A Novel Nomogram Containing Efficacy Indicators to Predict Axillary Pathologic Complete Response after Neoadjuvant Systemic Therapy in Clinically Node-Positive Patients with Breast Cancer

A Novel Nomogram Containing Efficacy Indicators to Predict Axillary Pathologic Complete Response after Neoadjuvant Systemic Therapy in Clinically Node-Positive Patients with Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Novel Nomogram Containing Efficacy Indicators to Predict Axillary Pathologic Complete Response after Neoadjuvant Systemic Therapy in Clinically Node-Positive Patients with Breast Cancer Wenjie shi, Xiaofeng Huang, Ye Wang, Xinyu Wan, Jinzhi He, Yinggang Xu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1597454/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Neoadjuvant systemic therapy (NST) could make some clinically node-positive (cN+) breast cancer patients achieve axillary pathologic complete response (pCR). This study aimed to identify the patients who are likely to achieve axillary pCR and help surgeons make surgical decisions of the axilla. Methods: The cN+ breast cancer patients who received NST from 2015 to 2021 at The First Affiliated Hospital of Nanjing Medical University were enrolled. Univariate and multivariate logistic regression analyses were performed, and a nomogram was constructed based on the results of multivariate logistic regression analysis to predict the probability of axillary pCR and validated. Results: The axillary pCR was achieved in 208 (38.7%) patients. Patients who had a higher radiological response rate of breast tumor ( P = 0.039), smaller longest diameter of positive node after NST ( P = 0.028), ER-negative status ( P = 0.006), HER2-positive status ( P = 0.048) and breast pCR ( P < 0.001) were more likely to achieve axillary pCR. The nomogram had an area under the receiver operating characteristic curve (AUC) of 0.795 (95% CI: 0.747–0.843), and the calibration curve showed good agreement. Conclusion: A nomogram was constructed to predict the axillary pCR of cN+ patients receiving NST based on baseline and efficacy indicators to assist surgeons in making surgical decisions of the axilla. Breast cancer neoadjuvant systemic therapy efficacy indicators pCR nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Currently, breast cancer is one of the most common malignant tumors and one of the leading causes of death in women worldwide [ 1 ]. Surgeons are increasingly embracing the idea of neoadjuvant systemic therapy (NST), because for inoperable disease, NST can decrease tumor burden and increase the eligibility of patients for surgery. In addition, NST may also provide breast-conservation opportunities for patients who were previously unable to receive such treatment [ 2 – 5 ]. Axillary lymph node metastasis is one of the most important prognostic factors for breast cancer. For patients with axillary lymph node metastasis, the recurrence rate is higher, and the survival time is shorter [ 6 , 7 ]. After NST, sentinel lymph node biopsy (SLNB) can be generally performed for initially clinically node-negative (cN-) patients, whereas for initially clinically node-positive (cN+) patients, axillary lymph node dissection (ALND) remains the standard surgical method [ 8 ]. ALND is associated with many physical and psychological obstacles to patients, including upper extremity edema, numbness, stiffness, movement disorders, and psychological trauma [ 9 – 11 ]. As a result, many efforts have been made to de-escalate axillary surgery after NST. The ACOSOG Z1071 trial concluded that SLNB may also be applied to cN + breast cancer patients if removing the clipped node located before NST, simultaneously combining blue dye with radiolabeled colloid and examining at least three sentinel lymph nodes [ 12 , 13 ]. However, due to the higher price of the clips and the difficulty of obtaining radiolabeled colloids, the development and application of this technology in many countries is limited. In order to find a convenient, accurate, and more economical method to predict axillary pathologic complete response (pCR), many predictive models have been developed [ 14 – 18 ]. It should be noted that, these models were based more on the baseline indicators before NST. However, different individuals have different responses to NST even if they have similar baseline indicators. The advantage of NST is that the efficacy indicators can be monitored, such as size of the primary tumor and the axillary lymph nodes. These efficacy indicators may also be important for evaluating response to NST and predicting axillary pCR. In this study, cN + patients who had previously received NST for breast cancer in our center were enrolled. Baseline indicators before NST and efficacy indicators after NST of patients were all collected and analyzed. Patients were randomly divided into a training set and a validation set. Then, a nomogram to predict the axillary pCR was established based on both baseline and efficacy indicators in the training set, which were then confirmed in the validation set. This nomogram may be used to help surgeons make surgical decisions regarding axilla. Materials And Methods Patient Population This retrospective study was approved by the Ethics Review Board (2022-SR-201). We collected data from breast cancer patients who previously received NST at The First Affiliated Hospital of Nanjing Medical University from 2015 to 2021. The eligibility criteria for this study were as follows: (1) diagnosed with primary unilateral breast cancer, (2) received NST, (3) tumor size was measurable, (4) all patients were cN+, (5) all patients underwent mastectomy or lumpectomy combined with ALND, and (6) postoperative pathology information for breast and axilla was complete. Patients with missing data were excluded. A total of 676 breast cancer patients receiving NST from 2015 to 2021 at The First Affiliated Hospital of Nanjing Medical University were enrolled. Of these, 538 patients were eligible to enter our study. We used a computer to assign a random number to each patient, scrambled the number, and divided it into a training set and a validation set at a ratio of 7:3. Finally, 378 and 160 patients were respectively enrolled in the training and validation set. Clinicopathologic Evaluation The maximum diameter of breast tumor and the longest diameter of positive node were measured by ultrasound or MRI before and after NST. Whether ultrasound or MRI was used, the same examination was used to evaluate response both before and after NST. The radiological response rate of breast tumor was considered the reduction rate of the maximum diameter of the tumor after NST compared with the maximum diameter of the tumor before NST. The radiological response rate of positive node was the reduction rate of the longest diameter of positive node after NST compared with the longest diameter of positive node before NST. The response evaluation of breast tumor was measured according to RECIST (version 1.1) [ 19 ]. The clinical and pathologic stage of primary tumor was assessed according to the eighth edition of the AJCC cancer staging system [ 20 ]. Before NST, we performed a needle biopsy of the tumor for immunohistochemical analysis. We considered ER and PgR assays to be positive if there were at least 1% positive nuclear-stained cells in the tumor [ 21 ]. The tumors with an immunohistochemistry [IHC] score 3 + or IHC2+/in situ hybridization [ISH]-positive were designated HER2 positive [ 22 ]. Ki67 expression > 20% was considered as a high Ki67 level [ 23 ]. The patients primarily received three NST regimens, which included epirubicin and cyclophosphamide followed by taxanes (EC-T), epirubicin and cyclophosphamide followed taxanes and trastuzumab (EC-TH), and epirubicin and cyclophosphamide followed by taxanes, trastuzumab and pertuzumab (EC-THP). Breast pCR (ypT0/is) was defined as the absence of residual invasive carcinoma in the breast on postoperative pathology after NST, irrespective of in situ ductal carcinoma [ 24 ]. Statistical Analysis For the general characteristics of the patients, continuous variables are presented as means and standard deviations, and categorical variables are presented as frequencies and proportions. At first, the univariate logistic regression analysis was performed to screen out the variables associated with axillary pCR after NST. Then, variables with P < 0.05 were included in the multivariate logistic regression analysis. Odds ratios (ORs) and 95% CIs were also calculated. The final nomogram was developed by the variables that were statistically significant. After the model was established, the predictive ability of the model was determined through discrimination and calibration. The discrimination ability of the model was assessed by the receiver operating characteristic (ROC) curve area under the curve (AUC), which ranged from 0.5 to 1.0. The 0.5 value indicates a random chance and 1.0 indicates a perfect fit. The calibration ability was primarily evaluated using a calibration curve. All statistical analyses were performed using IBM SPSS Statistics 21.0 software (IBM Corporation, Armonk, NY, USA), GraphPad Prism version 9.0.0 (GraphPad Software, San Diego, California USA), and R version 4.0.5 software (The R Foundation for Statistical Computing, Austria, Vienna). Results Patient Characteristics The study flowchart is shown in Fig. 1 . The clinicopathologic characteristics before and after NST for the patients in the training set, validation set, and total population are listed in Table 1 . Table 1 Characteristics of patients in the training set, validation set and total population. Characteristics Training set N = 378 (%) Validation set N = 160 (%) Total N = 538 (%) P- value Age at diagnosis (years) 49.2 ± 10.9 49.0 ± 9.2 49.1 ± 10.4 0.430 Menstrual status 0.629 Premenopausal 186 (49.2) 86 (53.8) 272 (50.6) Postmenopausal 192 (50.8) 74 (46.2) 266 (49.4) Location of tumor 0.146 Left breast 198 (52.4) 69 (43.1) 267 (49.6) Right breast 180 (47.6) 91 (56.9) 271 (50.4) Clinical tumor size (cT) 0.872 cT1 31 (8.2) 16 (10.0) 47 (8.7) cT2 259 (68.5) 99 (61.9) 358 (66.5) cT3 51 (13.5) 28 (17.5) 79 (14.7) cT4 37 (9.8) 17 (10.6) 54 (10.0) Maximum diameter of breast tumor (mm) Before NST 39.3 ± 18.0 39.6 ± 18.1 39.4 ± 18.0 0.983 After NST 19.2 ± 15.3 20.0 ± 16.9 19.4 ± 15.8 0.853 Radiological response rate of breast tumor (%) 51.4 ± 30.3 49.3 ± 33.3 50.7 ± 31.2 0.774 Response evaluation of breast tumor 0.673 Complete response (CR) 46 (12.2) 15 (9.4) 61 (11.3) Partial response (PR) 250 (66.1) 103 (64.4) 353 (65.6) Stable disease (SD) 78 (20.6) 37 (23.1) 115 (21.4) Progressive disease (PD) 4 (1.1) 5 (3.1) 9 (1.7) Longest diameter of positive node (mm) Before NST 19.2 ± 9.4 19.1 ± 9.4 19.2 ± 9.4 0.998 After NST 11.4 ± 6.6 11.9 ± 7.4 11.6 ± 6.8 0.753 Radiological response rate of positive node (%) 36.4 ± 29.6 33.9 ± 39.3 35.6 ± 32.7 0.708 ER status 0.716 Negative 144 (38.1) 55 (34.4) 199 (37.0) Positive 234 (61.9) 105 (65.6) 339 (63.0) PgR status 0.295 Negative 205 (54.2) 75 (46.9) 280 (52.0) Positive 173 (45.8) 85 (53.1) 258 (48.0) HER2 status 0.980 Negative 235 (62.2) 98 (61.2) 333 (61.9) Positive 143 (37.8) 62 (38.8) 205 (38.1) Ki67 expression 0.618 ≤ 20% 45 (11.9) 24 (15.0) 69 (12.8) > 20% 333 (88.1) 136 (85.0) 469 (87.2) NST regimens 0.718 EC-T 223 (59.0) 95 (59.4) 318 (59.1) EC-TH(P) 129 (34.1) 59 (36.9) 188 (34.9) Others 26 (6.9) 6 (3.8) 32 (5.9) Surgeries of breast 0.924 Mastectomy 351 (92.9) 147 (91.9) 498 (92.6) Lumpectomy 27 (7.1) 13 (8.1) 40 (7.4) Breast pCR (ypT0/is) 0.976 No 292 (77.2) 125 (78.1) 417 (77.5) Yes 86 (22.8) 35 (21.9) 121 (22.5) Abbreviations: NST, neoadjuvant systemic therapy; ER, estrogen receptor; PgR, progesterone receptor; HER2, human epidermal growth factor receptor 2; EC-TH(P), epirubicin and cyclophosphamide followed by taxane, trastuzumab and pertuzumab; pCR, pathologic complete response; ypT0/is, the absence of residual invasive carcinoma in the breast on postoperative pathology after NST, irrespective of in situ ductal carcinoma The mean age of the 538 patients was 49.1 years. Premenopausal and postmenopausal patients accounted for approximately 50%. The proportion of left and right breast cancers were essentially equal (49.6% vs. 50.4%). There were the largest number of patients with cT1 and cT2 (75.2%). The mean maximum diameter of breast tumor was 39.4 mm before NST and 19.4 mm after NST. The mean radiological response rate of breast tumor was 50.7%. According to the response evaluation of breast tumor, only nine (1.7%) patients had progressive disease (PD) after NST. The mean longest diameter of positive node was 19.2 mm before NST and 11.6 mm after NST. The mean radiological response rate of positive node was 35.6%. ER status was positive in 339 (63.0%) patients, and PgR status was positive in 258 (48.0%) patients. HER2 status was positive in 205 (38.1%) patients. The vast majority of patients had high Ki67 expression (87.2%). With respect to the choice of NST, most HER2-negative patients received EC-T as the NST regimen. Of the 205 HER2-positive patients, greater than 188 patients received targeted therapy during NST. There were also a small number of HER2-positive patients who refused targeted therapy. Regarding the choice of surgery, 498 (92.6%) patients selected mastectomy, while only 40 (7.4%) patients chose lumpectomy. Finally, 121 (22.5%) patients achieved breast pCR. Clinicopathologic Response to NST As shown in Fig. 2 A, 208 (38.7%) patients achieved axillary pCR, 121 (22.5%) patients achieved breast pCR, and 95 (18.0%) patients achieved total pCR among all 538 cN + patients after NST. Different molecular subtypes of breast cancer had different responses to NST (Fig. 2 B). HR-/HER2 + breast cancer patients exhibited the highest axillary pCR rate (62.2%), while HR+/HER2- patients had the lowest axillary pCR rate (22.3%). Additionally, regardless of the molecular subtypes of breast cancer, the axillary pCR rate was significantly higher compared with that of the breast pCR after NST (Supplementary Tables 1 and 2). A subgroup analysis was performed on the basis of different breast pathologies after surgery (Fig. 2 C). Among 121 (22.5%) patients achieving breast pCR, 95 (78.5%) patients had simultaneous axillary pCR, whereas in 417 (77.5%) patients with breast non-pCR, only 113 (27.1%) patients achieved axillary pCR ( P < 0.001, Supplementary Table 3). Significant Predictors for Axillary pCR The results of univariate logistic regression analysis are shown in Fig. 3 . After NST, patients with a smaller maximum diameter of breast tumor (OR = 0.954, 95% CI: 0.937–0.971, P < 0.001), higher radiological response rate of breast tumor (OR = 8.740, 95% CI: 4.034–18.935, P < 0.001), and smaller longest diameter of positive node (OR = 0.951, 95% CI: 0.918–0.985, P = 0.006) were more likely to achieve axillary pCR. Compared with complete response (CR), patients with partial response (PR) or stable disease (SD) were less likely to achieve axillary pCR (OR = 0.317, 95% CI: 0.163–0.618, P = 0.001 and OR = 0.125, 95% CI: 0.055–0.285, P < 0.001). For patients with a negative ER status (OR = 0.354, 95% CI: 0.230–0.545, P < 0.001), a negative PgR status (OR = 0.392, 95% CI: 0.254–0.604, P < 0.001), and a positive HER2 status (OR = 2.380, 95% CI: 1.550–3.654, P < 0.001), the axillary pCR was more readily achieved. Patients who were treated with an EC-TH(P) regimen were more likely to achieve axillary pCR than EC-T regimen (OR = 2.439, 95% CI: 1.558–3.820, P < 0.001). Compared with mastectomy, patients choosing lumpectomy had a higher probability of achieving axillary pCR (OR = 3.442, 95% CI: 1.502–7.886, P = 0.003). According to postoperative pathology results, patients with breast pCR also had a higher probability to achieve axillary pCR than those with breast non-pCR (OR = 9.345, 95% CI: 5.281–16.535, P < 0.001). Multivariate logistic regression analysis was performed to identify variables that could predict axillary pCR after NST (Table 2 ). The variables related to axillary pCR were the radiological response rate of breast tumor ( P = 0.039), the longest diameter of positive node after NST ( P = 0.028), ER status ( P = 0.006), HER2 status ( P = 0.048) and breast pCR ( P < 0.001). Table 2 Multivariate logistic regression analysis of variables associated with axillary pCR in the training set (N = 378). Variable OR (95%CI) P -value Radiological response rate of breast tumor (%) 2.531 (1.048–6.112) 0.039* Longest diameter of positive node after NST (mm) 0.956 (0.918–0.995) 0.028* ER status 0.006* Negative 1.0 Positive 0.502 (0.306–0.821) HER2 status 0.048* Negative 1.0 Positive 1.638 (1.004–2.673) Breast pCR < 0.001* No 1.0 Yes 5.395 (2.896–10.049) Construction and Validation of the Nomogram A nomogram was constructed to predict the probability of achieving the axillary pCR after NST based on the results of multivariate logistic regression analysis, which included the baseline and efficacy indicators before and after NST (Fig. 4 ). The final nomogram was verified using a bootstrap method (n = 1000). The AUC of this predictive model was 0.795 (95% CI: 0.747–0.843) in the training set and 0.809 (95% CI: 0.738–0.880) in the validation set, which indicates that the model has stronger predictive power (Fig. 5 ). And there was no statistical difference between these two AUCs ( P = 0.748, Delong’s test) [ 25 ]. The calibration curve used to assess the goodness-of-fit of the nomogram exhibited good agreement between the predicted and actual probabilities of axillary pCR in the training set and the validation set (Fig. 6 ). Discussion In this study, the axillary pCR rate (38.7%) was significantly higher than the breast pCR rate (22.5%) in all patients, which is consistent with other studies [ 26 – 28 ]. These results indicate that axillary pCR is more common after NST. Thus, cN + patients may be candidates for axillary de-escalation surgery. In a subgroup analysis, the axillary pCR rate of HR-/HER2 + breast cancer patients was the highest (62.2%) among the different molecular subtypes of breast cancer. In contrast, the axillary pCR rate of HR+/HER2- patients was the lowest (22.6%), which was similar to that of another report (60% for HR-/HER2 + and 18% for HR+/HER2-) [ 29 ]. However, it is still worth noting that since the HR+/HER2- subtype accounts for the highest proportion (approximately 70%) of breast cancer [ 30 ], the total number of patients who finally achieved axillary pCR after NST is not small. As a result, a predictive model for axillary pCR is necessary for all molecular subtypes of breast cancer treated with NST. To study the role of different indicators in predicting axillary pCR, enrolled patients were randomly assigned to training and validation sets at a ratio of 7:3. Based on the results of the multivariate logistic regression analysis, a final predictive model was constructed. Results of the univariate logistic regression analysis showed that most baseline indicators were not associated with axillary pCR, including age at diagnosis, menstrual status, and clinical tumor size. Efficacy indicators and some other baseline indicators were related to axillary pCR, so the multivariate logistic regression analysis was further conducted. The results indicated that some baseline indicators, including ER status and HER2 status, were associated with axillary pCR, which is consistent with other reports [ 14 , 15 , 26 , 31 ]. Different from previous findings, several efficacy indicators were found to be strongly associated with the axillary pCR, including the radiological response rate of breast tumor, the longest diameter of positive node after NST, and breast pCR. Results showed that the higher the radiological response rate of breast tumor after NST, the higher likelihood of achieving axillary pCR. Moreover, the smaller the longest diameter of positive node after NST, the higher probability of achieving axillary pCR. These two indicators were efficacy indicators that are closely associated with axillary pCR, which once again shows that the prediction of axillary pCR may focus not only on baseline indicators, but also on the efficacy indicators after NST. In this study, the radiological response rate of breast tumor was associated with axillary pCR, so it was reasonable that breast pCR could predict axillary pCR as an extremity of the radiological response rate. Since this was a retrospective study, all enrolled patients had already received surgery and the pathological data for breast pCR had been obtained. However, this data is missing when this model is applied to the prediction of axillary pCR prior to surgery. At this time, if the breast clinical complete response (cCR) is not considered in preoperative radiological examinations, the probability of breast pCR is very small, therefore, it may be directly judged as breast non-pCR. On the other hand, if the breast cCR is highly probable based on preoperative radiological examinations, the pathological information can also be obtained before surgery. In a study aiming to omit breast surgery, when using image-guided vacuum-assisted biopsy (VAB) to obtain at least six representative samples in patients with residual radiological abnormality or a tumor bed measuring 2 cm or smaller, the breast pCR can be accurately demonstrated (the false-negative rate was 3.2%) [ 32 ]. In addition, intraoperative frozen pathology of breast tumors may also be applied during breast surgery, which provides the data for breast pCR to predict axillary pCR. In contrast, the radiological response rate of positive node showed no correlation with axillary pCR. Unlike breast tumors, lymph nodes are inherently anatomical in size. The breast could achieve pCR when NST was highly effective, but the lymph nodes could only shrink back to their original size and would not disappear; thus, the radiological response rate of positive node could not achieve 100%. In this study, the mean longest diameter of positive node after NST was 11.6 mm, and the highest radiological response rate of positive node was 90%. This also suggests that evaluating the response of lymph nodes to NST may require the combination of other factors, such as the cortical thickness of the lymph nodes. As for the response evaluation of breast tumor, results showed that compared with PR and SD, patients achieving CR were more likely to achieve axillary pCR. While compared with PD, the predictive superiority of achieving CR was not demonstrated in this study. Since NST was effective, the number of PD patients was particularly small (1.7%), which affected the statistical results. In this study, a nomogram containing efficacy indicators after NST was constructed. The AUC of this predictive model was 0.795 and the calibration curve also showed good agreement between the predicted and actual probabilities of axillary pCR, which indicated that the model had a more accurate prediction ability of axillary pCR. In contrast, when only baseline indicators were included, the AUC that was shown in Fig. 5 was 0.705 ( P < 0.001, Delong’s test) [ 25 ], which was consistent with other report containing only baseline indicators [ 17 ]. The above evidence confirmed that the efficacy indicators are crucial for predicting axillary pCR. Our study also had some limitations. First, it was a retrospective and single-center study, and the number of patients used to construct and validate the nomogram was relatively small. Therefore, a subgroup analysis was somewhat limited. Second, all patients included in this study were clinically node-positive (cN+) rather than pathologically node-positive (pN+), which partially affects the accuracy of the model. A new prospective study is planned to solve these problems. In conclusion, we constructed a nomogram to predict axillary pCR after NST in cN + patients with breast cancer. According to the results of the statistical analyses, efficacy indicators reflecting the therapeutic response of NST were found to be very important in predicting axillary pCR. Based on these efficacy indicators and some baseline indicators, we established a novel nomogram, which was validated and considered to be highly accurate in predicting axillary pCR. This predictive model may help surgeons to de-escalate or even omit axillary surgery for patients with axillary lymph node downstaging after NST in the future. Declarations Acknowledgement We would like to thank all the physicians for their actively cooperating with us in collecting patient information, thank all the patients and their families for allowing us to analyse their data. Funding This work was funded by Jiangsu Province Six Talents Summit Project (WSW-001), Chinese Society of Clinical Oncology Foundation (Y-sy2018-077, Y-JS2019-096), and Young Scholars Fostering Fund of the First Affiliated Hospital of Nanjing Medical University (PY2021038). Authors’ contributions The authors’ contributions were as follows: Xiaoming Zha and Jue Wang conceptualized and designed the study. All authors completed the acquisition, analysis, and interpretation of the data. Xiaoming Zha and Jue Wang obtained the study funding. Wenjie Shi, Xiaofeng Huang and Jue Wang were responsible for the methodology. Xiaoming Zha and Jue Wang provided study supervision. Wenjie Shi drafted the original version of the manuscript. All authors critically revised drafts of the manuscript and approved the final version. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Review Board (2022-SR-201). Informed Consent Statement Due to the retrospective nature of the study, a need for informed consent was waived by the Ethics Review Board. Data availability The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Competing interest The authors declare no competing interests. References DeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Goding Sauer A, Jemal A, Siegel RL (2019) Breast cancer statistics, 2019. CA Cancer J Clin 69(6):438–451 Steenbruggen TG, van Ramshorst MS, Kok M, Linn SC, Smorenburg CH, Sonke GS (2017) Neoadjuvant Therapy for Breast Cancer: Established Concepts and Emerging Strategies. Drugs 77(12):1313–1336 Korde LA, Somerfield MR, Carey LA, Crews JR, Denduluri N, Hwang ES, Khan SA, Loibl S, Morris EA, Perez A et al (2021) Neoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline. J Clin Oncol 39(13):1485–1505 Miglietta F, Dieci MV, Griguolo G, Guarneri V (2021) Neoadjuvant approach as a platform for treatment personalization: focus on HER2-positive and triple-negative breast cancer. Cancer Treat Rev 98:102222 Cortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, Bonnefoi H, Cameron D, Gianni L, Valagussa P et al (2014) Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384(9938):164–172 Chang JM, Leung JWT, Moy L, Ha SM, Moon WK (2020) Axillary Nodal Evaluation in Breast Cancer: State of the Art. Radiology 295(3):500–515 Fisher CS, Margenthaler JA, Hunt KK, Schwartz T (2020) The Landmark Series: Axillary Management in Breast Cancer. Ann Surg Oncol 27(3):724–729 Boland MR, Al-Hilli Z (2021) Management of the axilla after neoadjuvant chemotherapy in breast cancer patients. Br J Surg 108(7):748–749 Hack TF, Cohen L, Katz J, Robson LS, Goss P (1999) Physical and psychological morbidity after axillary lymph node dissection for breast cancer. J Clin Oncol 17(1):143–149 Ivens D, Hoe AL, Podd TJ, Hamilton CR, Taylor I, Royle GT (1992) Assessment of morbidity from complete axillary dissection. Br J Cancer 66(1):136–138 Yeoh EK, Denham JW, Davies SA, Spittle MF (1986) Primary breast cancer. Complications of axillary management. Acta Radiol Oncol 25(2):105–108 Boughey JC, Suman VJ, Mittendorf EA, Ahrendt GM, Wilke LG, Taback B, Leitch AM, Kuerer HM, Bowling M, Flippo-Morton TS et al (2013) Sentinel lymph node surgery after neoadjuvant chemotherapy in patients with node-positive breast cancer: the ACOSOG Z1071 (Alliance) clinical trial. JAMA 310(14):1455–1461 Boughey JC, Ballman KV, Le-Petross HT, McCall LM, Mittendorf EA, Ahrendt GM, Wilke LG, Taback B, Feliberti EC, Hunt KK (2016) Identification and Resection of Clipped Node Decreases the False-negative Rate of Sentinel Lymph Node Surgery in Patients Presenting With Node-positive Breast Cancer (T0-T4, N1-N2) Who Receive Neoadjuvant Chemotherapy: Results From ACOSOG Z1071 (Alliance). Ann Surg 263(4):802–807 Jin X, Jiang YZ, Chen S, Shao ZM, Di GH (2016) A Nomogram for Predicting the Pathological Response of Axillary Lymph Node Metastasis in Breast Cancer Patients. Sci Rep 6:32585 Vila J, Mittendorf EA, Farante G, Bassett RL, Veronesi P, Galimberti V, Peradze N, Stauder MC, Chavez-MacGregor M, Litton JF et al (2016) Nomograms for Predicting Axillary Response to Neoadjuvant Chemotherapy in Clinically Node-Positive Patients with Breast Cancer. Ann Surg Oncol 23(11):3501–3509 Choi HJ, Ryu JM, Kim I, Nam SJ, Kim SW, Yu J, Lee JE, Lee SK (2019) Nomogram for accurate prediction of breast and axillary pathologic response after neoadjuvant chemotherapy in node positive patients with breast cancer. Ann Surg Treat Res 96(4):169–176 Fujii T, Kogawa T, Wu J, Sahin AA, Liu DD, Chavez-MacGregor M, Giordano SH, Raghavendra A, Murthy RK, Tripathy D et al (2017) Nomogram to predict pathologic complete response in HER2-positive breast cancer treated with neoadjuvant systemic therapy. Br J Cancer 116(4):509–514 Pu S, Wang K, Liu Y, Liao X, Chen H, He J, Zhang J (2020) Nomogram-derived prediction of pathologic complete response (pCR) in breast cancer patients treated with neoadjuvant chemotherapy (NCT). BMC Cancer 20(1):1120 Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247 Giuliano AE, Connolly JL, Edge SB, Mittendorf EA, Rugo HS, Solin LJ, Weaver DL, Winchester DJ, Hortobagyi GN (2017) Breast Cancer-Major changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin 67(4):290–303 Hammond MEH, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, Fitzgibbons PL, Francis G, Goldstein NS, Hayes M et al (2010) American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol 28(16):2784–2795 Wolff AC, Hammond MEH, Allison KH, Harvey BE, Mangu PB, Bartlett JMS, Bilous M, Ellis IO, Fitzgibbons P, Hanna W et al (2018) Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update. J Clin Oncol 36(20):2105–2122 Coates AS, Winer EP, Goldhirsch A, Gelber RD, Gnant M, Piccart-Gebhart M, Thürlimann B, Senn HJ (2015) Tailoring therapies–improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Annals of oncology: official journal of the European Society for Medical Oncology 26(8):1533–1546 von Minckwitz G, Untch M, Blohmer J-U, Costa SD, Eidtmann H, Fasching PA, Gerber B, Eiermann W, Hilfrich J, Huober J et al (2012) Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol 30(15):1796–1804 DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–845 Guo R, Su Y, Si J, Xue J, Yang B, Zhang Q, Chi W, Chen J, Chi Y, Shao Z et al (2020) A nomogram for predicting axillary pathologic complete response in hormone receptor-positive breast cancer with cytologically proven axillary lymph node metastases. Cancer 126(Suppl):3819–3829 Kim R, Chang JM, Lee H-B, Lee SH, Kim S-Y, Kim ES, Cho N, Moon WK (2019) Predicting Axillary Response to Neoadjuvant Chemotherapy: Breast MRI and US in Patients with Node-Positive Breast Cancer. Radiology 293(1):49–57 Choi HJ, Ryu JM, Kim I, Nam SJ, Kim SW, Yu J, Lee JE, Lee SK (2019) Prediction of axillary pathologic response with breast pathologic complete response after neoadjuvant chemotherapy. Breast Cancer Res Treat 176(3):591–596 Samiei S, Simons JM, Engelen SME, Beets-Tan RGH, Classe J-M, Smidt ML (2021) : Axillary Pathologic Complete Response After Neoadjuvant Systemic Therapy by Breast Cancer Subtype in Patients With Initially Clinically Node - Positive Disease : A Systematic Review and Meta - analysis . JAMA Surg 156 (6):e210891 Waks AG, Winer EP (2019) Breast Cancer Treatment: A Review. JAMA 321(3):288–300 Kim JY, Park HS, Kim S, Ryu J, Park S, Kim SI (2015) Prognostic Nomogram for Prediction of Axillary Pathologic Complete Response After Neoadjuvant Chemotherapy in Cytologically Proven Node-Positive Breast Cancer. Med (Baltim) 94(43):e1720 Tasoulis MK, Lee H-B, Yang W, Pope R, Krishnamurthy S, Kim S-Y, Cho N, Teoh V, Rauch GM, Smith BD et al (2020) Accuracy of Post-Neoadjuvant Chemotherapy Image-Guided Breast Biopsy to Predict Residual Cancer. JAMA Surg 155(12):e204103 Supplementary Files Supplementarymaterials.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1597454","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":108579130,"identity":"58f00208-5608-4572-9a9d-1c017f8abe99","order_by":0,"name":"Wenjie shi","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"shi","suffix":""},{"id":108579131,"identity":"f64e96be-7463-4b83-a250-b0279a024275","order_by":1,"name":"Xiaofeng Huang","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Huang","suffix":""},{"id":108579132,"identity":"e76f7727-1968-446b-ad2f-a5dcee5e2824","order_by":2,"name":"Ye Wang","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Wang","suffix":""},{"id":108579133,"identity":"6d921fbe-c61d-40a0-b35b-afb6b2cc735b","order_by":3,"name":"Xinyu Wan","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Wan","suffix":""},{"id":108579134,"identity":"4bc03e17-0a6a-43ff-80e5-95cceda78e9c","order_by":4,"name":"Jinzhi He","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinzhi","middleName":"","lastName":"He","suffix":""},{"id":108579135,"identity":"a088ebcc-2871-4710-bf58-20bfe6751477","order_by":5,"name":"Yinggang Xu","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yinggang","middleName":"","lastName":"Xu","suffix":""},{"id":108579136,"identity":"b1e9a6d7-07c4-48ec-88ac-ef1ae85380e2","order_by":6,"name":"Weiwei Zhang","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Zhang","suffix":""},{"id":108579137,"identity":"6c568a39-ce8a-49f8-b5c5-d6789d27d0f4","order_by":7,"name":"Xiaoqing Shi","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoqing","middleName":"","lastName":"Shi","suffix":""},{"id":108579138,"identity":"8d0a063f-5903-4bbd-ae20-1426b423d7da","order_by":8,"name":"Rui Chen","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Chen","suffix":""},{"id":108579139,"identity":"5b1733b3-37e3-44ba-a592-b0a8568e836a","order_by":9,"name":"Lu Xu","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Xu","suffix":""},{"id":108579140,"identity":"1e00b10c-a177-4240-8279-ca1c0bae9492","order_by":10,"name":"Xiaoming Zha","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoming","middleName":"","lastName":"Zha","suffix":""},{"id":108579141,"identity":"2c56eaee-6a5c-4a78-a2be-e9271b538010","order_by":11,"name":"Jue Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYLACCQYGOTZmxoYDCRUMzGARHiK0GPOzNx888OAMsVqAIHFmz7Hkgw/boFx8WgyOnz38wrLNhnHDjRyDA4nz7rDrzkhgfPC2jUHeHJeWM3lpFpJtacwGYC3bnjGb3UhgNpzbxmC4swG7FrMDOWYGkm2H2aBaDoO0sEnztjEkGBzAoeX8G5CW/zwQLXPAWth/49VyI8f4gWTbAQnJnmMJBxIbILYw49Nif+ONGYPEuWQDYCAfOJBwDKjlzMNmyTnnJAw34NAi2Z9j/FmizK6+jZmx+eOPmsPJZseTD354U2Yjj8sWIGCTlkDiJTMwMDYwgKMXN2D++AGJZ4dP6SgYBaNgFIxMAACB72WHt2yG2wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7564-3057","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jue","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2022-04-26 13:42:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1597454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1597454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21968556,"identity":"9ceb490e-1e41-46e5-8799-0a02cbd4437f","added_by":"auto","created_at":"2022-05-27 15:51:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":404385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flowchart\u003c/strong\u003e NST, neoadjuvant systemic therapy; cN-, clinically node-negative; cN+, clinically node-positive\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-1597454/v1/08e9e6679aa36ea2d2906b36.png"},{"id":21968553,"identity":"a539874f-b125-42f5-8891-c51c7a2d6873","added_by":"auto","created_at":"2022-05-27 15:51:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":323481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe axillary pCR rate was significantly higher than breast pCR rate in all patients (A). The axillary pCR rate was significantly higher than breast pCR rate in different molecular subtypes of breast cancer (B). The axillary pCR rate was significantly higher in the breast pCR subgroup than in the breast non-pCR subgroup (C). \u003c/strong\u003e＊, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; pCR, pathologic complete response; HR, hormone receptor; HER2, human epidermal growth factor receptor 2\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-1597454/v1/692fab965530d9296595a879.png"},{"id":21968555,"identity":"c6a1caf7-df8c-4674-abf5-a748aec49902","added_by":"auto","created_at":"2022-05-27 15:51:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":656251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe results of univariate logistic regression analysis of indicators associated with axillary pCR in the training set (N = 378)\u003c/strong\u003e pCR, pathologic complete response; NST, neoadjuvant systemic therapy; CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease; ER, estrogen receptor; PgR, progesterone receptor; HER2, human epidermal growth factor receptor 2; EC-TH(P), epirubicin, cyclophosphamide followed by taxanes, trastuzumab and pertuzumab\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-1597454/v1/29ad4a40953c92f44c75a213.png"},{"id":21968554,"identity":"6b89c3a5-b40d-4b65-bd96-0a8e249eaa79","added_by":"auto","created_at":"2022-05-27 15:51:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA nomogram to predict axillary pCR after NST in cN+ breast cancer patients \u003c/strong\u003eNST, neoadjuvant systemic therapy; ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; pCR, pathologic complete response\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-1597454/v1/0d1bf6d935689470ec9ebe63.png"},{"id":21968557,"identity":"3957e2a8-9b40-4ec2-91c1-f3f3ab5278e6","added_by":"auto","created_at":"2022-05-27 15:51:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":357445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe receiver operating characteristic (ROC) curves \u003c/strong\u003eThe\u0026nbsp;area under the curve (AUC) was 0.795 (95% CI: 0.747–0.843) in the training set, 0.809 (95% CI: 0.738–0.880) in the validation set, and 0.705 (95% CI: 0.651–0.759) in the predictive model only including baseline indicators.\u003c/p\u003e","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-1597454/v1/a184b11d2e1404406a4f7cab.png"},{"id":21968558,"identity":"214cb6df-184e-479c-a94a-75cfb647ed2d","added_by":"auto","created_at":"2022-05-27 15:51:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":353835,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve of actual and predicted probabilities of axillary pCR in (A) the training set and (B) the validation set \u003c/strong\u003eThe horizontal axis indicates the predicted probabilities measured by the nomogram and the vertical axis indicates the actual probabilities. pCR, pathologic complete response\u003c/p\u003e","description":"","filename":"OnlineFig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-1597454/v1/a0fadae6ad3c1535a3579c06.png"},{"id":22272369,"identity":"3117ec84-9b26-4b99-a9a6-a1d61daaa453","added_by":"auto","created_at":"2022-06-05 20:12:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1493419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1597454/v1/b00181a3-803d-4e21-ad45-292856b84553.pdf"},{"id":21968552,"identity":"7ff1c0ac-5f0d-4d57-82c4-b61ba1b554f5","added_by":"auto","created_at":"2022-05-27 15:51:08","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29696,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.doc","url":"https://assets-eu.researchsquare.com/files/rs-1597454/v1/d3a7657f049143bed9be0571.doc"}],"financialInterests":"","formattedTitle":"A Novel Nomogram Containing Efficacy Indicators to Predict Axillary Pathologic Complete Response after Neoadjuvant Systemic Therapy in Clinically Node-Positive Patients with Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCurrently, breast cancer is one of the most common malignant tumors and one of the leading causes of death in women worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Surgeons are increasingly embracing the idea of neoadjuvant systemic therapy (NST), because for inoperable disease, NST can decrease tumor burden and increase the eligibility of patients for surgery. In addition, NST may also provide breast-conservation opportunities for patients who were previously unable to receive such treatment [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAxillary lymph node metastasis is one of the most important prognostic factors for breast cancer. For patients with axillary lymph node metastasis, the recurrence rate is higher, and the survival time is shorter [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. After NST, sentinel lymph node biopsy (SLNB) can be generally performed for initially clinically node-negative (cN-) patients, whereas for initially clinically node-positive (cN+) patients, axillary lymph node dissection (ALND) remains the standard surgical method [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. ALND is associated with many physical and psychological obstacles to patients, including upper extremity edema, numbness, stiffness, movement disorders, and psychological trauma [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As a result, many efforts have been made to de-escalate axillary surgery after NST. The ACOSOG Z1071 trial concluded that SLNB may also be applied to cN\u0026thinsp;+\u0026thinsp;breast cancer patients if removing the clipped node located before NST, simultaneously combining blue dye with radiolabeled colloid and examining at least three sentinel lymph nodes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, due to the higher price of the clips and the difficulty of obtaining radiolabeled colloids, the development and application of this technology in many countries is limited.\u003c/p\u003e \u003cp\u003eIn order to find a convenient, accurate, and more economical method to predict axillary pathologic complete response (pCR), many predictive models have been developed [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It should be noted that, these models were based more on the baseline indicators before NST. However, different individuals have different responses to NST even if they have similar baseline indicators. The advantage of NST is that the efficacy indicators can be monitored, such as size of the primary tumor and the axillary lymph nodes. These efficacy indicators may also be important for evaluating response to NST and predicting axillary pCR.\u003c/p\u003e \u003cp\u003eIn this study, cN\u0026thinsp;+\u0026thinsp;patients who had previously received NST for breast cancer in our center were enrolled. Baseline indicators before NST and efficacy indicators after NST of patients were all collected and analyzed. Patients were randomly divided into a training set and a validation set. Then, a nomogram to predict the axillary pCR was established based on both baseline and efficacy indicators in the training set, which were then confirmed in the validation set. This nomogram may be used to help surgeons make surgical decisions regarding axilla.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Population\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the Ethics Review Board (2022-SR-201). We collected data from breast cancer patients who previously received NST at The First Affiliated Hospital of Nanjing Medical University from 2015 to 2021. The eligibility criteria for this study were as follows: (1) diagnosed with primary unilateral breast cancer, (2) received NST, (3) tumor size was measurable, (4) all patients were cN+, (5) all patients underwent mastectomy or lumpectomy combined with ALND, and (6) postoperative pathology information for breast and axilla was complete. Patients with missing data were excluded.\u003c/p\u003e \u003cp\u003eA total of 676 breast cancer patients receiving NST from 2015 to 2021 at The First Affiliated Hospital of Nanjing Medical University were enrolled. Of these, 538 patients were eligible to enter our study. We used a computer to assign a random number to each patient, scrambled the number, and divided it into a training set and a validation set at a ratio of 7:3. Finally, 378 and 160 patients were respectively enrolled in the training and validation set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathologic Evaluation\u003c/h2\u003e \u003cp\u003eThe maximum diameter of breast tumor and the longest diameter of positive node were measured by ultrasound or MRI before and after NST. Whether ultrasound or MRI was used, the same examination was used to evaluate response both before and after NST. The radiological response rate of breast tumor was considered the reduction rate of the maximum diameter of the tumor after NST compared with the maximum diameter of the tumor before NST. The radiological response rate of positive node was the reduction rate of the longest diameter of positive node after NST compared with the longest diameter of positive node before NST. The response evaluation of breast tumor was measured according to RECIST (version 1.1) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The clinical and pathologic stage of primary tumor was assessed according to the eighth edition of the AJCC cancer staging system [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBefore NST, we performed a needle biopsy of the tumor for immunohistochemical analysis. We considered ER and PgR assays to be positive if there were at least 1% positive nuclear-stained cells in the tumor [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The tumors with an immunohistochemistry [IHC] score 3\u0026thinsp;+\u0026thinsp;or IHC2+/in situ hybridization [ISH]-positive were designated HER2 positive [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Ki67 expression\u0026thinsp;\u0026gt;\u0026thinsp;20% was considered as a high Ki67 level [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The patients primarily received three NST regimens, which included epirubicin and cyclophosphamide followed by taxanes (EC-T), epirubicin and cyclophosphamide followed taxanes and trastuzumab (EC-TH), and epirubicin and cyclophosphamide followed by taxanes, trastuzumab and pertuzumab (EC-THP). Breast pCR (ypT0/is) was defined as the absence of residual invasive carcinoma in the breast on postoperative pathology after NST, irrespective of in situ ductal carcinoma [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eFor the general characteristics of the patients, continuous variables are presented as means and standard deviations, and categorical variables are presented as frequencies and proportions. At first, the univariate logistic regression analysis was performed to screen out the variables associated with axillary pCR after NST. Then, variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in the multivariate logistic regression analysis. Odds ratios (ORs) and 95% CIs were also calculated.\u003c/p\u003e \u003cp\u003eThe final nomogram was developed by the variables that were statistically significant. After the model was established, the predictive ability of the model was determined through discrimination and calibration. The discrimination ability of the model was assessed by the receiver operating characteristic (ROC) curve area under the curve (AUC), which ranged from 0.5 to 1.0. The 0.5 value indicates a random chance and 1.0 indicates a perfect fit. The calibration ability was primarily evaluated using a calibration curve. All statistical analyses were performed using IBM SPSS Statistics 21.0 software (IBM Corporation, Armonk, NY, USA), GraphPad Prism version 9.0.0 (GraphPad Software, San Diego, California USA), and R version 4.0.5 software (The R Foundation for Statistical Computing, Austria, Vienna).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eThe study flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The clinicopathologic characteristics before and after NST for the patients in the training set, validation set, and total population are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of patients in the training set, validation set and total population.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining set\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;378 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation set\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;160 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;538 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186 (49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation of tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e267 (49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e271 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical tumor size (cT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259 (68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358 (66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum diameter of breast tumor (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore NST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter NST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiological response rate of breast tumor (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.4\u0026thinsp;\u0026plusmn;\u0026thinsp;30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.3\u0026thinsp;\u0026plusmn;\u0026thinsp;33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.7\u0026thinsp;\u0026plusmn;\u0026thinsp;31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse evaluation of breast tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete response (CR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial response (PR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250 (66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStable disease (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgressive disease (PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongest diameter of positive node (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore NST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter NST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiological response rate of positive node (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.9\u0026thinsp;\u0026plusmn;\u0026thinsp;39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.6\u0026thinsp;\u0026plusmn;\u0026thinsp;32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e339 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePgR status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e280 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67 expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333 (88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e469 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNST regimens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223 (59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e318 (59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC-TH(P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgeries of breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMastectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351 (92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (91.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e498 (92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumpectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast pCR (ypT0/is)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (78.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e417 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNST, neoadjuvant systemic therapy; ER, estrogen receptor; PgR, progesterone receptor; HER2, human epidermal growth factor receptor 2; EC-TH(P), epirubicin and cyclophosphamide followed by taxane, trastuzumab and pertuzumab; pCR, pathologic complete response; ypT0/is, the absence of residual invasive carcinoma in the breast on postoperative pathology after NST, irrespective of in situ ductal carcinoma\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean age of the 538 patients was 49.1 years. Premenopausal and postmenopausal patients accounted for approximately 50%. The proportion of left and right breast cancers were essentially equal (49.6% vs. 50.4%). There were the largest number of patients with cT1 and cT2 (75.2%). The mean maximum diameter of breast tumor was 39.4 mm before NST and 19.4 mm after NST. The mean radiological response rate of breast tumor was 50.7%. According to the response evaluation of breast tumor, only nine (1.7%) patients had progressive disease (PD) after NST. The mean longest diameter of positive node was 19.2 mm before NST and 11.6 mm after NST. The mean radiological response rate of positive node was 35.6%. ER status was positive in 339 (63.0%) patients, and PgR status was positive in 258 (48.0%) patients. HER2 status was positive in 205 (38.1%) patients. The vast majority of patients had high Ki67 expression (87.2%). With respect to the choice of NST, most HER2-negative patients received EC-T as the NST regimen. Of the 205 HER2-positive patients, greater than 188 patients received targeted therapy during NST. There were also a small number of HER2-positive patients who refused targeted therapy. Regarding the choice of surgery, 498 (92.6%) patients selected mastectomy, while only 40 (7.4%) patients chose lumpectomy. Finally, 121 (22.5%) patients achieved breast pCR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathologic Response to NST\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, 208 (38.7%) patients achieved axillary pCR, 121 (22.5%) patients achieved breast pCR, and 95 (18.0%) patients achieved total pCR among all 538 cN\u0026thinsp;+\u0026thinsp;patients after NST. Different molecular subtypes of breast cancer had different responses to NST (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). HR-/HER2\u0026thinsp;+\u0026thinsp;breast cancer patients exhibited the highest axillary pCR rate (62.2%), while HR+/HER2- patients had the lowest axillary pCR rate (22.3%). Additionally, regardless of the molecular subtypes of breast cancer, the axillary pCR rate was significantly higher compared with that of the breast pCR after NST (Supplementary Tables\u0026nbsp;1 and 2). A subgroup analysis was performed on the basis of different breast pathologies after surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Among 121 (22.5%) patients achieving breast pCR, 95 (78.5%) patients had simultaneous axillary pCR, whereas in 417 (77.5%) patients with breast non-pCR, only 113 (27.1%) patients achieved axillary pCR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSignificant Predictors for Axillary pCR\u003c/h2\u003e \u003cp\u003eThe results of univariate logistic regression analysis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. After NST, patients with a smaller maximum diameter of breast tumor (OR\u0026thinsp;=\u0026thinsp;0.954, 95% CI: 0.937\u0026ndash;0.971, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher radiological response rate of breast tumor (OR\u0026thinsp;=\u0026thinsp;8.740, 95% CI: 4.034\u0026ndash;18.935, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and smaller longest diameter of positive node (OR\u0026thinsp;=\u0026thinsp;0.951, 95% CI: 0.918\u0026ndash;0.985, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) were more likely to achieve axillary pCR. Compared with complete response (CR), patients with partial response (PR) or stable disease (SD) were less likely to achieve axillary pCR (OR\u0026thinsp;=\u0026thinsp;0.317, 95% CI: 0.163\u0026ndash;0.618, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 and OR\u0026thinsp;=\u0026thinsp;0.125, 95% CI: 0.055\u0026ndash;0.285, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For patients with a negative ER status (OR\u0026thinsp;=\u0026thinsp;0.354, 95% CI: 0.230\u0026ndash;0.545, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a negative PgR status (OR\u0026thinsp;=\u0026thinsp;0.392, 95% CI: 0.254\u0026ndash;0.604, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a positive HER2 status (OR\u0026thinsp;=\u0026thinsp;2.380, 95% CI: 1.550\u0026ndash;3.654, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the axillary pCR was more readily achieved. Patients who were treated with an EC-TH(P) regimen were more likely to achieve axillary pCR than EC-T regimen (OR\u0026thinsp;=\u0026thinsp;2.439, 95% CI: 1.558\u0026ndash;3.820, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared with mastectomy, patients choosing lumpectomy had a higher probability of achieving axillary pCR (OR\u0026thinsp;=\u0026thinsp;3.442, 95% CI: 1.502\u0026ndash;7.886, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). According to postoperative pathology results, patients with breast pCR also had a higher probability to achieve axillary pCR than those with breast non-pCR (OR\u0026thinsp;=\u0026thinsp;9.345, 95% CI: 5.281\u0026ndash;16.535, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate logistic regression analysis was performed to identify variables that could predict axillary pCR after NST (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The variables related to axillary pCR were the radiological response rate of breast tumor (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039), the longest diameter of positive node after NST (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), ER status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), HER2 status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) and breast pCR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eMultivariate logistic regression analysis of variables associated with axillary pCR in the training set (N\u0026thinsp;=\u0026thinsp;378).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiological response rate of breast tumor (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.531 (1.048\u0026ndash;6.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongest diameter of positive node after NST (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.956 (0.918\u0026ndash;0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.502 (0.306\u0026ndash;0.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.638 (1.004\u0026ndash;2.673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast pCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.395 (2.896\u0026ndash;10.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and Validation of the Nomogram\u003c/h2\u003e \u003cp\u003eA nomogram was constructed to predict the probability of achieving the axillary pCR after NST based on the results of multivariate logistic regression analysis, which included the baseline and efficacy indicators before and after NST (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe final nomogram was verified using a bootstrap method (n\u0026thinsp;=\u0026thinsp;1000). The AUC of this predictive model was 0.795 (95% CI: 0.747\u0026ndash;0.843) in the training set and 0.809 (95% CI: 0.738\u0026ndash;0.880) in the validation set, which indicates that the model has stronger predictive power (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). And there was no statistical difference between these two AUCs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.748, Delong\u0026rsquo;s test) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The calibration curve used to assess the goodness-of-fit of the nomogram exhibited good agreement between the predicted and actual probabilities of axillary pCR in the training set and the validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the axillary pCR rate (38.7%) was significantly higher than the breast pCR rate (22.5%) in all patients, which is consistent with other studies [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These results indicate that axillary pCR is more common after NST. Thus, cN\u0026thinsp;+\u0026thinsp;patients may be candidates for axillary de-escalation surgery. In a subgroup analysis, the axillary pCR rate of HR-/HER2\u0026thinsp;+\u0026thinsp;breast cancer patients was the highest (62.2%) among the different molecular subtypes of breast cancer. In contrast, the axillary pCR rate of HR+/HER2- patients was the lowest (22.6%), which was similar to that of another report (60% for HR-/HER2\u0026thinsp;+\u0026thinsp;and 18% for HR+/HER2-) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, it is still worth noting that since the HR+/HER2- subtype accounts for the highest proportion (approximately 70%) of breast cancer [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], the total number of patients who finally achieved axillary pCR after NST is not small. As a result, a predictive model for axillary pCR is necessary for all molecular subtypes of breast cancer treated with NST.\u003c/p\u003e \u003cp\u003eTo study the role of different indicators in predicting axillary pCR, enrolled patients were randomly assigned to training and validation sets at a ratio of 7:3. Based on the results of the multivariate logistic regression analysis, a final predictive model was constructed. Results of the univariate logistic regression analysis showed that most baseline indicators were not associated with axillary pCR, including age at diagnosis, menstrual status, and clinical tumor size. Efficacy indicators and some other baseline indicators were related to axillary pCR, so the multivariate logistic regression analysis was further conducted. The results indicated that some baseline indicators, including ER status and HER2 status, were associated with axillary pCR, which is consistent with other reports [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Different from previous findings, several efficacy indicators were found to be strongly associated with the axillary pCR, including the radiological response rate of breast tumor, the longest diameter of positive node after NST, and breast pCR. Results showed that the higher the radiological response rate of breast tumor after NST, the higher likelihood of achieving axillary pCR. Moreover, the smaller the longest diameter of positive node after NST, the higher probability of achieving axillary pCR. These two indicators were efficacy indicators that are closely associated with axillary pCR, which once again shows that the prediction of axillary pCR may focus not only on baseline indicators, but also on the efficacy indicators after NST.\u003c/p\u003e \u003cp\u003eIn this study, the radiological response rate of breast tumor was associated with axillary pCR, so it was reasonable that breast pCR could predict axillary pCR as an extremity of the radiological response rate. Since this was a retrospective study, all enrolled patients had already received surgery and the pathological data for breast pCR had been obtained. However, this data is missing when this model is applied to the prediction of axillary pCR prior to surgery. At this time, if the breast clinical complete response (cCR) is not considered in preoperative radiological examinations, the probability of breast pCR is very small, therefore, it may be directly judged as breast non-pCR. On the other hand, if the breast cCR is highly probable based on preoperative radiological examinations, the pathological information can also be obtained before surgery. In a study aiming to omit breast surgery, when using image-guided vacuum-assisted biopsy (VAB) to obtain at least six representative samples in patients with residual radiological abnormality or a tumor bed measuring 2 cm or smaller, the breast pCR can be accurately demonstrated (the false-negative rate was 3.2%) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, intraoperative frozen pathology of breast tumors may also be applied during breast surgery, which provides the data for breast pCR to predict axillary pCR.\u003c/p\u003e \u003cp\u003eIn contrast, the radiological response rate of positive node showed no correlation with axillary pCR. Unlike breast tumors, lymph nodes are inherently anatomical in size. The breast could achieve pCR when NST was highly effective, but the lymph nodes could only shrink back to their original size and would not disappear; thus, the radiological response rate of positive node could not achieve 100%. In this study, the mean longest diameter of positive node after NST was 11.6 mm, and the highest radiological response rate of positive node was 90%. This also suggests that evaluating the response of lymph nodes to NST may require the combination of other factors, such as the cortical thickness of the lymph nodes. As for the response evaluation of breast tumor, results showed that compared with PR and SD, patients achieving CR were more likely to achieve axillary pCR. While compared with PD, the predictive superiority of achieving CR was not demonstrated in this study. Since NST was effective, the number of PD patients was particularly small (1.7%), which affected the statistical results.\u003c/p\u003e \u003cp\u003eIn this study, a nomogram containing efficacy indicators after NST was constructed. The AUC of this predictive model was 0.795 and the calibration curve also showed good agreement between the predicted and actual probabilities of axillary pCR, which indicated that the model had a more accurate prediction ability of axillary pCR. In contrast, when only baseline indicators were included, the AUC that was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e was 0.705 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Delong\u0026rsquo;s test) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which was consistent with other report containing only baseline indicators [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The above evidence confirmed that the efficacy indicators are crucial for predicting axillary pCR.\u003c/p\u003e \u003cp\u003eOur study also had some limitations. First, it was a retrospective and single-center study, and the number of patients used to construct and validate the nomogram was relatively small. Therefore, a subgroup analysis was somewhat limited. Second, all patients included in this study were clinically node-positive (cN+) rather than pathologically node-positive (pN+), which partially affects the accuracy of the model. A new prospective study is planned to solve these problems.\u003c/p\u003e \u003cp\u003eIn conclusion, we constructed a nomogram to predict axillary pCR after NST in cN\u0026thinsp;+\u0026thinsp;patients with breast cancer. According to the results of the statistical analyses, efficacy indicators reflecting the therapeutic response of NST were found to be very important in predicting axillary pCR. Based on these efficacy indicators and some baseline indicators, we established a novel nomogram, which was validated and considered to be highly accurate in predicting axillary pCR. This predictive model may help surgeons to de-escalate or even omit axillary surgery for patients with axillary lymph node downstaging after NST in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003eWe would like to thank all the physicians for their actively cooperating with us in collecting patient information, thank all the patients and their families for allowing us to analyse their data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work was funded by Jiangsu Province Six Talents Summit Project (WSW-001), Chinese Society of Clinical Oncology Foundation (Y-sy2018-077, Y-JS2019-096), and Young Scholars Fostering Fund of the First Affiliated Hospital of Nanjing Medical University (PY2021038).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003eThe authors\u0026rsquo; contributions were as follows: Xiaoming Zha and Jue Wang conceptualized and designed the study. All authors completed the acquisition, analysis, and interpretation of the data. Xiaoming Zha and Jue Wang obtained the study funding. Wenjie Shi, Xiaofeng Huang and Jue Wang were responsible for the methodology. Xiaoming Zha and Jue Wang provided study supervision. Wenjie Shi drafted the original version of the manuscript. All authors critically revised drafts of the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Review Board (2022-SR-201).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e Due to the retrospective nature of the study, a need for informed consent was waived by the Ethics Review Board.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Goding Sauer A, Jemal A, Siegel RL (2019) Breast cancer statistics, 2019. CA Cancer J Clin 69(6):438\u0026ndash;451\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteenbruggen TG, van Ramshorst MS, Kok M, Linn SC, Smorenburg CH, Sonke GS (2017) Neoadjuvant Therapy for Breast Cancer: Established Concepts and Emerging Strategies. Drugs 77(12):1313\u0026ndash;1336\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorde LA, Somerfield MR, Carey LA, Crews JR, Denduluri N, Hwang ES, Khan SA, Loibl S, Morris EA, Perez A et al (2021) Neoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline. J Clin Oncol 39(13):1485\u0026ndash;1505\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiglietta F, Dieci MV, Griguolo G, Guarneri V (2021) Neoadjuvant approach as a platform for treatment personalization: focus on HER2-positive and triple-negative breast cancer. Cancer Treat Rev 98:102222\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, Bonnefoi H, Cameron D, Gianni L, Valagussa P et al (2014) Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384(9938):164\u0026ndash;172\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang JM, Leung JWT, Moy L, Ha SM, Moon WK (2020) Axillary Nodal Evaluation in Breast Cancer: State of the Art. Radiology 295(3):500\u0026ndash;515\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisher CS, Margenthaler JA, Hunt KK, Schwartz T (2020) The Landmark Series: Axillary Management in Breast Cancer. Ann Surg Oncol 27(3):724\u0026ndash;729\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoland MR, Al-Hilli Z (2021) Management of the axilla after neoadjuvant chemotherapy in breast cancer patients. Br J Surg 108(7):748\u0026ndash;749\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHack TF, Cohen L, Katz J, Robson LS, Goss P (1999) Physical and psychological morbidity after axillary lymph node dissection for breast cancer. J Clin Oncol 17(1):143\u0026ndash;149\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvens D, Hoe AL, Podd TJ, Hamilton CR, Taylor I, Royle GT (1992) Assessment of morbidity from complete axillary dissection. Br J Cancer 66(1):136\u0026ndash;138\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeoh EK, Denham JW, Davies SA, Spittle MF (1986) Primary breast cancer. Complications of axillary management. Acta Radiol Oncol 25(2):105\u0026ndash;108\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoughey JC, Suman VJ, Mittendorf EA, Ahrendt GM, Wilke LG, Taback B, Leitch AM, Kuerer HM, Bowling M, Flippo-Morton TS et al (2013) Sentinel lymph node surgery after neoadjuvant chemotherapy in patients with node-positive breast cancer: the ACOSOG Z1071 (Alliance) clinical trial. JAMA 310(14):1455\u0026ndash;1461\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoughey JC, Ballman KV, Le-Petross HT, McCall LM, Mittendorf EA, Ahrendt GM, Wilke LG, Taback B, Feliberti EC, Hunt KK (2016) Identification and Resection of Clipped Node Decreases the False-negative Rate of Sentinel Lymph Node Surgery in Patients Presenting With Node-positive Breast Cancer (T0-T4, N1-N2) Who Receive Neoadjuvant Chemotherapy: Results From ACOSOG Z1071 (Alliance). Ann Surg 263(4):802\u0026ndash;807\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin X, Jiang YZ, Chen S, Shao ZM, Di GH (2016) A Nomogram for Predicting the Pathological Response of Axillary Lymph Node Metastasis in Breast Cancer Patients. Sci Rep 6:32585\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVila J, Mittendorf EA, Farante G, Bassett RL, Veronesi P, Galimberti V, Peradze N, Stauder MC, Chavez-MacGregor M, Litton JF et al (2016) Nomograms for Predicting Axillary Response to Neoadjuvant Chemotherapy in Clinically Node-Positive Patients with Breast Cancer. Ann Surg Oncol 23(11):3501\u0026ndash;3509\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi HJ, Ryu JM, Kim I, Nam SJ, Kim SW, Yu J, Lee JE, Lee SK (2019) Nomogram for accurate prediction of breast and axillary pathologic response after neoadjuvant chemotherapy in node positive patients with breast cancer. Ann Surg Treat Res 96(4):169\u0026ndash;176\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujii T, Kogawa T, Wu J, Sahin AA, Liu DD, Chavez-MacGregor M, Giordano SH, Raghavendra A, Murthy RK, Tripathy D et al (2017) Nomogram to predict pathologic complete response in HER2-positive breast cancer treated with neoadjuvant systemic therapy. Br J Cancer 116(4):509\u0026ndash;514\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePu S, Wang K, Liu Y, Liao X, Chen H, He J, Zhang J (2020) Nomogram-derived prediction of pathologic complete response (pCR) in breast cancer patients treated with neoadjuvant chemotherapy (NCT). BMC Cancer 20(1):1120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228\u0026ndash;247\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiuliano AE, Connolly JL, Edge SB, Mittendorf EA, Rugo HS, Solin LJ, Weaver DL, Winchester DJ, Hortobagyi GN (2017) Breast Cancer-Major changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin 67(4):290\u0026ndash;303\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammond MEH, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, Fitzgibbons PL, Francis G, Goldstein NS, Hayes M et al (2010) American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol 28(16):2784\u0026ndash;2795\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolff AC, Hammond MEH, Allison KH, Harvey BE, Mangu PB, Bartlett JMS, Bilous M, Ellis IO, Fitzgibbons P, Hanna W et al (2018) Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update. J Clin Oncol 36(20):2105\u0026ndash;2122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoates AS, Winer EP, Goldhirsch A, Gelber RD, Gnant M, Piccart-Gebhart M, Th\u0026uuml;rlimann B, Senn HJ (2015) Tailoring therapies\u0026ndash;improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Annals of oncology: official journal of the European Society for Medical Oncology 26(8):1533\u0026ndash;1546\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Minckwitz G, Untch M, Blohmer J-U, Costa SD, Eidtmann H, Fasching PA, Gerber B, Eiermann W, Hilfrich J, Huober J et al (2012) Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol 30(15):1796\u0026ndash;1804\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837\u0026ndash;845\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo R, Su Y, Si J, Xue J, Yang B, Zhang Q, Chi W, Chen J, Chi Y, Shao Z et al (2020) A nomogram for predicting axillary pathologic complete response in hormone receptor-positive breast cancer with cytologically proven axillary lymph node metastases. Cancer 126(Suppl):3819\u0026ndash;3829\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim R, Chang JM, Lee H-B, Lee SH, Kim S-Y, Kim ES, Cho N, Moon WK (2019) Predicting Axillary Response to Neoadjuvant Chemotherapy: Breast MRI and US in Patients with Node-Positive Breast Cancer. Radiology 293(1):49\u0026ndash;57\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi HJ, Ryu JM, Kim I, Nam SJ, Kim SW, Yu J, Lee JE, Lee SK (2019) Prediction of axillary pathologic response with breast pathologic complete response after neoadjuvant chemotherapy. Breast Cancer Res Treat 176(3):591\u0026ndash;596\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamiei S, Simons JM, Engelen SME, Beets-Tan RGH, Classe J-M, Smidt ML (2021) : \u003cb\u003eAxillary Pathologic Complete Response After Neoadjuvant Systemic Therapy by Breast Cancer Subtype in Patients With Initially Clinically Node\u003c/b\u003e-\u003cb\u003ePositive Disease\u003c/b\u003e: \u003cb\u003eA Systematic Review and Meta\u003c/b\u003e-\u003cb\u003eanalysis\u003c/b\u003e. \u003cem\u003eJAMA Surg\u003c/em\u003e \u003cb\u003e156\u003c/b\u003e(6):e210891\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaks AG, Winer EP (2019) Breast Cancer Treatment: A Review. JAMA 321(3):288\u0026ndash;300\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JY, Park HS, Kim S, Ryu J, Park S, Kim SI (2015) Prognostic Nomogram for Prediction of Axillary Pathologic Complete Response After Neoadjuvant Chemotherapy in Cytologically Proven Node-Positive Breast Cancer. Med (Baltim) 94(43):e1720\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTasoulis MK, Lee H-B, Yang W, Pope R, Krishnamurthy S, Kim S-Y, Cho N, Teoh V, Rauch GM, Smith BD et al (2020) Accuracy of Post-Neoadjuvant Chemotherapy Image-Guided Breast Biopsy to Predict Residual Cancer. JAMA Surg 155(12):e204103\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, neoadjuvant systemic therapy, efficacy indicators, pCR, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-1597454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1597454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eNeoadjuvant systemic therapy (NST) could make some clinically node-positive (cN+) breast cancer patients achieve axillary pathologic complete response (pCR). This study aimed to identify the patients who are likely to achieve axillary pCR and help surgeons make surgical decisions of the axilla. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe cN+ breast cancer patients who received NST from 2015 to 2021 at The First Affiliated Hospital of Nanjing Medical University were enrolled. Univariate and multivariate logistic regression analyses were performed, and a nomogram was constructed based on the results of multivariate logistic regression analysis to predict the probability of axillary pCR and validated.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The\u003cstrong\u003e \u003c/strong\u003eaxillary pCR was achieved in 208 (38.7%) patients. Patients who had a higher radiological response rate of breast tumor (\u003cem\u003eP\u003c/em\u003e = 0.039), smaller longest diameter of positive node after NST (\u003cem\u003eP\u003c/em\u003e = 0.028), ER-negative status (\u003cem\u003eP\u003c/em\u003e = 0.006), HER2-positive status (\u003cem\u003eP\u003c/em\u003e = 0.048) and breast pCR (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) were more likely to achieve axillary pCR. The nomogram had an area under the receiver operating characteristic curve (AUC) of 0.795 (95% CI: 0.747–0.843), and the calibration curve showed good agreement.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eA nomogram was constructed to predict the axillary pCR of cN+ patients receiving NST based on baseline and efficacy indicators to assist surgeons in making surgical decisions of the axilla.\u003c/p\u003e","manuscriptTitle":"A Novel Nomogram Containing Efficacy Indicators to Predict Axillary Pathologic Complete Response after Neoadjuvant Systemic Therapy in Clinically Node-Positive Patients with Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-27 15:51:06","doi":"10.21203/rs.3.rs-1597454/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"248a6d2d-07d0-4fe6-bfbb-e8def297964d","owner":[],"postedDate":"May 27th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-05T20:12:20+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-27 15:51:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1597454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1597454","identity":"rs-1597454","version":["v1"]},"buildId":"re_ckhLnmML6MCF96OHNJ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}