Construction of a multi-parametric ultrasonographic nomogram for precise assessment of papillary breast lesions.

IF 1.8 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yunhua Li, Hui Li, Lianfang Du, Qiusheng Shi, Gang Li, Chao Jia, Lifang Jin, Hongmei Liang, Fan Li
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引用次数: 0

Abstract

Objectives: To analyze the multi-parametric ultrasonographic (MpUS) features of atypical/malignant papillary lesions of the breast with clinical information, identify independent risk factors, and construct a nomogram to improve the diagnostic accuracy.

Methods: This retrospective study analyzed consecutively hospitalized patients diagnosed with pathologically confirmed papillary breast lesions from January 2017 to June 2023. Preoperative sonographic examinations, including gray-scale ultrasound (G-US), color Doppler flow imaging (CDFI), and contrast-enhanced ultrasound (CEUS), were conducted. Sonographic scans were retrospectively reviewed alongside clinicopathological data. Binary logistic regression identified independent risk factors for screening atypical/malignant papillary lesions. The receiver operating characteristic curve evaluated the predictive accuracy of these lesions, resulting in the development of a nomogram for assessing risk.

Results: The study involved 176 female patients with breast papillary lesions, identifying key predictors for atypical or malignant outcomes: age 57 or order, US diameter ≥13.95 mm, resistive index ≥0.70, enlarged enhancement on CEUS, and contrast agent retention, with respective odds ratios of 6.279, 8.078, 9.246, 9.401, and 5.047. The integrated use of G-US, CDFI, and CEUS in the MpUS approach offered higher diagnostic accuracy (area under the curve [AUC]: 0.966) than G-US or CDFI alone (0.869/0.918). CEUS particularly enhanced prediction for non-mass-like lesions, with a positive predictive value of 83.3%. A nomogram incorporating MpUS and patient age achieved an AUC of 0.956 for predicting atypical or malignant papillary lesions.

Conclusions: MpUS imaging is highly effective for predicting malignant breast papillary lesions, especially considering patient age. The nomogram offers an intuitive framework for assessing malignant risk in these lesions.

Advances in knowledge: Ultrasound excels in identifying papillary lesions, and integrating diverse data and multi-parametric imaging enhances malignant risk evaluation. This study establishes a predictive risk model using the nomogram method, demonstrating heightened diagnostic efficacy in breast papillary lesions.

构建用于精确评估乳腺乳头状病变的多参数超声波提名图
目的:结合临床资料分析乳腺非典型/恶性乳头状病变的多参数超声(MpUS)特征,识别独立危险因素,构建影像学图,提高诊断准确率。方法:回顾性分析2017年1月至2023年6月连续住院的经病理证实的乳腺乳头状病变患者。术前进行超声检查,包括灰度超声(G-US)、彩色多普勒血流显像(CDFI)和对比增强超声(CEUS)。回顾性回顾超声扫描和临床病理资料。二元logistic回归确定了筛查非典型/恶性乳头状病变的独立危险因素。接受者工作特征曲线评估了这些病变的预测准确性,从而形成了用于评估风险的nomogram。结果:该研究纳入176例女性乳腺乳头状病变患者,确定了非典型或恶性结局的关键预测因素:年龄57岁或顺序,超声造影直径≥13.95 mm,阻性指数≥0.70,超声造影增强放大和造影剂潴留,比值比分别为6.279,8.078,9.246,9.401和5.047。综合使用G-US、CDFI和CEUS在MpUS方法中的诊断准确率(AUC: 0.966)高于单独使用G-US或CDFI(0.869/0.918)。超声造影特别增强了对非肿块样病变的预测,阳性预测值为83.3%。结合MpUS和患者年龄的nomogram预测非典型或恶性乳头状病变的AUC为0.956。结论:MpUS对乳腺乳头状恶性病变的预测是非常有效的,特别是考虑到患者的年龄。图为评估这些病变的恶性风险提供了一个直观的框架。知识进步:超声在识别乳头状病变方面具有优势,整合多种数据和多参数成像增强了恶性风险评估。本研究使用nomogram方法建立了一个预测风险模型,显示了对乳腺乳头状病变的更高诊断效能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
British Journal of Radiology
British Journal of Radiology 医学-核医学
CiteScore
5.30
自引率
3.80%
发文量
330
审稿时长
2-4 weeks
期刊介绍: BJR is the international research journal of the British Institute of Radiology and is the oldest scientific journal in the field of radiology and related sciences. Dating back to 1896, BJR’s history is radiology’s history, and the journal has featured some landmark papers such as the first description of Computed Tomography "Computerized transverse axial tomography" by Godfrey Hounsfield in 1973. A valuable historical resource, the complete BJR archive has been digitized from 1896. Quick Facts: - 2015 Impact Factor – 1.840 - Receipt to first decision – average of 6 weeks - Acceptance to online publication – average of 3 weeks - ISSN: 0007-1285 - eISSN: 1748-880X Open Access option
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