Background Pathological responses of neoadjuvant chemotherapy (NCT) are associated with survival outcomes in patients with breast cancer. Previous studies constructed models using out-of-date variables to predict pathological outcomes, and lacked external validation, making them unsuitable to guide current clinical practice. Objective The aim of this study was to develop and validate a nomogram to predict the objective remission rate (ORR) of NCT based on pretreatment clinicopathological variables. Methods Data from 110 patients with breast cancer who received NCT were used to establish and calibrate a nomogram for pathological outcomes based on multivariate logistic regression. The predictive performance of this model was further validated using a second cohort of 55 patients with breast cancer. Discrimination of the prediction model was assessed using an area under the receiver operating characteristic curve (AUC), and calibration was assessed using calibration plots. The diagnostic odds ratio (DOR) was calculated to further evaluate the performance of the nomogram and determine the optimal cut-off value. Results The final multivariate regression model included age, NCT cycles, estrogen receptor, human epidermal growth factor receptor 2 (HER2), and lymphovascular invasion. A nomogram was developed as a graphical representation of the model and showed good calibration and discrimination in both sets (an AUC of 0.864 and 0.750 for the training and validation cohorts, respectively). Finally, according to the Youden index and DORs, we assigned an optimal ORR cut-off value of 0.646. Conclusion We developed a nomogram to predict the ORR of NCT in patients with breast cancer. Using the nomogram, for patients who are operable and whose ORR is < 0.646, we believe that the benefits of NCT are limited and these patients can be treated directly using surgery.

Background Previous research results on the predictive factors of neoadjuvant chemotherapy (NCT) efficacy in breast cancer are inconsistent, suggesting that the ability of a single factor to predict efficacy is insufficient. Combining multiple potential efficacy-related factors to build a model may improve the accuracy of prediction. This study intends to explore the clinical and biological factors in breast cancer patients receiving NCT and to establish a nomogram that can predict the pathologic complete response (pCR) rate of NCT. Methods We selected 165 breast cancer patients receiving NCT from July 2017 to May 2019. Using pretreatment biopsy materials, immunohistochemical studies to assess estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and Ki-67 expression. The correlation between biological markers and pCR was analyzed. These predictors were used to develop a binary logistic regression model with cross-validation and to show the established predictive model with a nomogram. Results The nomogram for pCR based on lymphovascular invasion, anemia (hemoglobin≤120 g/L), ER, Ki67 expression levels and NCT regimen had good discrimination performance (area under the curve [AUC], 0.758; 95% confidence interval [CI], 0.675–0.841) and calibration coordination. According to the Hosmer-Lemeshow test, the calibration chart showed satisfactory agreement between the predicted and observed probabilities. The final prediction accuracy of cross-validation was 76%. Conclusions We developed a nomogram based on multiple clinical and biological covariations that can provide an early prediction of NCT response and can help to quickly assess the individual benefits of NCT.

PurposeDue to the rarity of metaplastic breast carcinoma (MpBC), no randomized trials have investigated the role of combined chemotherapy and radiotherapy (CCRP) in this condition. We aimed to explore and identify the effectiveness of CCRP in patients with regional lymph node metastasis (N+) non-metastatic MpBC.Materials and MethodsData were obtained from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program database. We assessed the effects of CCRP on overall survival (OS), breast cancer-specific survival (BCSS), and breast cancer-specific death (BCSD) using Kaplan-Meier analysis, competing risk model analysis, and competing risk regression mode analysis.ResultsA total of 707 women and 361 death cases were included in the unmatched cohort, of which 76.45% (276/361) were BCSD, and 23.55% (85/361) were non-breast cancer-specific deaths (non-BCSD). Both the ChemT and CCRP groups had better OS (ChemT group: HR: 0.59, 95% CI: 0.45–0.78, P&lt;0.001; CCRP group: HR: 0.31, 95% CI: 0.23–0.41, P&lt;0.001) and BCSS (ChemT group: HR: 0.63, 95% CI: 0.45–0.87, P&lt;0.001; CCRP group: HR: 0.32, 95%CI: 0.22–0.46, P&lt;0.001) than the non-therapy group. Subjects in the CCRP group tended to have significantly lower cumulative BCSD (Gray’s test, P=0.001) and non-BCSD (Gray’s test, P&lt;0.001) than the non-therapy group or ChemT group. In competing risk regression model analysis, subjects in the CCRP group had a better prognosis in BCSD (HR: 0.710, 95% CI: 0.508–0.993, P=0.045) rather than the ChemT group (HR: 1.081, 95% CI: 0.761–1.535, P=0.660) than the non-therapy group.ConclusionOur study demonstrated that CCRP could significantly decrease the risk of death for both BCSD and non-BCSD and provided a valid therapeutic strategy for patients with N+ non-metastatic MpBC.

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