常规超声联合剪切波弹性成像鉴别非肿块型导管原位癌和浸润性乳腺癌的价值

摘要/Abstract

摘要：

目的探讨常规超声联合剪切波弹性成像（SWE）鉴别诊断非肿块型导管原位癌（DCIS）和浸润性乳腺癌（IBC）的价值。方法选取2019年3月至2022年4月在南京大学医学院附属鼓楼医院收治的102例非肿块型乳腺癌患者作为研究对象，其中DCIS 32例、IBC 70例。比较非肿块型DCIS、IBC患者常规超声参数回声、微钙化、方位、后方回声、血流、腋窝淋巴结、乳腺影像报告与数据系统（BI-RADS）评分及SWE相关参数剪切波速度最大值（SWV_max）、剪切波速度最小值（SWV_min）、剪切波速度平均值（SWV_mean）和剪切波速度中位数（SWV_median），采用二元logistic回归分析鉴别诊断非肿块型DCIS和IBC的独立影响因素。根据多因素分析结果构建列线图预测模型，采用受试者操作特征（ROC）曲线评估预测模型的预测效能，采用校准曲线及决策曲线分析（DCA）评估模型的准确性和实用性。结果非肿块型DCIS、IBC患者间血流（χ²=8.47，P=0.004）、腋窝淋巴结（χ²=9.11，P=0.003）、SWV_max（Z=-3.32，P<0.001）、SWV_mean（t=3.00，P=0.003）、SWV_median（Z=-2.69，P=0.007）差异均有统计学意义。多因素分析显示，血流（OR=3.56，95%CI为1.28~9.89，P=0.015）、腋窝淋巴结（OR=3.04，95%CI为1.10~8.42，P=0.032）和SWV_max（OR=1.40，95%CI为1.13~1.73，P=0.002）均为鉴别诊断非肿块型DCIS和IBC的独立影响因素。基于血流、腋窝淋巴结、SWV_max构建列线图预测模型。ROC曲线分析显示，血流、腋窝淋巴结、SWV_max、预测模型鉴别诊断非肿块型DCIS和IBC的曲线下面积分别为0.64（95%CI为0.52~0.76）、0.66（95%CI为0.55~0.77）、0.71（95%CI为0.60~0.81）、0.79（95%CI为0.70~0.88），预测模型的鉴别诊断价值高于血流（Z=2.92，P=0.004）、腋窝淋巴结（Z=2.94，P=0.003）、SWV_max（Z=1.88，P=0.060）单独诊断。预测模型鉴别诊断非肿块型DCIS和IBC的C-index为0.77，校准曲线显示模型的预测概率接近实际概率。DCA表明，该模型能提供较高的临床净获益，具有一定的临床实用性。结论常规超声参数中的血流、腋窝淋巴结和SWE相关参数的SWV_max均为鉴别诊断非肿块型DCIS和IBC的独立影响因素，以此构建的列线图预测模型对非肿块型DCIS和IBC具有较高的鉴别诊断价值。

关键词:乳腺肿瘤,癌，导管内，非浸润性,超声检查,弹性成像技术,浸润乳腺癌

Abstract:

ObjectiveTo investigate the value of conventional ultrasound combined with shear wave elastography （SWE） in the differential diagnosis of non-mass ductal carcinomain situ（DCIS） and invasive breast cancer （IBC）.MethodsA total of 102 patients with non-mass breast cancer admitted to Nanjing Drum Tower Hospital， Affiliated Hospital of Nanjing University Medical School from March 2019 to April 2022 were selected as the study objects， including 32 cases of DCIS and 70 cases of IBC. Conventional ultrasound parameters echo， microcalcification， location， posterior echo， blood flow， axillary lymph node， breast imaging reporting and data system （BI-RADS） score and SWE-related parameters maximum shear wave velocity （SWV_max）， minimum shear wave velocity （SWV_min）， mean shear wave velocity （SWV_mean） and median shear wave velocity （SWV_median） were compared between patients with non-mass DCIS and IBC. Binary logistic regression was used to analyze the independent factors for the differential diagnosis of non-mass DCIS and IBC. Based on the results of multivariate analysis， a nomogram prediction model was constructed and the predictive efficacy of the prediction model was evaluated by receiver operator characteristic （ROC） curve. Calibration curve and decision curve analysis （DCA） were used to evaluate the accuracy and practicability of the model.ResultsThere were statistically significant differences in blood flow （χ²=8.47，P=0.004）， axillary lymph nodes （χ²=9.11，P=0.003）， SWV_max（Z=-3.32，P<0.001）， SWV_mean（t=3.00，P=0.003）， SWV_median（Z=-2.69，P=0.007） between patients with non-mass DCIS and IBC. Multivariate analysis showed that， blood flow （OR=3.56， 95%CI： 1.28-9.89，P=0.015）， axillary lymph nodes （OR=3.04， 95%CI： 1.10-8.42，P=0.032） and SWV_max（OR=1.40， 95%CI： 1.13-1.73，P=0.002） were independent factors for distinguishing non-mass DCIS from IBC. A nomogram prediction model was constructed based on blood flow， axillary lymph nodes and SWV_max. ROC curve analysis showed that， the area under the curve of blood flow， axillary lymph nodes， SWV_max， and prediction model for differential diagnosis of non-mass DCIS and IBC were 0.64 （95%CI： 0.52-0.76）， 0.66 （95%CI： 0.55-0.77）， 0.71 （95%CI： 0.60-0.81）， and 0.79 （95%CI： 0.70-0.88）， respectively， and the differential diagnostic value of prediction model was higher than that of blood flow （Z=2.92，P=0.004）， axillary lymph nodes （Z=2.94，P=0.003）， and SWV_max（Z=1.88，P=0.060） alone. The C-index of the prediction model for the differential diagnosis of non-mass DCIS and IBC was 0.77， and the calibration curve showed that the prediction probability of the prediction model was close to the actual probability. DCA showed that this prediction model could provide higher clinical net benefit and had certain clinical practicability.ConclusionBlood flow and axillary lymph nodes in conventional ultrasound parameters and SWV_maxof SWE-related parameters are independent factors in the differential diagnosis of non-mass DCIS and IBC. The nomogram prediction model constructed by this method has a high value in the differential diagnosis of non-mass DCIS and IBC.

Key words:Breast neoplasms,Carcinoma， intraductal， noninfiltrating,Ultrasonography,Elasticity imaging techniques,Invasive breast cancer

檀双秀, 张一丹, 王颖, 于鹏丽, 孔文韬, 姚静, 陈桥梁. 常规超声联合剪切波弹性成像鉴别非肿块型导管原位癌和浸润性乳腺癌的价值[J]. 国际肿瘤学杂志, 2024, 51(12): 743-748.

Tan Shuangxiu, Zhang Yidan, Wang Ying, Yu Pengli, Kong Wentao, Yao Jing, Chen Qiaoliang. Value of conventional ultrasound combined with shear wave elastography in differentiating non-mass ductal carcinomain situfrom invasive breast cancer[J]. Journal of International Oncology, 2024, 51(12): 743-748.

图/表8

图1

图2

表1

非肿块型DCIS和IBC患者的年龄、常规超声参数及SWE相关参数比较[$\bar{x} \pm s$/例/M（Q1，Q3）]"

项目	DCIS（n=32）	IBC（n=70）	t/χ²/Z值	P值
年龄（岁）	51.41±9.76	50.86±10.99	-0.24	0.809
回声
低回声	29	66	-	0.675
等、高、混合回声	3	4	-	0.675
微钙化
有	18	49	1.84	0.175
无	14	21	1.84	0.175
方位
垂直	1	3	-	>0.999
平行	31	67	-	>0.999
后方回声
无变化	29	55	2.20	0.138
衰减	3	15	2.20	0.138
血流
0~Ⅰ级	16	15	8.47	0.004
Ⅱ~Ⅲ级	16	55	8.47	0.004
腋窝淋巴结
阳性	8	40	9.11	0.003
阴性	24	30	9.11	0.003
BI-RADS评分（分）
4A	8	13	4.72	0.194
4B	12	17
4C	11	30
5	1	10
SWV_max（m/s）	6.01（3.95，7.90）	7.95（6.25，9.62）	-3.32	<0.001
SWV_min（m/s）	2.87（2.26，3.89）	2.97（2.24，3.74）	-0.41	0.684
SWV_mean（m/s）	4.26±1.34	5.14±1.39	3.00	0.003
SWV_median（m/s）	3.86（2.88，5.12）	4.81（4.02，5.71）	-2.69	0.007

表1

因素	β值	SE值	Waldχ²值	OR值	95%CI	P值
血流	1.27	0.52	5.91	3.56	1.28~9.89	0.015
腋窝淋巴结	1.11	0.52	4.58	3.04	1.10~8.42	0.032
SWV_max	0.33	0.11	9.35	1.40	1.13~1.73	0.002

表2

图3

图4

图5

图6

参考文献19

[1]	Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023[J].CA Cancer J Clin,2023,73(1): 17-48. DOI:10.3322/caac.21763.
[2]	段帅, 郭晨明, 李慧芳, 等. 1990—2019年中国女性乳腺癌发病死亡趋势及年龄-时期-队列模型分析[J].国际肿瘤学杂志,2022,49(10): 586-591. DOI:10.3760/cma.j.cn371439-20220726-00116.
[3]	Wang X, Jing L, Yan L, et al. A conditional inference tree model for predicting cancer risk of non-mass lesions detected on breast ultrasound[J].Eur Radiol,2024,34(7): 4776-4788. DOI:10.1007/s00330-023-10504-7.
[4]	Golatta M, Pfob A, Büsch C, et al. The potential of combined shear wave and strain elastography to reduce unnecessary biopsies in breast cancer diagnostics—an international, multicentre trial[J].Eur J Cancer,2022,161: 1-9. DOI:10.1016/j.ejca.2021.11.005.
[5]	Qu XX, Song Y, Zhang YH, et al. Value of ultrasonic elastography and conventional ultrasonography in the differential diagnosis of non-mass-like breast lesions[J].Ultrasound Med Biol,2019,45(6): 1358-1366. DOI:10.1016/j.ultrasmedbio.2019.01.020.
[6]	于鹏丽, 孔文韬, 薛海燕, 等. 声触诊组织成像量化技术鉴别诊断非肿块型乳腺病变良恶性的临床价值[J].肿瘤影像学,2022,31(6): 575-580. DOI:10.19732/j.cnki.2096-6210.2022.06.003.
[7]	Mannu GS, Wang Z, Broggio J, et al. Invasive breast cancer and breast cancer mortality after ductal carcinoma in situ in women attending for breast screening in England, 1988—2014: population based observational cohort study[J].BMJ,2020,369: m1570. DOI:10.1136/bmj.m1570.
[8]	Marino MA, Avendano D, Zapata P, et al. Lymph node imaging in patients with primary breast cancer: concurrent diagnostic tools[J].Oncologist,2020,25(2): e231-e242. DOI:10.1634/theoncologist.2019-0427.
[9]	Park KW, Park S, Shon I, et al. Non-mass lesions detected by breast US: stratification of cancer risk for clinical management[J].Eur Radiol,2021,31(3): 1693-1706. DOI:10.1007/s00330-020-07168-y.
[10]	Guo W, Wang T, Li F, et al. Non-mass breast lesions: could multimodal ultrasound imaging be helpful for their diagnosis?[J].Diagnostics (Basel),2022,12(12): 2923. DOI:10.3390/diagnostics12122923.
[11]	Zhang F, Wang J, Jin L, et al. Comparison of the diagnostic value of contrast-enhanced ultrasound combined with conventional ultrasound versus magnetic resonance imaging in malignant non-mass breast lesions[J].Br J Radiol,2023,96(1150): 20220880. DOI:10.1259/bjr.20220880.
[12]	Cutuli B. Ductal carcinoma in situ in 2019: diagnosis, treatment, prognosis[J].Presse Med,2019,48(10): 1112-1122. DOI:10.1016/j.lpm.2019.08.018.
[13]	Badve SS, Gökmen-Polar Y. Ductal carcinoma in situ of breast: update 2019[J].Pathology,2019,51(6): 563-569. DOI:10.1016/j.pathol.2019.07.005.
[14]	Gradishar WJ, Moran MS, Abraham J, et al. Breast cancer, version 3.2022, NCCN clinical practice guidelines in oncology[J].J Natl Compr Canc Netw,2022,20(6): 691-722. DOI:10.6004/jnccn.2022.0030.
[15]	中国抗癌协会乳腺癌专业委员会, 中华医学会肿瘤学分会乳腺肿瘤学组. 中国抗癌协会乳腺癌诊治指南与规范(2024年版)[J].中国癌症杂志,2023,33(12): 1092-1186. DOI:10.19401/j.cnki.1007-3639.2023.12.004.
[16]	Luo R, Zhang Y, Jiang W, et al. Value of micro-flow imaging and high-definition micro-flow imaging in differentiating malignant and benign breast lesions[J].Clin Radiol,2024,79(1): e48-e56. DOI:10.1016/j.crad.2023.10.007.
[17]	Bartolotta TV, Orlando AAM, Schillaci MI, et al. Ultrasonographic detection of vascularity of focal breast lesions: microvascular imaging versus conventional color and power doppler imaging[J].Ultrason Imaging,2021,43(5): 273-281. DOI:10.1177/01617346211029542.
[18]	Aladag Kurt S, Kayadibi Y, Onur I, et al. Predicting axillary nodal metastasis based on the side of asymmetrical cortical thickening in breast cancer: evaluation with grayscale and microvascular imaging findings[J].Eur J Radiol,2023,158: 110643. DOI:10.1016/j.ejrad.2022.110643.
[19]	Zhang P, Kong P, Liu D, et al. Non-mass-type ductal carcinoma in situ of the breast on ultrasound: features and pathological analysis[J].J Cancer Res Ther,2024,20(2): 665-668. DOI:10.4103/jcrt.jcrt_2251_23. pmid:38687938