宜宾市第六人民医院;川北医学院附属医院;
目的：基于多模态超声技术分析乳腺癌的独立危险因素，构建乳腺结节恶性风险列线图预测模型，评估模型的预测价值，指导临床制定更有效的乳腺癌筛查策略。方法：前瞻性收集2021年5月至2023年12月川北医学院附属医院超声BI-RADS分类3～5类且均经病理证实的乳腺结节患者228例(共230个结节),其中良性组146个，恶性组84个。通过单因素分析筛选指标，绘制受试者操作特征(ROC)曲线并计算曲线下面积(AUC)评价各指标的诊断效能。将表现较好的指标纳入多因素Logistic回归并构建乳腺结节恶性风险列线图预测模型。行Bootstrap法对模型进行验证，使用一致性指数(C-index)、校正曲线和决策曲线评价模型的预测效能和临床获益率。结果：单因素初筛显示，血流参数中AP分型与RI对乳腺癌的诊断效能较高，与CDFI分级、AP分级、PSV的AUC两两比较，差异均有统计学意义(均P<0.05)。SWE相关参数中Emax对乳腺癌的诊断效能最好，与SWE分型、Emean的AUC比较，差异均具有统计学意义(Z=2.742、3.174,均P<0.05)。多因素分析显示，年龄、BI-RADS、AP分型、RI、Emax可作为独立危险因素纳入乳腺癌预测列线图模型的构建。模型的C-index为0.996,对模型行内部验证后校正的C-index为0.992。校正曲线显示，模型的预测概率与实际概率之间存在良好的一致性。决策曲线显示模型的阈概率为0～1.0时，患者可获得正的净收益。结论：基于年龄、BI-RADS、RI、AP分型、Emax构建的多模态超声列线图模型可有效预测乳腺结节良恶性，具有良好的临床应用价值。多模态超声列线图的可视化展现有助于指导临床制定更合理的乳腺癌筛查策略，便于乳腺癌的早发现、早诊断，提高患者的生存率和生活质量。
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[1] Sung H,Ferlay J,Siegel R L,et al.Global cancer statistics 2020:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J].CA:A Cancer Journal for Clinicians,2021,71(3):209-249.
[2] Lei S Y,Zheng R S,Zhang S W,et al.Breast cancer incidence and mortality in women in China:Temporal trends and projections to 2030[J].Cancer Biollogy & Medicine,2021,18(3):900-909.
[3] 代妮娜，张文君.高频超声对乳腺微小结节良恶性鉴别诊断意义[J].影像科学与光化学，2022,40(2):414-417.
[4] 周建桥，詹维伟.超声乳腺影像报告数据系统及其解读[J].中华医学超声杂志(电子版),2011,8(6):1332-1341.
[5] Lee Y J,Choi S Y,Kim K S,et al.Variability in observer performance between faculty members and residents using breast imaging reporting and data system (BI-RADS)-ultrasound,fifth edition(2013)[J].Iranian Journal of Radiology:a Quarterly Journal Published by the Iranian Radiological Society,2016,13(3):e28281.
[6] Wen W,Liu J Y,Wang J R,et al.A national Chinese survey on ultrasound feature interpretation and risk assessment of breast masses under ACR BI-RADS[J].Cancer Management and Research,2021,13:9107-9115.
[7] Mango V L,Sun M,Wynn R T,et al.Should we ignore,follow,or biopsy?Impact of artificial intelligence decision support on breast ultrasound lesion assessment[J].AJR:American Journal of Roentgenology,2020,214(6):1445-1452.
[8] Lyu S Y,Zhang Y,Zhang M W,et al.Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules[J].World Journal of Clinical Cases,2022,10(2):518-527.
[9] Barr R G,Nakashima K,Amy D,et al.WFUMB guidelines and recommendations for clinical use of ultrasound elastography:Part 2:Breast[J].Ultrasound in Medicine & Biology,2015,41(5):1148-1160.
[10] Park A,Seo B,Han M.Breast ultrasound microvascular imaging and radiogenomics[J].Korean Journal of Radiology,2021,22(5):677-687.
[11] 谭雪艳，岳文胜，陈思思，等.健康成人上肢肱动脉-指端微循环的血流动力学梯度变化研究[J].中华医学超声杂志(电子版),2021,18(2):193-198.
[12] Pillai A,Voruganti T,Barr R,et al.Diagnostic accuracy of shear-wave elastography for breast lesion characterization in women:A systematic review and Meta-analysis[J].Journal of the American College of Radiology:JACR,2022,19(5):625-634.e0.
[13] Fujioka T,Mori M,Kubota K,et al.Simultaneous comparison between strain and shear wave elastography of breast masses for the differentiation of benign and malignant lesions by qualitative and quantitative assessments[J].Breast Cancer,2019,26(6):792-798.
[14] Eghtedari M,Chong A,Rakow-Penner R,et al.Current status and future of BI-RADS in multimodality imaging,from the AJR special series on radiology reporting and data dystems[J].AJR:American Journal of Roentgenology,2021,216(4):860-873.
[15] Adler D D,Carson P L,Rubin J M,et al.Doppler ultrasound color flow imaging in the study of breast cancer:Preliminary findings[J].Ultrasound in Medicine & Biology,1990,16(6):553-559.
[16] 陈欣，罗葆明，管小凤，等.超微血管成像与超声造影微血管成像在乳腺病变诊断中的对比研究[J].中华超声影像学杂志，2026,25(7):608-611.
[17] Tozaki M,Fukuma E.Pattern classification of ShearWave?Elastography images for differential diagnosis between benign and malignant solid breast masses[J].Acta Radiologica,2011,52(10):1069-1075.
[18] Spak D A,Plaxco J S,Santiago L,et al.BI-RADS? fifth edition:A summary of changes[J].Diagnostic and Interventional Imaging,2017,98(3):179-190.
[19] Bartolotta T V,Orlando A A M,Spatafora L,et al.S-Detect characterization of focal breast lesions according to the US BI RADS lexicon:A pictorial essay[J].Journal of Ultrasound,2020,23(2):207-215.
[20] Elverici E,Zengin B,Nurdan Barca A,et al.Interobserver and intraobserver agreement of sonographic BIRADS lexicon in the assessment of breast masses[J].Iranian Journal of Radiology:A Quarterly Journal Published by the Iranian Radiological Society,2013,10(3):122-127.
[21] 邱霜.基于DE-Light框架的人工智能诊断系统在乳腺超声中的应用研究[D].成都：电子科技大学，2022.
[22] 杨磊，唐灿.人工智能在乳腺癌超声诊断的应用价值[J].实用医学杂志，2022,38(1):106-110.
[23] Wu J Y,Zhao Z Z,Zhang W Y,et al.Computer-aided diagnosis of solid breast lesions with ultrasound:Factors associated with false-negative and false-positive results[J].Journal of Ultrasound in Medicine:Official Journal of the American Institute of Ultrasound in Medicine,2019,38(12):3193-3202.
[24] Kim S,Lee H J,Ko K H,et al.New Doppler imaging technique for assessing angiogenesis in breast tumors:Correlation with immunohistochemically analyzed microvessels density[J].Acta Radiologica,2018,59(12):1414-1421.
[25] 左文思，金林原，刘新桥，等.微血管成像联合超声造影5分法对BI-RADS 4类乳腺肿块的诊断价值[J].中国超声医学杂志，2021,37(9):974-978.
[26] 李涛，林剑英，刘观成，等.超声剪切波联合彩色像素密度鉴别乳腺肿块的价值[J].中国超声医学杂志，2021,37(1):31-34.
[27] Zhao Y F,Zhou P,Liu W G,et al.Application of a novel microvascular imaging technique in breast lesion evaluation[J].Ultrasound in Medicine & Biology,2016,42(9):2097-2105.
[28] Berg W A,Cosgrove D O,Doré C J,et al.Shear-wave elastography improves the specificity of breast US:The BE1 multinational study of 939 masses[J].Radiology,2012,262(2):435-449.
[29] Park J,Woo O H,Shin H S,et al.Diagnostic performance and color overlay pattern in shear wave elastography(SWE)for palpable breast mass[J].European Journal of Radiology,2015,84(10):1943-1948.
[30] 孟蕾，王瑞先，张志扬，等.利用多模态超声提高乳腺BI-RADS 4类结节诊断准确率的临床应用研究[J].中国临床医学影像杂志，2023,34:701-704,709.
基本信息:
DOI：
中图分类号:R445.1;R737.9
引用信息:
[1]罗季平,官愫,岳文胜.乳腺结节恶性风险列线图预测模型的构建与验证[J].影像科学与光化学,2024,42(04):344-352.
基金信息:
川北医学院附属医院揭榜挂帅项目2022JB001