ObjectiveTo investigate the relation between mammographic density (MD) and the efficacy of neoadjuvant chemotherapy (NACT) for patients with breast cancer. MethodsThe clinicopathologic data of patients diagnosed with breast cancer in the Affiliated Hospital of Southwest Medical University from January 2019 to December 2021 and met the inclusion and exclusion criteria of this study were collected. According to the 5th edition of the Breast Imaging-Reporting and Data System, the MD was classified into 4 categories: a, b, c, and d. Based on the pathological evaluation systems of Miller-Payne and Residual Cancer Burden, the new and improved pathological criteria was structured including the residual cancer cell and lymph node statuses to evaluate the pathological changes of breast cancer after NACT. After adjusting the factors affecting MD, the original model (only including MD categories as independent variables), the minimum adjustment model (adding age, body mass index, and menopausal status as independent variables), and the fully adjusted model (further including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, Ki-67, axillary lymph node status at the initial diagnosis, and NACT regimen) were used to analyze the relation between MD and NACT effect. In the 3 models, the MD category a was used as the reference. ResultsA total of 287 patients with breast cancer were enrolled in this study. Thirty-eight, 76, 114, and 59 of whom with MD category a, b, c, and d respectively, and 14, 74, 117, and 82 of whom with grade L1, L2, L3, and L4 of NACT effect respectively. No matter in integrated patients or premenopausal patients, the results of the fully adjusted model showed that, the regression coefficient of MD classification was negative, and with the increase of MD classification, the odds ratio was <1 and showed a decreasing trend. ConclusionsFrom the results of this study, the increase of MD classification may have a negative impact on the effect of NACT. Namely, effect of NACT is poor in integrated patients or premenopausal patients of whom with higher MD. MD can be used as a predictor of NACT effect, so as to guide doctors in the selection and individual management of neoadjuvant therapy, and improve the prognosis of patients with breast cancer.
ObjectiveTo explore the factors associated with non-sentinel lymph node (NSLN) metastasis in early breast cancer patients with 1-2 positive sentinel lymph nodes (SLN), seeking the basis for exempting some SLN-positive patients from axillary lymph node dissection. MethodsA total of 299 early breast cancer patients who were diagnosed with positive sentinel lymph node (SLN) biopsy and underwent axillary lymph node dissection at the Affiliated Hospital of Southwest Medical University from January 2019 to April 2023 were selected. Univariate analysis was performed on the clinical and pathological data of patients, and multivariate logistic regression analysis was conducted to identify factors related to axillary non-sentinel lymph node (NSLN) metastasis of patients with SLN positive in early breast cancer. GraphPad Prim 9.0 was used to draw receiver operating characteristic (ROC) curve, and the area under curve (AUC) of ROC was calculated to quantify the predictive value of risk factors. ResultsAmong the 299 breast cancer patients with 1-2 SLN positive, 101 cases (33.78%) were NSLN positive and 198 cases (66.22%) were NSLN negative. Univariate analysis showed that the number of positive SLN, clinical T staging and lymphovascular invasion were related to the metastasis of NSLN (P<0.001). Multivariate logistic regression analysis indicated that having 2 positive SLN [OR=3.601, 95%CI (2.005, 6.470), P<0.001], clinical T2 staging [OR=4.681, 95%CI (2.633, 8.323), P<0.001], and presence lymphovascular invasion [OR=3.781, 95%CI (2.124, 6.730), P<0.001] were risk factors affecting axillary NSLN metastasis. The AUCs of the three risk factors were 0.623 3, 0.702 7 and 0.682 5, respectively, and the AUCs all were greater than 0.6, suggesting that the three risk factors had good predictive ability for NSLN metastasis. ConclusionThe number of positive SLN, clinical T staging, and lymphovascular invasion are related factors affecting NSLN metastasis in early breast cancer patients with positive SLN, and these factors have guiding significance for whether to exempt axillary lymph node dissection.
ObjectiveTo analyze the clinicopathologic characteristics and prognosis of human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients with different expression status of estrogen receptor (ER). MethodsThe patients with HER2-negative breast cancer met the inclusion and exclusion criteria and were treated in the Affiliated Hospital of Southwest Medical University from January 1, 2017 to December 31, 2019 were retrospectively collected, and then were assigned into 3 groups according to the ER expression status: ER-negative (ER expression positive rate <1%) group, ER-low expression (ER expression positive rate 1%–10%) group, and ER expression positive rate >10% group. The differences of clinicopathologic characteristics, therapy, and prognosis among the 3 groups were compared. And the risk factors affecting recurrence and metastasis of patients with ER-low expression were analyzed by Cox proportional hazards regression model. ResultsA total of 610 patients with HER2-negative breast cancer were included in this study, including 130 patients in the ER-negative group, 48 patients in the ER-low expression group, and 432 patients in the ER expression positive rate >10% group. The Bonferroni method was used to correct the test level after pairwise comparison, it was found that the histological grade was later (P<0.001, P=0.023) and the Ki-67 expression was higher (P<0.001, P=0.023) in the ER-negative group and ER-low expression group as compared with the ER expression positive rate >10% group; The proportion of the patients receiving chemotherapy in the ER-negative group was higher than that of the ER expression positive rate >10% group (χ2=10.310, P=0.001), while which had no statistical difference between the ER-low expression group and the ER-negative group or the ER expression positive rate >10% group (Fisher exact probability method, P=1.000; χ2= 3.585, P=0.058); The proportion of patients receiving endocrine therapy in the ER-low expression group was higher than that in the ER-negative group (χ2=36.333, P<0.001) and lower than the ER expression positive rate >10% group (χ2=246.996, P<0.001). The difference in disease-free survival (DFS) curves among 3 groups was statistically significant (χ2=46.805, P<0.001); There were no statistical differences in the overall survival (OS) curve and DFS curve between the ER-negative group and the ER-low expression group (Two stage test, P=0.786; χ2=1.141, P=0.286), and which in the ER expression positive rate >10% group were significantly better than thoses in the ER-negative group (χ2=10.137, P=0.001; χ2=39.344, P<0.001) and the ER-low expression group (χ2=4.075, P=0.044; χ2=31.911, P<0.001). The results of multivariate Cox proportional hazards regression analysis showed that N1 and N2 [N0 as reference: RR (95%CI)=7.740 (1.939, 30.897), P=0.004; RR (95%CI)=9.513 (1.990, 45.478), P=0.005) and T3 [T1 as reference: RR (95%CI)=27.357 (2.188, 342.041), P=0.010] increased the probabilities of recurrence and metastasis HER2-negative breast cancer patients with ER-low expression. ConclusionsAccording to results of this study, patients with HER2-negative breast cancer showed certain differences in histological grade and Ki-67 expression among patients with three different ER expression status, but no statistical difference is found between ER-low expression and ER-negative breast cancer, and the prognoses of both are worse than that of ER expression positive rate >10% breast cancer patients. Lymph node metastasis and larger tumor are risk factors affecting recurrence and metastasis in ER-low expression breast cancer patients.
ObjectiveTo analyze the association between nutritional and immune-related laboratory indices and pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients and focused on constructing a combination of laboratory indices to serve as a clinical predictor of pCR after NAC in breast cancer. MethodsRetrospectively collected the pre-NAC laboratory indices [albumin (ALB), total cholesterol, triglyceride, high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol, apolipoprotein A- Ⅰ, apolipoprotein B, white blood cell, neutrophil, lymphocyte, monocyte (MON), and platelet ] and clinicopathologic data of 310 patients with invasive breast cancer who had received NAC in the Department of Breast Surgery, Affiliated Hospital of Southwest Medical University, from September 1, 2020 to October 31, 2022. Logistic regression analysis was conducted to determine the correlation between laboratory indices and post-NAC pCR. The combinations of laboratory indices were constructed by simple mathematical operation. The area under the receiver operating characteristic curve (AUC) was used to evaluate the efficacy of different combinations of laboratory indices in predicting pCR and to determine the optimal combination of liboratory indices. Multivariate logistic regression analysis was used to analysis the relevance between clinicopathologic features and post-NAC pCR in breast cancer patients and to determine the independent predictor of post-NAC pCR. ResultsAmong the 310 patients, 49.4% (153/310) of them achieved pCR after NAC. Logistic regression analysis revealed that ALB (Z=5.203, P<0.001) and HDL-C (Z=2.129, P=0.033) were positively correlated with post-NAC pCR, while MON (Z=–4.883, P<0.001) was negatively correlated with post-NAC pCR. The AUC analysis of 6 different combinations of laboratory indices showed that the ALB/MON combination (the optimal combination of liboratory indices) had the highest predictive performance (median AUC=0.708) and was determined to be the neoadjuvant therapy predictive index (NTPI). Multivariate logistic regression analysis showed that estrogen receptor (Z=–3.273, P=0.001), human epidermal growth factor 2 (Z=7.041, P<0.001), Ki-67 (Z=2.457, P=0.014), and NTPI (Z=4.661, P<0.001) were the independent predictors for post-NAC pCR. ConclusionNTPI could serve as a predictive index for post-NAC pCR in patients with breast cancer.
Objective To explore the accuracy of contrast-enhanced magnetic resonance imaging (MRI) in predicting pathological complete remission (pCR) in breast cancer patients after neoadjuvant therapy (NAC). Methods The clinicopathological data of 245 patients with invasive breast cancer who had completed the surgical resection after NAC in the Affiliated Hospital of Southwest Medical University from March 2020 to April 2022 were collected retrospectively. According to the results of hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) detected by immunohistochemistry, all patients were divided into four subgroups: HR+/HER2–, HR+/HER2+, HR–/HER2+ and HR–/HER2–. The value of MRI in evaluating the efficacy of NAC was analyzed by comparing the postoperative pathological results as the gold standard with the residual tumor size assessed by preoperative MRI. Meanwhile, the sensitivity, specificity and positive predictive value (PPV) of pCR predicted by the evaluation results of enhanced MRI were analyzed, and further analyzed its predictive value for pCR of different subtypes of breast cancer. Results There were 88 cases (35.9%) achieved radiological complete response (rCR) and 106 cases (43.3%) achieved pCR in 245 patients. Enhanced MRI in assessing the size of residual tumors overestimated and underestimated 12.7% (31/245) and 9.8% (24/245) of patients, respectively. When setting rCR as the MRI assessment index the specificity, sensitivity and PPV were 84.2% (117/139), 62.3% (66/106) and 75.0% (66/88), respectively. When setting near-rCR as the MRI assessment index the specificity, sensitivity and PPV were 70.5% (98/139), 81.1% (86/106), and 67.7% (86/127), respectively. The positive predictive value of both MRI-rCR and MRI-near-rCR in evaluating pCR of each subtype subgroup of breast cancer was the highest in the HR–/HER2+ subgroup (91.7% and 83.3%, respectively). In each subgroup, compared with rCR, the specificity of near-rCR to predict pCR decreased to different degrees, while the sensitivity increased to different degrees. Conclusions Breast contrast-enhanced MRI can more accurately evaluate the efficacy of localized breast lesions after NAC, and can also more accurately predict the breast pCR after NAC. The HR–/HER2+ subgroup may be a potentially predictable population with pCR exemption from breast surgery. However, the accuracy of the evaluation of pCR by breast enhancement MRI in HR+/HER2– subgroup is low.