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

IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2025-02-14 DOI:10.1186/s12880-025-01580-w
Xianping Jiang, Chen Chen, Jincao Yao, Liping Wang, Chen Yang, Wei Li, Di Ou, Zhiyan Jin, Yuanzhen Liu, Chanjuan Peng, Yifan Wang, Dong Xu
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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.

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三维体积超声诊断BI-RADS 4型乳腺结节的影像学研究。
目的:乳腺影像学报告和数据系统(BI-RADS)将乳腺恶性结节分为四类,其差异很大,给临床诊断带来了挑战。本研究探讨结合自动乳腺超声系统(ABUS)的nomogram预测模型是否能提高BI-RADS 4乳腺结节良恶性鉴别的准确性。方法:收集2019年1月至2022年8月行ABUS检查并获得病理结果的257例BI-RADS 4对应乳腺结节的数据。采用回顾性研究方法将患者分为良性组(188例)和恶性组(69例)。记录超声影像特征。采用Logistic回归分析筛选临床及超声特征。根据这些分析结果,建立了相应的nomogram预测模型。结果:年龄、结节与乳头的距离、钙化、c平面会聚征象是区分乳腺结节良恶性的独立危险因素(均P)。结论:基于ABUS的临床及超声影像特征的影像学检查可提高BI-RADS 4型结节良恶性诊断的准确性。它可以作为超声医师和临床医生相对准确的预测工具,因此在临床上是有用的。知识进展声明:我们回顾性分析了ABUS BI-RADS 4型结节的临床和超声特征,并建立了一个nomogram模型,以提高大多数ABUS阅读器对BI-RADS 4型结节的诊断效率。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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