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Objective To investigate the influencing factors of neoadjuvant chemotherapy (NAC) in estrogen receptor (ER) positive breast cancer and establish a prediction model of NAC efficacy. Methods A total of 321 ER positive breast cancer patients who received NAC treatment for 4 cycles followed by surgical treatment in our department from 2015 to 2018 were enrolled in this study. According to the efficacy evaluation criteria of solid tumors, the patients who achieved complete response (CR) or partial response (PR) after NAC were assigned into effective group, while those with stable disease (SD) and progressive disease (PD) into ineffective group. The differences of clinical and pathological parameters were retrospectively analyzed and compared between the 2 groups. Univariate and multivariate logistic regression analyses were applied to screen out the significant influencing factors. R Language was employed to establish the prediction models of NAC efficacy and pathological CR (pCR) rate of ER positive breast cancer patients. Then the prediction ability of the prediction models was evaluated by receiver operating characteristic (ROC) curve and correction curve. Results The overall effective rate of NAC and pCR were 75.39% and 15.26%, respectively, in the patients with ER positive breast cancer. Significant differences were seen in age, menstrual status, maximum tumor diameter, clinical node stage (cN), tumor blood supply, and ER and Ki-67 expression levels and chemotherapy regimen between the 2 groups (P < 0.05). Logistic analysis showed that cN, tumor blood supply, ER and Ki-67 expression levels were related to the efficacy of NAC (P < 0.05), while the rate of pCR after NAC was related to the maximal tumor diameter, tumor blood supply, and ER and Ki-67 expression levels (P < 0.05). The nomograms of predicting NAC efficacy and pCR in ER positive breast cancer patients were constructed with significant independent influencing factors. The area under ROC curve (AUC) of NAC Effectiveness Prediction Model for ER positive breast cancer patients was 0.785, and the cut-off value was (0.696, 0.760), and with calibration curve coinciding well with the reference standard. The AUC of pCR prediction model was 0.829 and the cut-off value was (0.673, 0.857), and the 2 curves were slightly biased. Conclusion Tumor blood supply, cN, ER and Ki-67 expression levels are the independent factors of NAC efficacy in ER positive breast cancer patients. The patients with axillary lymph node metastasis, low ER expression, high Ki-67 expression and good blood supply of tumor can achieve the best efficacy of NAC. Tumor maximal diameter, tumor blood supply, ER and Ki-67 expression levels are independent factors of pCR after NAC. The patients with small tumor, low expression of ER, high expression of Ki-67 and good blood supply of tumor have the highest probability of pCR after NAC. The established nomogram can primarily evaluate the benefit and probability of pCR in these patients. |
