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.
ObjectiveTo analyze the risk factors influencing major postoperative complications (MPC) after minimally invasive radical gastrectomy for gastric cancer following neoadjuvant chemotherapy (NACT), and to construct a nomogram for accurately predicting MPC risk factors, and provide a reference for clinical decision-making. MethodsThe gastric cancer patients who underwent minimally invasive radical gastrectomy in the Department of General Surgery of the First Medical Center of the Chinese PLA General Hospital from February 2012 to December 2022 and met the inclusion criteria of this study were retrospectively collected. The univariate and multivariate logistic regression model were used to evaluate the risk factors influencing MPC and a nomogram model was constructed. The MPC were defined as Clavien-Dindo classification grade Ⅱ and beyond. The area under the receiver operating characteristic curve (AUC) and the calibration curve were used to evaluate the discrimination and accuracy of the nomogram model. ResultsA total of 362 patients were included in this study, among whom 65 cases (18.0%) experienced MPC. The multivariate logistic regression analysis showed that the age ≥58 years old, body mass index (BMI) ≥25 kg/m2, tumor long diameter ≥30 mm, operative time ≥300 min, and preoperative neutrophil-to-lymphocyte ratio (NLR) ≥3.7 were the risk factors influencing MPC. The nomogram model constructed using the above variables showed that the AUC (95%CI) was 0.731 (0.662, 0.801) in predicting the risk of MPC. The calibration curves showed that the prediction curve of the nomogram in predicting the MPC was agree well with the actual MPC (Hosmer-Lemeshow test: χ2=9.293, P=0.056). ConclusionFrom the results of this study, nomogram model constructed by combining age, BMI, tumor long diameter, operative time, and preoperative NLR can distinguish between patients with and without MPC after minimally invasive radical gastrectomy for gastric cancer following NACT, and has a better accuracy.
ObjectiveTo investigate the differences between indocyanine green (ICG) plus methylene blue and radioactive nuclide plus methylene blue for sentinel lymph node biopsy (SLNB) after Neoadjuvant chemotherapy (NAC) in breast cancer patients. Methods A total of 77 breast cancer patients who accepted SLNB and axillary lymph node dissection (ALND) after NAC from June 2017 to February 2019 were involved, among them, 46 breast cancer patients accepted SLNB by ICG plus methylene blue and 31 breast cancer patients accepted SLNB by radioactive nuclide plus methylene blue, pathological and clinical data were collected and analyzed.ResultsThere were 43 patients in the ICG plus methylene blue group and 30 patients in radioactive nuclide plus methylene blue group, which totally 73 patients were detected at least one sentinel lymph node in all the 77 patients, and the detection rate was 94.80%. The SLN detected rate, SLN detected numbers, sensitivity, false negative rate, and accuracy of the ICG plus methylene blue group were 93.48% (43/46), 2.32 per case, 82.61% (19/23), 17.39% (4/23), and 90.70% (39/43) respectively, as well as 96.77% (30/31), 2.6 per case, 83.33% (10/12), 16.67% (2/10), and 93.33% (28/30) in the radioactive nuclide plus methylene blue group. There was no significant difference between the ICG plus methylene blue group and radioactive nuclide plus methylene blue group in terms of SLN detected rate, SLN detected numbers, sensitivity, false negative rate, and accuracy (P>0.05).ConclusionICG plus methylene blue showed similar SLN detection rate, SLN detected numbers, sensitivity, false negative rate, and accuracy as radioactive nuclide plus methylene blue for SLNB in breast cancer patients after NAC, and both of them can be performed easily and conveniently.
ObjectiveTo evaluate the predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with multislice computed tomography (MSCT) in the evaluation of neoadjuvant chemotherapy (NACT) for breast cancer. MethodsThe clinical, imaging, and pathological data of breast cancer patients who received NACT in the Affiliated Hospital of Southwest Medical University from February 2019 to August 2021 were retrospectively collected. Based on the results of postoperative pathological examination, the patients were assigned into significant remission (Miller-Payne grade Ⅰ–Ⅲ) and non-significant remission (Miller-Payne grade Ⅳ–Ⅴ). The variables with statistical significance by univariate analysis or factors with clinical significance judged based on professional knowledge were included to conduct the logistic regression multivariate analysis to screen the risk factors affecting the degree of pathological remission after NACT. Then, the screened risk factors were used to establish a prediction model for the degree of pathological remission of breast cancer after NACT, and the efficacy of this model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve. ResultsAccording to the inclusion and exclusion criteria, a total of 211 breast cancer patients who received NACT were collected, including 116 patients with significant remission and 95 patients with non-significant remission. Logistic regression multivariate analysis results showed that the human epidermal growth factor receptor 2 positive, lower early enhancement rate after NACT, lower arterial stage net increment after NACT, and lower CT value of arterial phase of lesions would increase the probability of significant remission in patients with breast cancer after NACT (P<0.05). The area under the ROC curve of the model for predicting the degree of pathological remission of breast cancer after NACT was 0.984, the specificity was 93.7%, and the sensitivity was 95.7%. The calibration curve showed that the model result fit well with the actual result, and the DCA result showed that it had a high clinical net benefit value. ConclusionFrom the results of this study, DCE-MRI combined with MSCT enhanced scanning has a good predictive value for pathological remission degree after NACT for breast cancer, which can provide clinical guidance for further treatment.
ObjectiveTo compare the prognosis of neoadjuvant chemotherapy (NAC) and adjuvant chemotherapy (AC) in patients with T1-2N1-2M0 luminal breast cancer, and to analyze the factors affecting the prognosis. MethodsPatients with luminal breast cancer who met the inclusion criteria and had complete follow-up data from January 2014 to December 2019 were retrospectively collected. Patients received either neoadjuvant chemotherapy (NAC) or adjuvant chemotherapy (AC), both based on anthracycline-containing regimens. Kaplan-Meier analysis was performed to estimate survival, and Cox proportional hazards regression was used to identify risk factors affecting 5-year cumulative overall survival rate. The significance level was set at α=0.05. ResultsA total of 206 patients (99 receiving NAC and 107 receiving AC) who met the inclusion criteria were enrolled. The cohort comprised 101 patients with luminal A (57 AC, 44 NAC) and 105 with luminal B (50 AC, 55 NAC). At a median follow-up of 72.5 months, no significant difference in the 5-year cumulative overall survival rate was observed between AC and NAC patients (89.7% vs. 88.9%, P=0.571); However, the 5-year cumulative disease-free survival rate was significantly higher in the AC group as compared with the NAC group (85.0% vs. 73.5%, P<0.001). Subgroup analysis demonstrated that no significant differences in the 5-year cumulative overall survival rates between AC and NAC patients within either luminal A (94.7% vs. 86.4%, P=0.727) or luminal B (84.0% vs. 89.3%, P=0.864). However, for patients with luminal A, the 5-year cumulative disease-free survival rate was significantly higher in the AC subgroup than in the NAC subgroup (93.0% vs. 77.3%, P<0.001). In contrast, no significant difference in the 5-year cumulative disease-free survival rate between AC and NAC patients was observed in patients with luminal B (74.0% vs. 71.4%, P=0.201). Multivariate analysis using the Cox proportional hazards model identified the following independent risk factors for lower 5-year cumulative overall survival rate in patients with T1-2N1-2M0 luminal breast cancer: N2 stage [HR (95%CI)=2.290 (1.249, 4.196)], lymphovascular invasion [HR (95%CI)=2.181 (1.182, 4.026)], omission of endocrine therapy [HR (95%CI)=6.013 (2.590, 13.965)], and absence of pathological complete response after NAC [HR (95%CI)=2.403 (1.284, 4.496)]. ConclusionsThe results of this study suggest that patients with T1-2N1-2M0 luminal breast cancer can achieve higher disease-free survival from AC. But it is still necessary to comprehensively consider the patient’s condition such as lymph node metastasis, vascular cancer thrombus to formulate an individualized treatment plan to increase the overall survival rate of patients.
Objective To investigate the effect of radiotherapy after neoadjuvant chemotherapy and modified radical surgery on breast cancer specific survival (BCSS) of patients with stage cT1–2N1M0 breast cancer. Methods A total of 917 cT1–2N1M0 stage breast cancer patients treated with neoadjuvant chemotherapy and modified radical surgery from 2010 to 2017 were extracted from the The Surveillance, Epidemiology, and End Results (SEER) database. Of them 720 matched patients were divided into radiotherapy group (n=360) and non-radiotherapy group (n=360) by using propensity score matching (PSM). Cox proportional hazard regression model was used to explore the factors affecting BCSS. Results Patients were all interviewed for a median follow-up of 65 months, and the 5-year BCSS was 91.9% in the radiotherapy group and 93.2% in the non-radiotherapy group, there was no significant difference between the 2 groups (χ2=0.292, P=0.589). The results were the same in patients with no axillary lymph node metastasis, one axillary lymphnode metastasis, two axillary lymph node metastasis and 3 axillary lymph node metastasis group (χ2=0.139, P=0.709; χ2=0.578, P=0.447; χ2=2.617, P=0.106; χ2=0.062, P=0.803). The result of Cox proportional hazard regression analysis showed that, after controlling for Grade grade, time from diagnosis to treatment, efficacy of neoadjuvant chemotherapy, number of positive axillary lymph nodes, molecular typing, and tumor diameter at first diagnosis, radiotherapy had no statistically significant effect on BCSS [HR=1.048, 95%CI (0.704, 1.561), P=0.817]. Conclusions The effect of radiotherapy on the BCSS of patients with stage cT1–2N1M0 breast cancer who have received neoadjuvant chemotherapy and modified radical surgery with 0 to 3 axillary lymph nodes metastases is limited, but whether to undergo radiotherapy should still be determined according to the comprehensive risk of individual tumor patients.
ObjectiveTo compare the clinicopathologic characteristics and the difference of efficacy after neoadjuvant chemotherapy (NACT) between the patients with triple positive breast cancer (TPBC) and human epidermal growth factor receptor 2 (HER2) overexpression breast cancer. MethodsThe patients with TPBC and HER2 overexpression breast cancer admitted to the Affiliated Hospital of Southwest Medical University from January 2019 to July 2021 who met the inclusion conditions of this study were retrospectively collected, and the differences of clinicopathologic characteristics and the efficacy after NACT were compared between the patients with TPBC and HER2overexpression breast cancer. Meanwhile the risk factors affecting the efficacy of NACT were analyzed by logistic regression according to the risk factors of pCR based on the statistically significant indicators of univariate analysis and the indicators related to clinical characteristics. The HER2 overexpressing breast cancer was positive HER2, negative estrogen receptor (ER) and progesterone receptor (PR), and the TPBC was positive HER2, ER, and PR. The pathological complete response (pCR) was used to evaluate the efficacy, which was defined as ypT0/isypN0. ResultsA total of 105 patients were enrolled in this study, including 57 patients with TPBC and 48 patients with HER2 overexpression breast cancer, as well as 50 patients with pCR and 55 patients with non-pCR. ① Compared with patients with TPBC, the patients with HER2 overexpression breast cancer had the higher proportions of Miller-Payne system grade 4–5, partial response, pCR, and neutrophil-lymphocyte ratio (NLR) >2.77 (P<0.05). There were no statistical differences in other characteristics such as age, family history of breast cancer, histological grade, T and TNM stages, lymph node metastasis, lymphocyte-monocyte ratio, Ki-67 expression, and surgical method between them (P>0.05). ② The results of logistic multivariate analysis showed that the later T stage, the lower Ki-67 expression, the lower NLR, and the positive ER and PR statuses were not easy to achieve pCR after NACT (P<0.05). ConclusionsAccording to the results of this study, the efficacy of patients with HER2 overexpression breast cancer receiving NACT is more likely to achieve pCR than that of patients with TPBC. Positive hormone receptors (ER and PR), later T stage (stage 3–4), lower Ki-67 expression, and lower NLR (≤2.77) might be a worse efficacy after NACT for patients with breast cancer.
ObjectiveTo explore the value of a decision tree (DT) model based on CT for predicting pathological complete response (pCR) after neoadjuvant chemotherapy therapy (NACT) in patients with locally advanced rectal cancer (LARC).MethodsThe clinical data and DICOM images of CT examination of 244 patients who underwent radical surgery after the NACT from October 2016 to March 2019 in the Database from Colorectal Cancer (DACCA) in the West China Hospital were retrospectively analyzed. The ITK-SNAP software was used to select the largest level of tumor and sketch the region of interest. By using a random allocation software, 200 patients were allocated into the training set and 44 patients were allocated into the test set. The MATLAB software was used to read the CT images in DICOM format and extract and select radiomics features. Then these reduced-dimensions features were used to construct the prediction model. Finally, the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, and specificity values were used to evaluate the prediction model.ResultsAccording to the postoperative pathological tumor regression grade (TRG) classification, there were 28 cases in the pCR group (TRG0) and 216 cases in the non-pCR group (TRG1–TRG3). The outcomes of patients with LARC after NACT were highly correlated with 13 radiomics features based on CT (6 grayscale features: mean, variance, deviation, skewness, kurtosis, energy; 3 texture features: contrast, correlation, homogeneity; 4 shape features: perimeter, diameter, area, shape). The AUC value of DT model based on CT was 0.772 [95% CI (0.656, 0.888)] for predicting pCR after the NACT in the patients with LARC. The accuracy of prediction was higher for the non-PCR patients (97.2%), but lower for the pCR patients (57.1%).ConclusionsIn this preliminary study, the DT model based on CT shows a lower prediction efficiency in judging pCR patient with LARC before operation as compared with homogeneity researches, so a more accurate prediction model of pCR patient will be optimized through advancing algorithm, expanding data set, and digging up more radiomics features.
ObjectiveTo investigate the effect and predictive value of systemic inflammatory markers on pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) for locally advanced breast cancer (LABC). MethodsThe clinicopathologic data of female patients with LABC who received NACT and radical surgical resection in the Department of Breast Surgery, Affiliated Hospital of Southwest Medical University from February 2019 to February 2022 were retrospectively analyzed. The factors affecting pCR after NACT were analyzed by the multivariate logistic regression and the prediction model was established. The efficiency of the prediction model was evaluated by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). ResultsA total of 98 patients were gathered, of which 29 obtained pCR, with a pCR rate of 29.6%. The multivariate analysis of binary logistic regression showed that the patients with non-menopausal status, negative estrogen receptor (ER), chemotherapy+targeted therapy, and systemic immune-inflammation index (SII) <532.70 (optimal critical value) were more likely to obtain pCR after NACT (P<0.05). The prediction model was established according to logistic regression analysis: Logit (P)=0.697–2.974×(menopausal status)–1.932×(ER status)+3.277×(chemotherapy regimen)–2.652×(SII). The AUC (95%CI) of the prediction model was 0.914 (0.840, 0.961), P<0.001. ConclusionsIt is not found that other inflammatory indicators such as neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio are associated with pCR after NACT. But SII is an important predictor of pCR after NACT for LABC and has a good predictive efficiency.
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.