Investigation of correlation between shear wave elastography and lymphangiogenesis in invasive breast cancer and diagnosis of axillary lymph node metastasis

• Published in: BMC cancer 2024-04, Vol.24 (1), p.409-409, Article 409
• Main Authors: Li, Bo, Dai, Shaochun, Wang, Qiucheng, Jing, Hui, Shao, Hua, Zhang, Lei, Qin, Ling, Qiao, Cong, Wang, Zhuozhong, Cheng, Wen
• Format: Article
• Language: English
• Subjects: Biopsy; Breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - surgery; Collagen; Collagen (type I); Elasticity Imaging Techniques - methods; Ethics; Female; Hospitals; Humans; Invasiveness; Lesions; Lymph node metastasis; Lymph nodes; Lymphangiogenesis; Lymphatic Metastasis - diagnostic imaging; Lymphatic system; Metastases; Metastasis; Normal distribution; Patients; Regression analysis; Retrospective Studies; Review boards; Shear wave elastography; Software; Statistical analysis; Tumors; Ultrasonic imaging
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-024-12115-x

Accurate evaluation of axillary lymph node metastasis (LNM) in breast cancer is very important. A large number of hyperplastic and dilated lymphangiogenesis cases can usually be found in the pericancerous tissue of breast cancer to promote the occurrence of tumor metastasis.Shear wave elastography (SWE) can be used as an important means for evaluating pericancerous stiffness. We determined the stiffness of the pericancerous by SWE to diagnose LNM and lymphangiogenesis in invasive breast cancer (IBC). Patients with clinical T1-T2 stage IBC who received surgical treatment in our hospital from June 2020 to December 2020 were retrospectively enrolled. A total of 299 patients were eventually included in the preliminary study, which included an investigation of clinicopathological features, ultrasonic characteristics, and SWE parameters. Multivariable logistic regression analysis was used to establish diagnostic model and evaluated its diagnostic performance of LNM. The correlation among SWE values, collagen volume fraction (CVF), and microlymphatic density (MLD) in primary breast cancer lesions was analyzed in another 97 patients. The logistic regression model is Logit(P)=-1.878 + 0.992*LVI-2.010*posterior feature enhancement + 1.230*posterior feature shadowing + 0.102*posterior feature combined pattern + 0.009*Emax. The optimum cutoff value of the logistic regression model was 0.365, and the AUC (95% CI) was 0.697 (0.636-0.758); the sensitivity (70.7 vs. 54.3), positive predictive value (PPV) (54.0 vs. 50.8), negative predictive value (NPV) (76.9 vs. 69.7), and accuracy (65.2 vs. 61.9) were all higher than Emax. There was no correlation between the SWE parameters and MLD in primary breast cancer lesions. The logistic regression model can help us to determine LNM, thus providing more imaging basis for the selection of preoperative treatment. The SWE parameter of the primary breast cancer lesion cannot reflect the peritumoral lymphangiogenesis, and we still need to find a new ultrasonic imaging method.