The value of ultrasonic parameters combined with clinicopathological parameters in predicting axillary lymph node metastasis in triple-negative breast cancer

The clinical data of 119 patients with triple-negative breast cancer (TNBC) were retrospectively analyzed, and comparisons revealed that the differences between those who developed axillary lymph node metastasis and those who did not were statistically significant when comparing the age, histological grading of the lesions, expression of Ki-67, and information about the morphology of the lesions, internal blood flow, and the ultrasonographic manifestations of axillary lymph nodes on ultrasonography of the distribution of the lesions in the lesions’ quadrants (p < 0.05). Multifactorial regression analysis suggested that age, histological grade, lesion quadrant, and axillary lymph node ultrasound performance were all relevant factors affecting axillary lymph node metastasis in TNBC patients; the predictive model of axillary lymph node metastasis in TNBC was constructed with the results of multifactorial regression analysis, and the results of the ROC curve analysis showed that the logistic regression model had an AUC of 0.761 and the sensitivity and specificity were 0.824 and 0.714, respectively, for predicting the metastasis of the axillary lymph nodes in TNBC patients. This suggests that ultrasound combined with pathological parameters has some value in helping clinical judgment of axillary lymph node metastasis in TNBC patients.

Triple-negative breast cancer; Ultrasound; Pathological parameters; Predictive value

[1] Derakhshan F, Reis-Filho JS. Pathogenesis of triple-negative breast cancer. Annual Review of Pathology: Mechanisms of Disease. 2022; 17: 181–204.

[2] Bianchini G, De Angelis C, Licata L, Gianni L. Treatment landscape of triple-negative breast cancer—expanded options, evolving needs. Nature Reviews Clinical Oncology. 2022; 19: 91–113.

[3] Howard FM, Olopade OI. Epidemiology of triple-negative breast cancer. The Cancer Journal. 2021; 27: 8–16.

[4] Li S, Li Y, Ma D, Shao Z. Prediction of axillary lymph node metastasis in triple-negative breast cancer by multi-omics analysis and an integrated model. Annals of Translational Medicine. 2022; 10: 623–623.

[5] Wang J, Lu X, Zheng X, Xia C, Li P. Clinical value of preoperative ultrasound signs in evaluating axillary lymph node status in triple-negative breast cancer. Journal of Oncology. 2022; 2022: 1–7.

[6] Jin J, Liu P, Ye J, Wu Y. Analysis of risk factors of axillary lymph-node metastasis in triple-negative breast cancer. Asian Journal of Surgery. 2023; 46: 2265–2267.

[7] Srour MK, Qu Y, Deng N, Carlson K, Mirocha J, Gao B, et al. Gene expression comparison between primary estrogen receptor‐positive and triple-negative breast cancer with paired axillary lymph node metastasis. The Breast Journal. 2021; 27: 432–440.

[8] Chen X, Chen A, Liu C, Zhang B. Triple-negative breast cancer with dermatomyositis: a case report and literature review. Cancer Management and Research. 2022; 14: 569–576.

[9] Dass SA, Tan KL, Selva Rajan R, Mokhtar NF, Mohd Adzmi ER, Wan Abdul Rahman WF, et al. Triple negative breast cancer: a review of present and future diagnostic modalities. Medicina. 2021; 57: 62.

[10] D’Orsi CJ, Bassett LW, Berg WA, Feig SA, Jackson VP, Kopans DB. Breast imaging reporting and data system: ACR BI-RADS-mammography. 4th edn. American College of Radiology: Reston, VA. 2003.

[11] Jud SM, Hatko R, Emons J, Lauterbach B, Hack CC, Preuß C, et al. Discordance between primary breast cancer and ipsilateral breast cancer tumor recurrence as a function of distance. Journal of Clinical Medicine. 2020; 9: 4033.

[12] Xia C, Dong X, Li H, Cao M, Sun D, He S, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chinese Medical Journal. 2022; 135: 584–590.

[13] Wang X, Wang C, Guan J, Chen B, Xu L, Chen C. Progress of breast cancer basic research in China. International Journal of Biological Sciences. 2021; 17: 2069–2079.

[14] Qiu R, Zhong Y, Hu M, Wu B. Breastfeeding and reduced risk of breast cancer: a systematic review and meta-analysis. Computational and Mathematical Methods in Medicine. 2022; 2022: 1–9.

[15] Zheng RS, Zhang SW, Sun KX, Chen R, Wang SM, Li L, et al. Cancer statistics in China, 2016. Chinese Journal of Oncology. 2023; 45: 212–220. (In Chinese)

[16] Zong X, Yu Y, Yang H, Chen W, Ding X, Liu S, et al. Effects of gonadotropin-releasing hormone analogs on ovarian function against chemotherapy-induced gonadotoxic effects in premenopausal women with breast cancer in China. JAMA Oncology. 2022; 8: 252.

[17] Yu Y, He Z, Ouyang J, Tan Y, Chen Y, Gu Y, et al. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: a machine learning, multicenter study. EBioMedicine. 2021; 69: 103460.

[18] Xu K, Wang R, Xie H, Hu L, Wang C, Xu J, et al. Single-cell RNA sequencing reveals cell heterogeneity and transcriptome profile of breast cancer lymph node metastasis. Oncogenesis. 2021; 10; 66.

[19] Zhang J, Li L, Zhe X, Tang M, Zhang X, Lei X, et al., The diagnostic performance of machine learning-based radiomics of dce-mri in predicting axillary lymph node metastasis in breast cancer: a meta-analysis. Frontiers in Oncology. 2022; 12: 799209.

[20] Song B. A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer. Breast Cancer. 2021; 28: 664–671.

[21] Chang JM, Leung JWT, Moy L, Ha SM, Moon WK. Axillary nodal evaluation in breast cancer: state of the art. Radiology. 2020; 295: 500–515.

[22] Tinterri C, Gentile D, Gatzemeier W, Sagona A, Barbieri E, Testori A, et al. Preservation of axillary lymph nodes compared with complete dissection in T1–2 breast cancer patients presenting one or two metastatic sentinel lymph nodes: the SINODAR-ONE multicenter randomized clinical trial. Annals of Surgical Oncology. 2022; 29: 5732–5744.

[23] Narongrit FW, Rispoli JV. Editorial for “Preoperative prediction of axillary lymph node metastasis in breast cancer using CNN based on multiparametric MRI”. Journal of Magnetic Resonance Imaging. 2022; 56: 710–711.

[24] Wang Z, Sun H, Li J, Chen J, Meng F, Li H, et al. Preoperative prediction of axillary lymph node metastasis in breast cancer using CNN based on multiparametric MRI. Journal of Magnetic Resonance Imaging. 2022; 56: 700–709.

[25] Wang D, Hu Y, Zhan C, Zhang Q, Wu Y, Ai T. A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer. Frontiers in Oncology. 2022; 12: 940655.

[26] Radosa JC, Eaton A, Stempel M, Khander A, Liedtke C, Solomayer E, et al. Evaluation of local and distant recurrence patterns in patients with triple-negative breast cancer according to age. Annals of Surgical Oncology. 2017; 24: 698–704.

[27] Lu C, Ma Z, Cheng X, Wu H, Tuo B, Liu X, et al. Pathological role of ion channels and transporters in the development and progression of triple-negative breast cancer. Cancer Cell International. 2020; 20: 377.

[28] Drapalik LM, Estes A, Sarode AL, Cao L, Shenk RR, Jarrett CM, et al. Age disparities in triple-negative breast cancer treatment and outcomes: an NCDB analysis. Surgery. 2022; 172: 821–830.

[29] Carvalho MJ, Dias MF, Silva TS, Custódio S, de Oliveira CF. Breast cancer patients with micrometastases in sentinel lymph nodes: differences considering additional metastatic lymph nodes. European Journal of Gynaecological Oncology. 2009; 30: 631–634.

[30] Kumagai H, Takehana K, Shioi Y, Tono C. Axillary schwannoma mimicking lymph node metastasis-associated breast cancer: a case report. Surgical Case Reports. 2022; 8: 135.