超声黏弹性成像在乳腺肿瘤良恶性鉴别中的研究

摘要/Abstract

摘要：

目的：探讨超声黏弹性成像技术在乳腺肿瘤良恶性鉴别中的应用价值。方法：连续纳入2023年2月至2023年8月期间，开云网页登录医学院附属瑞金医院收治的经手术病理证实为乳腺肿瘤的717例患者，其中471例为恶性，246例为良性。所有患者均在治疗前进行乳腺超声检查，包括灰阶超声、超声应变弹性成像、超声剪切波弹性成像、超声黏性成像。超声黏弹性成像技术包括测量肿瘤及其周围组织的黏性系数、频散系数和剪切波弹性模量、应变比等4组参数，以4组参数中的较佳预测指标，分别构建多种预测模型，包括黏性系数单变量模型、频散系数单变量模型、黏性组合模型（Shell/T-Vmean+Shell/T-Dmean）、剪切波单变量模型、应变单变量模型、乳腺影像报告和数据系统（Breast Imaging Reporting and Data System， BI-RADS）、BI-RADS联合黏性组合模型，评估每种模型在乳腺肿瘤良恶性鉴别中的效能。结果：超声黏弹性成像的黏性系数、频散系数、弹性模量及应变比等参数均可有效地区分乳腺良恶性肿瘤，其中肿瘤边缘2 mm区域与瘤体的参数比值Shell/T-Vmean、Shell/T-Dmean、Shell/T-Emean、Strain Ratio A为较佳预测指标，曲线下面积分别为0.742、0.745、0.726、0.705，而BI-RADS模型预测乳腺肿瘤良恶性的0.822。将Shell/T-Vmean、Shell/T-Dmean分别与BI-RADS分类联合时，受试者操作特征曲线的曲线下面积高达0.895（95%CI为0.868～0.917），高于BI-RADS。结论：超声黏弹性成像的黏弹性参数中，肿瘤边缘2 mm区域与瘤体的黏性系数、频散系数及弹性模量均值比为关键诊断指标；Shell/T-Vmean、Shell/T-Dmean联合BI-RADS后，可为术前无创精准诊断提供了新策略。

关键词:超声黏弹性成像,乳腺肿瘤,良恶性鉴别,黏性系数,频散系数

Abstract:

ObjectiveTo evaluate the application value of ultrasonic viscoelastic imaging technology in differentia-ting benign and malignant breast tumors.MethodsA total of 717 patients with breast tumors confirmed by surgical patho-logy were consecutively enrolled at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine between February 2023 and August 2023, including 471 malignant and 246 benign cases. All patients underwent breast ultrasound examinations before treatment, including grayscale ultrasound, ultrasound strain elastography, ultrasound shear wave elastography, and ultrasound viscoelastic imaging. Ultrasound viscoelastic imaging technology included measuring four sets of parameters of the tumor and its surrounding tissues: viscosity coefficient, dispersion coefficient, shear wave elastic modulus, and strain ratio. Using the optimal predictive indicators from the four parameter groups, multiple prediction models were established, including single-variable models (viscosity coefficient, dispersion coefficient, shear wave, strain), a combined viscoelastic model (Shell/T-Vmean + Shell/T-Dmean), a Breast Imaging Reporting and Data System (BI-RADS) model, and a combined model integrating BI-RADS with viscoelastic parameters. The effectiveness of each model in differentiating benign and malignant breast tumors was evaluated.ResultsParameters including viscosity coefficient, dispersion coefficient, elastic modulus, and strain ratio from ultrasound viscoelastic imaging could effectively distinguish benign and malignant breast tumors. Among them, the ratios of the tumor margin (2 mm region) to the tumor itself—Shell/T-Vmean, Shell/T-Dmean, Shell/T-Emean, and Strain Ratio A—were optimal predictive indicators, with areas under the curve (AUCs) of 0.742, 0.745, 0.726, and 0.705, respectively. The BI-RADS model for predicting benign and malignant breast tumors achieved an AUC of 0.822. When Shell/T-Vmean and Shell/T-Dmean were respectively combined with BI-RADS classification, the receiver operating characteristic (ROC) curve's AUC reached 0.895 (95% CI: 0.868–0.917), which was higher than that of BI-RADS alone.ConclusionAmong the viscoelastic parameters of ultrasound viscoelastic imaging, the average ratios of viscosity coefficient, dispersion coefficient, and elastic modulus between the 2 mm tumor margin region and the tumor body are key diagnostic indicators. The combination of Shell/T-Vmean and Shell/T-Dmean with BI-RADS provides a new stra-tegy for noninvasive preoperative precision diagnosis.

Key words:Ultrasound viscoelastic imaging,Breast tumor,Benign and malignant differentiation,Viscosity coefficient,Dispersion coefficient

中图分类号:

R737.9

覃雨, 李程, 华晴, 张慧婷, 贾宛儒, 董屹婕, 周建桥, 夏蜀珺. 超声黏弹性成像在乳腺肿瘤良恶性鉴别中的研究[J]. 诊断学理论与实践, 2025, 24(02): 194-203.

QIN Yu, LI Cheng, HUA Qing, ZHANG Huiting, JIA Wanru, DONG Yijie, ZHOU Jianqiao, XIA Shujun. Ultrasound viscoelastic imaging in differentiation of benign and malignant breast tumors[J]. Journal of Diagnostics Concepts & Practice, 2025, 24(02): 194-203.

图/表9

图1

图2

图3

表 1

717例病例的病理结果

Item	Number (%)
Benign/Malignant
Malignant	471	(65.69)
Benign	246	(34.31)
Pathological type
Adenosis	64	(8.93)
Fibroadenoma	93	(12.97)
Intraductal papilloma	52	(7.25)
Other benign lesions	33	（4.60)
Borderline tumor	6	（0.84)
Ductal carcinoma in situ	55	(7.67)
Lobular carcinoma in situ	8	(1.12)
Invasive papillary carcinoma	17	(2.37)
Invasive ductal carcinoma	334	(46.58)
Invasive lobular carcinoma	15	(2.09)
Neuroendocrine tumor	2	(0.28)
Mucinous carcinoma	13	(1.81)
Mucinous carcinoma	25	(3.49)

表 1

表2

超声黏性、弹性变量对乳腺肿瘤良恶性的单因素分析

Item	Total (N= 717)	Benign(n= 246)	Malignant(n= 471)	P-value
Age	52.49±14.05	45.00 ± 13.17	56.40 ± 12.86	<0.001
BI-RADS				<0.001
4B and above	516(71.97%)	73(29.67%)	443(94.06%)
Below 4B	201(28.03%)	173(70.33%)	28(5.94%)
T-Emean	25.93±14.65	23.12±13.30	27.40±15.12	<0.001
T-Emax	126.63±84.63	90.34± 64.77	145.58±87.61	<0.001
T-Esd	16.83 ± 11.33	12.83 ± 8.56	18.92 ±12.02	<0.001
Shell-Emean	30.25 ±16.93	23.36 ±13.24	33.85 ± 17.53	<0.001
Shell-Emax	139.84±85.38	98.11± 66.43	161.63±86.12	<0.001
Shell-Esd	21.26 ± 13.57	15.18± 10.11	24.44 ± 14.06	<0.001
A-Emean	27.74 ± 14.78	23.41± 12.85	30.00 ± 15.22	<0.001
A-Emax	151.54±90.95	107.56±72.52	174.50±91.20	<0.001
A-Esd	19.58 ± 12.02	14.56 ± 9.28	22.20 ± 12.46	<0.001
Shell/T-Emean	1.21 ± 0.33	1.05 ± 0.27	1.29 ± 0.33	<0.001
Shell/T-Emax	1.24 ± 0.59	1.22 ± 0.64	1.24 ± 0.56	0.400
Shell/T-Esd	1.37 ± 0.53	1.27 ± 0.55	1.42 ± 0.52	<0.001
T-Vmean	1.74 ± 0.89	1.77 ± 0.87	1.72 ± 0.90	0.400
T-Vmax	8.15 ± 4.78	6.60 ± 3.92	8.95 ± 4.99	<0.001
T-Vsd	1.15±0.71	1.02 ± 0.61	1.22 ± 0.75	<0.001
Shell-Vmean	2.07±1.00	1.80 ± 0.85	2.21 ± 1.04	<0.001
Shell-Vmax	8.97±4.77	7.10 ± 4.00	9.95 ± 4.85	<0.001
Shell-Vsd	1.46±0.85	1.18 ± 0.69	1.60 ± 0.90	<0.001

表2

图4

Model	AIC	AUC	95%CI
Shell/T-Vmean+ Shell/T-Dmean Combined model	788.58	0.755	0.718-0.789
Shell/T-Vmean Univariate model	804.32	0.742	0.704-0.775
Shell/T-Dmean Univariate model	800.82	0.745	0.707-0.780

表3

图 5

Model	AIC	AUC	95%CI
BI-RADS combined with viscoelastic model	546.862	0.895	0.868-0.917
BI-RADS model	586.959	0.822	0.790-0.853
Viscoelastic combined model	788.584	0.755	0.718-0.789
Shear wave univariate model	830.377	0.726	0.685-0.764
Strain univariate model	848.274	0.705	0.663-0.744

表4

参考文献26

[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 Cancer J Clin,2021,71(3): 209-249.
[2]	CHEN W, ZHENG R, BAADE P D, et al. Cancer statistics in China, 2015[J].CA Cancer J Clin,2016,66(2): 115-132.
[3]	FELDMANN A, LANGLOIS C, DEWAILLY M, et al. Shear wave elastography (SWE): an analysis of breast lesion characterization in 83 breast lesions[J].Ultrasound Med Biol,2015,41(10): 2594-2604. doi:10.1016/j.ultrasmedbio.2015.05.019pmid:26159068
[4]	RICCI P, MAGGINI E, MANCUSO E, et al. Clinical application of breast elastography: state of the art[J].Eur J Radiol,2014,83(3): 429-37. doi:10.1016/j.ejrad.2013.05.007pmid:23787274
[5]	SIGRIST R M S, LIAU J, KAFFAS A E, et al. Ultrasound elastography: review of techniques and clinical applications[J].Theranostics,2017,7(5): 1303-1329. doi:10.7150/thno.18650pmid:28435467
[6]	SHIINA T, NIGHTINGALE K R, PALMERI M L, et al. WFUMB guidelines and recommendations for clinical use of ultrasound elastography: Part 1: basic principles and terminology[J].Ultrasound Med Biol,2015,41(5):1126-1147. doi:10.1016/j.ultrasmedbio.2015.03.009pmid:25805059
[7]	KUMAR V, DENIS M, GREGORY A, et al. Viscoelastic parameters as discriminators of breast masses: Initial human study results[J].PLoS One,2018,13(10): e0205717.
[8]	LI W, JIANG J, CAO J, et al. The value of ultrasound viscosity imaging in preoperative differential diagnosis between malignant and benign breast lesions: Preliminary clinical applications[J].Clin Hemorheol Microcirc,2025,89(1):111-122. doi:10.3233/CH-242405pmid:39911120
[9]	American College of Radiology.ACR BI-RADS atlas: breast imaging reporting and data system[M].5th ed, Reston, Virginia,2013.
[10]	JIA W, XIA S, JIA X, et al. Ultrasound Viscosity Imaging in Breast Lesions: A Multicenter Prospective Study[J].Acad Radiol,2024,31(9): 3499-510. doi:10.1016/j.acra.2024.03.017pmid:38582684
[11]	MANDUCA A, BAYLY P J, EHMAN R L, et al. MR elastography: Principles, guidelines, and terminology[J].Magn Reson Med,2021,85(5): 2377-2390. doi:10.1002/mrm.28627pmid:33296103
[12]	SHI Y, QI Y F, LAN G Y, et al. Three-dimensional MR elastography depicts liver inflammation, fibrosis, and portal hypertension in chronic hepatitis B or C[J].Radio-logy,2021,301(1): 154-162.
[13]	TAPPER E B, LOOMBA R. Noninvasive imaging biomarker assessment of liver fibrosis by elastography in NAFLD[J].Nat Rev Gastroenterol Hepatol,2018,15(5): 274-282. doi:10.1038/nrgastro.2018.10pmid:29463906
[14]	PATEL B K, SAMREEN N, ZHOU Y, et al. MR elastography of the breast: evolution of technique, case examples, and future directions[J].Clin Breast Cancer,2021,21(1): e102-e111. doi:10.1016/j.clbc.2020.08.005pmid:32900617
[15]	CHEN S, SANCHEZ W, CALLSTROM M R, et al. Assessment of liver viscoelasticity by using shear waves induced by ultrasound radiation force[J].Radiology,2013,266(3): 964-970. doi:10.1148/radiol.12120837pmid:23220900
[16]	SUGIMOTO K, MORIYASU F, OSHIRO H, et al. The role of multiparametric US of the liver for the evaluation of nonalcoholic steatohepatitis[J].Radiology,2020,296(3): 532-540. doi:10.1148/radiol.2020192665pmid:32573385
[17]	LEE D H, LEE J Y, BAE J S, et al. Shear-wave dispersion slope from US shear-wave elastography: detection of allograft damage after liver transplantation[J].Radiology,2019,293(2): 327-333. doi:10.1148/radiol.2019190064pmid:31502939
[18]	HOSSAIN M M, SELZO M R, HINSON R M, et al. Evaluating renal transplant status using viscoelastic response (VisR) Ultrasound[J].Utrasound Med Biol,2018,44(8): 1573-1584.
[19]	SADIGH G, CARLOS R C, NEAL C H, et al. Accuracy of quantitative ultrasound elastography for differentiation of malignant and benign breast abnormalities: a meta-analysis[J].Breast Cancer Res Treat,2012,134(3): 923-931.
[20]	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. doi:10.1148/radiol.11110640pmid:22282182
[21]	ZHANG H, GUO Y, ZHOU Y, et al. Fluidity and elasticity form a concise set of viscoelastic biomarkers for breast cancer diagnosis based on Kelvin-Voigt fractional derivative modeling[J].Biomech Model Mechanobiol,2020,19(6): 2163-2177.
[22]	MIERKE C T. Viscoelasticity acts as a marker for tumor extracellular matrix characteristics[J].Front Cell Dev Biol,2021,9: 785138.
[23]	ZHOU J, ZHAN W, CHANG C, et al. Breast lesions: evaluation with shear wave elastography, with special emphasis on the "stiff rim" sign[J].Radiology,2014,272(1): 63-72. doi:10.1148/radiol.14130818pmid:24661245
[24]	PARK H S, SHIN H J, SHIN K C, et al. Comparison of peritumoral stromal tissue stiffness obtained by shear wave elastography between benign and malignant breast lesions[J].Acta Radiol,2018,59(10): 1168-1175. doi:10.1177/0284185117753728pmid:29359949
[25]	王艳萍, 唐笛娇, 努尔比耶·买买提依力, 等. 血清抗着丝粒蛋白F抗体在乳腺癌中的临床价值探讨 [J].重庆医科大学学报,2024,49(9): 1188-1192.
	WANG Y P, TANG D J, NUERBIYE·M, et al. Clinical value of serum anti-centromere protein F antibody in breast cancer[J].J Chongqing Med Univ,2024,49(9): 1188-1192.
[26]	SRIDHAR M, INSANA M F. Ultrasonic measurements of breast viscoelasticity[J].Med Phys,2007,34(12): 4757-4767. pmid:18196803