. The area under curve (AUC) values for the nomogram model, age, distance between nodule and nipple, calcification, and C-plane convergence sign were 0.86, 0.735, 0.645, 0.697, and 0.685, respectively. Thus, the AUC value for the model was significantly higher than a single variable.

CONCLUSIONS

A nomogram based on the clinical and ultrasound imaging features of ABUS can be used to improve the accuracy of the diagnosis of benign and malignant BI-RADS 4 nodules. It can function as a relatively accurate predictive tool for sonographers and clinicians and is therefore clinically useful. ADVANCES IN KNOWLEDGE STATEMENT: we retrospectively analyzed the clinical and ultrasound characteristics of ABUS BI-RADS 4 nodules and established a nomogram model to improve the efficiency of the majority of ABUS readers in the diagnosis of BI-RADS 4 nodules.

摘要

目的

根据乳腺影像报告和数据系统(BI-RADS)将乳腺恶性结节分为四类存在显著差异,给临床诊断带来挑战。本研究调查纳入自动乳腺超声系统(ABUS)的列线图预测模型能否提高鉴别BI-RADS 4类乳腺良恶性结节的准确性。

方法

收集2019年1月至2022年8月期间接受ABUS检查且有病理结果的共257例BI-RADS 4类乳腺结节患者的数据。采用回顾性研究方法将参与者分为良性组(188例)和恶性组(69例)。记录超声成像特征。采用逻辑回归分析筛选临床和超声特征。根据这些分析结果建立列线图预测模型。

结果

年龄、结节与乳头距离、钙化及C平面汇聚征是鉴别乳腺良恶性结节的独立危险因素(均P<0.05)。基于这些变量建立了列线图模型。列线图模型、年龄、结节与乳头距离、钙化及C平面汇聚征的曲线下面积(AUC)值分别为0.86、0.735、0.645、0.697和0.685。因此,模型的AUC值显著高于单一变量。

结论

基于ABUS临床和超声成像特征的列线图可用于提高BI-RADS 4类结节良恶性诊断的准确性。它可作为超声检查人员和临床医生相对准确的预测工具,因此具有临床应用价值。知识进展声明:我们回顾性分析了ABUS BI-RADS 4类结节的临床和超声特征,并建立了列线图模型,以提高大多数ABUS阅片者对BI-RADS 4类结节的诊断效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbc9/11829536/1d0834b1acf9/12880_2025_1580_Fig1_HTML.jpg

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