生长方位量化联合S-Detect技术对乳腺癌腋窝淋巴结转移的预测价值

doi:10.3969/j.issn.1006-5725.2025.01.017

Abstract

Abstract:

Objective To investigate the utility of combining breast mass growth orientation quantification with the S-Detect technique for predicting axillary lymph node （ALN） metastasis in breast cancer. Methods Data was collected from 163 breast cancer patients admitted to our hospital between March 2023 and October 2024， who were categorized into metastatic （n = 62） and non-metastatic （n = 101） groups based on ALN pathology results. All patients underwent routine preoperative ultrasound and S-Detect examination. Univariate and multivariate regression analyses were performed to assess the correlation between each observational index and ALN metastasis. Significant indexes were identified through screening， leading to the establishment of a logistic regression prediction model. The predictive value of the model was evaluated using receiver operating characteristic （ROC） curve analysis. Results The univariate analysis revealed statistically significant differences （P < 0.05） in the maximum diameter of the mass， border characteristics， margin features， calcification patterns， orientation angle， and blood flow between the two groups. Multifactorial analysis demonstrated that calcification， border characteristics， orientation angle， margin features， and maximum diameter independently influenced the prediction of axillary lymph node （ALN） status in breast cancer patients （P < 0.05）. Consequently， a logistic regression prediction model was constructed as follows： Y = -7.995 + 2.299 × maximal diameter + 1.171 × border + 2.137 × margin + 1.397 × calcication + 0.034 × orientation angle. The area under curve （AUC） for this combined prediction model was 0.869 which significantly outperformed each independent influencing factor alone （P < 0.05）， indicating good agreement between this joint prediction model and pathological results （Kappa = 0.701， P < 0.05）. Conclusions Quantification of the orientation angle of a breast mass aids in predicting axillary lymph node （ALN） metastasis and enhances the interpretation and application of non-parallel orientations. The combination of quantifying growth orientation based on breast mass with artificial intelligence S-Detect technique demonstrates promising predictive value for ALN metastasis in breast cancer， providing a reference basis for personalized treatment.

Key words: ultrasound, S-Detect technique, breast cancer, ALN, growth orientation, quantification

CLC Number:

R737.9

Yaqian DENG,Wenxiao LI,Zelin XU,Jinmei MA,Tingting DU,Wen LIU,Jun LI. Predictive value of growth orientation quantification combined with S⁃Detect technique for axillary lymph node metastasis in breast cancer[J]. The Journal of Practical Medicine, 2025, 41(1): 100-107.

Figures/Tables 8

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References 32

1	HYUNA S， JACQUES F， SIEGEL R L， et al.Global cancer statistics2020： Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries［J］. Cancer J Clin， 2021， 71（3）：209-249. doi:10.3322/caac.21660 doi: 10.3322/caac.21660
2	TO B， ISAAC D， ANDRECHEK E R. Studying Lymphatic Metastasis in Breast cancer： Current Models， Strategies， and Clinical Perspectives［J］.J Mammary Gland Biol Neoplasia， 2020， 25（3）：191-203. doi:10.1007/s10911-020-09460-5 doi: 10.1007/s10911-020-09460-5
3	MARINO M A， AVENDANO D， ZAPATA P， et al. Lymph node imagingin patients with primary breast cancer： Concurrent diagnostic tools［J］.Oncologist， 2020， 25（2）：e231-e242. doi:10.1634/theoncologist.2019-0427 doi: 10.1634/theoncologist.2019-0427
4	ZHU Y Y， JIA Y Y， PANG W J， et al. Ultrasound contrast-enhancedpatterns of sentinel lymph nodes： Predictive value for nodal statusand metastatic burden in early breast cancer［J］. Quant Imaging Med Surg， 2023， 13（1）：160-170. doi:10.21037/qims-22-234 doi: 10.21037/qims-22-234
5	LI J， LI H， GUAN L， et al. The value of preoperative sentinellymph node contrast-enhanced ultrasound for breast cancer： Alarge， multicenter trial［J］. BMC Cancer， 2022， 22（1）：455. doi:10.1186/s12885-022-09551-y doi: 10.1186/s12885-022-09551-y
6	HANKÓ-BAUER O， PODOLEANU C， GEORGESCU R， et al.The accuracy of the preoperative axillary ultrasound examination in predicting the status of the sentinel lymph node involvement in patients with infiltrating breast carcinoma［J］. Chirurgia（Bucur）， 2019， 114（3）：384-391. doi:10.21614/chirurgia.114.3.384 doi: 10.21614/chirurgia.114.3.384
7	BARTOLOTTA T V， ORLANDO A A M， SPATAFORA L， et al. S-Detect characterization of focal breast lesions according to the US BI RADS lexion：A pictorial essay［J］. J Ultrasound， 2020， 23 （2）：207-215. doi:10.1007/s40477-020-00447-w doi: 10.1007/s40477-020-00447-w
8	GU Y， TIAN J W， RAN H T， et al. The utility of the fifth edition of the BI-RADS ultrasound lexicon in category 4 breast lesions：A prospective multicenter study in China［J］. Acad Radiol， 2022， 29 ：S26–S34. doi:10.1016/j.acra.2020.06.027 doi: 10.1016/j.acra.2020.06.027
9	GUO Q， ZHANG L， DI Z， et al. Assessing risk category of breast cancer by ultrasound imaging characteristics［J］. Ultrasound Med Biol， 2018， 44（4）：815–824. doi:10.1016/j.ultrasmedbio.2017.12.001 doi: 10.1016/j.ultrasmedbio.2017.12.001
10	FENG C， ZHAN Y， SHAO H， et al. Postoperative expressions of TRACP5b and CA125 in patients with breast cancerand their values for monitoring bone metastasis［J］. J BUON， 2020， 25（2）：688-695.
11	韩文，李卫民，吴晓明，等. 纵横比大于1乳腺肿块的X线再评估及临床、超声特征分析［J］. 实用医学杂志， 2021， 37（24）：3179-3183.
12	WATANABE T， YAMAGUCHI T， TOHNO E， et al. B-mode ultrasound diagnostic flowchart for solid breast masses： JABTSBC-01 study［J］. J Med Ultrason， 2021， 48（1）：71-81. doi:10.1007/s10396-020-01072-0 doi: 10.1007/s10396-020-01072-0
13	WANG B， ZHU L， HE C， et al. Growth pattern can be used as a new characteristic to predict malignancy in breast cancer［J］.Breast Cancer， 2020， 27（3）：445-455. doi:10.1007/s12282-019-01041-7 doi: 10.1007/s12282-019-01041-7
14	WANG H， ZHAN W， CHEN W， et al. Sonography with vertical orientation feature predicts worse disease outcome in triple negative breast cancer［J］. Breast，2020，49：33-40. doi:10.1016/j.breast.2019.10.006 doi: 10.1016/j.breast.2019.10.006
15	CHEN K， WU S.The utility of quantifying the orientation of breast masses in ultrasound imaging［J］. Sci Rep， 2024， 14（1）：4578. doi:10.1038/s41598-024-55298-w doi: 10.1038/s41598-024-55298-w
16	桑田，张海俊，曹玉文，等. Logistic回归分析乳腺癌常规超声征象与腋窝淋巴结转移的关系［J］.中国医学影像技术， 2021， 37（8）：1158-1162.
17	石丽楠，曹春莉，桑田，等. 基于乳腺癌超声特征及临床病理指标的列线图预测腋窝淋巴结转移风险［J］. 中国医学影像学杂志， 2024， 32（4）：332-338.
18	ZONG Q， DENG J， GE W， et al. Establishment of simple nomograms for predicting axillary lymph node involvement in early breast cancer［J］. Cancer Manag Res， 2020， 12：2025-2035. doi:10.2147/cmar.s241641 doi: 10.2147/cmar.s241641
19	LIU H， XU G， YAO M H， et al. Association of conventional ultrasound， elastography and clinicopathological factors with axillary lymph node status in invasive ductal breast carcinoma with sizes > 10 mm ［J］. Oncotarget， 2018， 9（2）：2819-2828. doi:10.18632/oncotarget.18969 doi: 10.18632/oncotarget.18969
20	LI X L， XU H X， LI D D， et al. A risk model based on ultra-sound，ultrasound elastography，and histologic parameters for predicting axillary lymph node metastasis in breast in-vasive ductal carcinoma ［J］. Sci Rep， 2017， 7（1）：3029-3039． doi:10.1038/s41598-017-03582-3 doi: 10.1038/s41598-017-03582-3
21	YI C B， DING Z Y， DENG J， et al.Combining the ultrasound features of primary tumor and axillary lymph nodes can reduce false-negative rate dueing the prediction of high axillary node burden in BI-RADS category 4 or 5 breast cancer lesions［J］. Ultrasound Med Biol， 2020， 46（8）：1941-1948. doi:10.1016/j.ultrasmedbio.2020.04.003 doi: 10.1016/j.ultrasmedbio.2020.04.003
22	XUE S， ZHAO Q， TAI M， et al.Correlation between breast ultrasound microcalcification and the prognosis of breast cancer［J］. J Healthc Eng， 2021， 2021：6835963. doi:10.1155/2021/6835963 doi: 10.1155/2021/6835963
23	OGRADY S， MORGAN M P.Microcalcifications in breastcancer： From pathophysiology to diagnosis and prognosis［J］. Biochim Biophys Acta Rev Cancer， 2018， 1869（2）：310-320. doi:10.1016/j.bbcan.2018.04.006 doi: 10.1016/j.bbcan.2018.04.006
24	熊朝月，周敏，何小芳，等. 基于超声特征联合临床资料的预测模型对早期乳腺癌患者腋窝淋巴结转移的评估价值［J］. 实用临床医药杂志， 2022， 26（12）：14-18， 22.
25	YU X Q， HAO X Y， WAN J， et al. Correlation between ultrasound appearance of small breast cancer and axillary lymph node metastasis［J］. Ultrasound Med Biol， 2018， 44（2）：342-349. doi:10.1016/j.ultrasmedbio.2017.09.020 doi: 10.1016/j.ultrasmedbio.2017.09.020
26	LI L， YU T， SUN J， et al. Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI［J］. Acta Radiol， 2022， 63（8）：1014-1022. doi:10.1177/02841851211025857 doi: 10.1177/02841851211025857
27	YI C B， DING Z Y， DENG J， et al. Combining the ultrasound features of primary tumor and axillary lymph nodes can reduce false-negative rate during the prediction of high axillary node burden in BI-RADS category 4 or 5 breast cancer lesions［J］. Ultrasound Med Biol， 2020， 46（8）：1941-1948. doi:10.1016/j.ultrasmedbio.2020.04.003 doi: 10.1016/j.ultrasmedbio.2020.04.003
28	ZHANG H， SUI X， ZHOU S， et al. Correlation of Conventional Ultrasound Characteristics of Breast Tumors With Axillary Lymph Node Metastasis and Ki-67 Expression in Patients With Breast Cancer［J］. J Ultrasound Med， 2019， 38（7）：1833-1840. doi:10.1002/jum.14879 doi: 10.1002/jum.14879
29	XIONG J， ZUO W， WU Y， et al. ultrasonography and clinicopathological features of breast cancer in predicting axillary lymph node metastases［J］. BMC Cancer， 2022， 22（1）：1155. doi:10.1186/s12885-022-10240-z doi: 10.1186/s12885-022-10240-z
30	郑碧玉，张晓东，苏毅明，等. 年轻乳腺癌69例超声征象与腋窝淋巴结转移的关系［J］. 超声影像学杂志， 2022， 31（6）：525-531．
31	ZHAO Q， SUN J W， ZHOU H， et al. Pre-operative Conventional Ultrasound and Sonoelastography Evaluation for Predicting Axillary Lymph Node Metastasis in Patients with Malignant Breast Lesions［J］. Ultrasound Med Biol， 2018， 44 （12）： 2587-2595. doi:10.1016/j.ultrasmedbio.2018.07.017 doi: 10.1016/j.ultrasmedbio.2018.07.017
32	ZHU A Q， LI X L， AN L W， et al. Predicting axillary lymph node metastasis in patients with breast invasive ductal carcinoma with negative axillary ultrasound results using conventional altrasound and contrast-enhanced ultrasound［J］. J Ultrasound Med， 2020， 39（10）：2059-2070. doi:10.1002/jum.15314 doi: 10.1002/jum.15314

项目	病理类型
项目	浸润性导管癌	导管原位癌	浸润小叶癌	黏液癌	乳头状癌	髓样癌
未转移	69	25	2	1	3	1
转移	55	5	1	1	0	0
χ²值	10.518
P值	0.062

指标	未转移组（n = 101）	转移组（n = 62）	Z值	P值
年龄［M（P₂₅，P₇₅ ）］/岁	52（42,62）	55（41,69）	-1.964	0.050
月经状态			2.070	0.150
绝经	85（84.2）	57（91.9）
未绝经	16（15.8）	5（8.1）
最大径			26.844	< 0.001
≤ 2 cm	65（64.4）	14（37.1）
> 2 cm	36（35.6）	48（62.9）
象限			2.601	0.627
内上	19（18.8）	7（11.3）
内下	9（8.9）	7（11.3）
外上	58（57.4）	41（66.1）
外下	12（11.9）	6（9.7）
乳晕	3（3.0）	1（1.6）
组织学分级			1.598	0.450
低	4（4.0）	1（1.6）
中	65（64.4）	45（72.6）
高	32（31.6）	16（25.8）
边界			6.628	0.010
清晰	39（38.6）	12（19.4）
不清晰	62（61.4）	50（80.6）
边缘			4.843	0.028
光整	19（16.8）	4（6.5）
不光整	82（83.2）	58（93.5）
形状			2.280	0.131
规则	12（11.9）	3（4.8）
不规则	89（88.1）	59（95.2）
钙化			5.851	0.016
无	62（61.4）	26（41.9）
有	39（38.6）	36（58.1）
血流分级			4.392	0.036
0 ～Ⅰ级	46（45.5）	18（29.0）
Ⅱ～Ⅲ级	55（54.5）	44（71.0）
内部回声			0.013	0.909
均匀	12（11.9）	7（11.3）
不均匀	89（88.1）	55（88.7）
后方回声			0.397	0.529
未衰减	54（53.5）	30（48.4）
衰减	47（46.5）	32（51.6）
方位角/［M（P₂₅，P₇₅）］	27.7（33，35）	39.2（42，97）	-3.042	0.002

特征参数	变量名	赋值
最大径	X₁	0=最大径≤ 2 cm，1=最大径> 2 cm
年龄	X₂	连续变量
边界	X₃	0=边界清晰，1=边界不清晰
边缘	X₄	0=边缘光整，1=边缘不光整
钙化	X₅	0=无钙化，1=有钙化
方位角	X₆	连续变量
血流分级	X₇	0=0～Ⅰ级，1=Ⅱ～Ⅲ级

观察指标	B	SE	Wald χ²	P值	OR（95%CI）	AUC（95%CI）
最大径	2.299	0.445	26.662	< 0.001	9.961（4.163 ~ 23.836）	0.709（0.633 ~ 0.777）
年龄	0.022	0.021	1.124	0.289	1.022（0.981 ~ 1.065）	-
边界	1.171	0.476	6.060	0.014	3.227（1.270 ~ 8.199）	0.596（0.517 ~ 0.672）
边缘	2.137	0.721	8.783	0.003	8.475（2.062 ~ 34.827）	0.562（0.482 ~ 0.639）
钙化	1.397	0.437	10.212	0.001	4.044（1.717 ~ 9.529）	0.597（0.518 ~ 0.673）
方位角	0.034	0.009	13.239	< 0.001	1.034（1.016 ~ 1.053）	0.642（0.563 ~ 0.716）
血流分级	0.470	0.444	1.122	0.290	1.600（0.670 ~ 3.821）	-
常量	-7.995	1.615	24.498	-	-	-
预测模型	-	-	-	-	-	0.869（0.807 ~ 0.917）

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Predictive value of growth orientation quantification combined with S⁃Detect technique for axillary lymph node metastasis in breast cancer

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预测模型	病理
预测模型	转移（n = 30）	未转移（n = 50）
转移	23	4
未转移	7	46
Kappa值	0.701
P值	< 0.005