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基于多模态超声特征的乳腺非肿块病变评估列线图:一项单中心研究。

Nomogram based on multimodal ultrasound features for evaluating breast nonmass lesions: a single center study.

机构信息

Department of Ultrasound, Hangzhou First People's Hospital, Hangzhou, China.

Ultrasonography Department, Hangzhou First People's Hospital, No. 261 Huansha Road, Hangzhou, Zhejiang Province, 310006, China.

出版信息

BMC Med Imaging. 2024 Oct 21;24(1):282. doi: 10.1186/s12880-024-01462-7.

Abstract

BACKGROUND

It is challenging to correctly identify and diagnose breast nonmass lesions. This study aimed to explore the multimodal ultrasound features associated with malignant breast nonmass lesions (NMLs), and evaluate their combined diagnostic performance.

METHODS

This retrospective analysis was conducted on 573 breast NMLs, including 309 were benign and 264 were malignant, their multimodal ultrasound features (B-mode, color Doppler and strain elastography) were assessed by two experienced radiologists. Univariate and multivariate logistic regression analysises were used to explore multimodal ultrasound features associated with malignancy, and a nomogram was developed. Diagnostic performance and clinical utility were evaluated and validated by the receiver operating characteristic (ROC) curve, calibration curve and decision curve in the training and validation cohorts.

RESULTS

Multimodal ultrasound features including linear (odds ratio [OR] = 4.69) or segmental distribution (OR = 7.67), posterior shadowing (OR = 3.14), calcification (OR = 7.40), hypovascularity (OR = 0.38), elasticity scored 4 (OR = 7.00) and 5 (OR = 15.77) were independent factors associated with malignant breast NMLs. The nomogram based on these features exhibited diagnostic performance in the training and validation cohorts were comparable to that of experienced radiologists, with superior specificity (89.4%, 89.5% vs. 81.2%) and positive predictive value (PPV) (89.2%, 90.4% vs. 82.4%). The nomogram also demonstrated good calibration in both training and validation cohorts (all P > 0.05). Decision curve analysis indicated that interventions guided by the nomogram would be beneficial across a wide range of threshold probabilities (0.05-1 in the training cohort and 0.05-0.93 in the validation cohort).

CONCLUSIONS

The combined use of linear or segmental distribution, posterior shadowing, calcification, hypervascularity and high elasticity score, displayed as a nomogram, demonstrated satisfied diagnostic performance for malignant breast NMLs, which may contribute to the imaging interpretation and clinical management of tumors.

摘要

背景

正确识别和诊断乳腺非肿块性病变具有挑战性。本研究旨在探讨与恶性乳腺非肿块性病变(NML)相关的多模态超声特征,并评估其联合诊断性能。

方法

本回顾性分析纳入了 573 例乳腺 NML,其中 309 例为良性,264 例为恶性,由两位有经验的放射科医生评估其多模态超声特征(B 模式、彩色多普勒和应变弹性成像)。采用单因素和多因素逻辑回归分析探讨与恶性相关的多模态超声特征,并建立列线图。在训练和验证队列中,通过接受者操作特征(ROC)曲线、校准曲线和决策曲线评估和验证诊断性能和临床实用性。

结果

多模态超声特征包括线性(比值比[OR] = 4.69)或节段性分布(OR = 7.67)、后方声影(OR = 3.14)、钙化(OR = 7.40)、低血流(OR = 0.38)、弹性评分 4(OR = 7.00)和 5(OR = 15.77)是与恶性乳腺 NML 相关的独立因素。基于这些特征的列线图在训练和验证队列中的诊断性能与有经验的放射科医生相当,具有更高的特异性(89.4%,89.5% vs. 81.2%)和阳性预测值(PPV)(89.2%,90.4% vs. 82.4%)。列线图在训练和验证队列中均具有良好的校准度(均 P > 0.05)。决策曲线分析表明,该列线图指导的干预措施在广泛的阈值概率范围内(训练队列为 0.05-1,验证队列为 0.05-0.93)均有益。

结论

线性或节段性分布、后方声影、钙化、高血流和高弹性评分的联合使用,以列线图的形式呈现,对恶性乳腺 NML 的诊断性能令人满意,这可能有助于肿瘤的影像学解释和临床管理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2f5/11492699/c1467160b5f4/12880_2024_1462_Fig1_HTML.jpg

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