Objective To investigate clinical significance of serum VEGF-C level and C-erbB-2 protein expression in patients with breast cancer. Methods Sixty-two female patients with breast invasive ductal cancer and breast benign lesion were respectively selected. Serum VEGF-C level was detected by enzyme-linked immunosorbent assay (ELISA) before operation and at one month after operation, and C-erbB-2 protein expression in tissues of breast cancer was detected by immunohistochemistry. Then, the relationship between serum VEGF-C level and clinicopathologic characteristics and C-erbB-2 protein expressions wereas analyzed. Results The serum VEGF-C level before operation in breast cancer patients〔(279.65±17.34) pg/ml〕 was significantly higher than that in breast benign lesions patients 〔(167.26±12.15) pg/ml〕, P＜0.01. In breast cancer patients, the serum VEGF-C level before operation was higher than that at one month after operation 〔(209.45±15.23) pg/ml〕, P＜0.01. The serum VEGF level was related to tumor stage (P＜0.05) but not to patient age, tumor size, menopause status , lymph node metastasis or not and ER and PR expression (Pgt;0.05). The positive expression rate of C-erbB-2 protein in breast cancer patients (54.84%, 34/62) was significantly higher than that in breast benign lesion patients (11.29%, 7/62), P＜0.01. Moreover, the positive expression rate of C-erbB-2 protein in breast cancer patients with axilla lymph node metastasis (69.44%) was significantly higher than that without axilla lymph node metastases (34.62%), P＜0.05. The serum VEGF level increased with increasing expression intensity of C-erbB-2 protein and there was positive correlation between them (r=0.813，P＜0.05). Conclusions The serum VEGF-C level in breast cancer may be conducted as an assisted marker to differential diagnosis of breast tumor. C-erbB-2 is related to lymph node metastasis of breast cancer patients. There is synergistic effect between VEGF-C and C-erbB-2 in the lymph node metastasis way of breast cancer.
With the rising incidence of breast cancer among women, Neoadjuvant Chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting Pathological Complete Response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.