Predictors of pathological complete response to neoadjuvant chemotherapy in HER2-positive breast cancer: development and validation of a clinical-inflammatory nomogram

Predictors of pathological complete response to neoadjuvant chemotherapy in HER2-positive breast cancer: development and validation of a clinical-inflammatory nomogram | 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 Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictors of pathological complete response to neoadjuvant chemotherapy in HER2-positive breast cancer: development and validation of a clinical-inflammatory nomogram XIAOLIU JIANG, ZHAOHUI HUANG, XINXIN WANG, JIE LONG, LU JIANG, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7500826/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 Background Pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) strongly predicts favorable prognosis in patients with breast cancer. However, significant gaps remain in identifying reliable predictors of pCR—particularly regarding inflammatory biomarkers. This study aimed to identify clinicopathological and inflammatory factors associated with pCR in human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients and develop a predictive nomogram. Methods We retrospectively analyzed 460 patients with HER2 + breast cancer who received NAC at Nanchang People's Hospital (January 2017–May 2024). Patients were randomly allocated to the training (n = 322) or testing (n = 138) cohorts at a ratio of 7:3. Variables with significant associations in the univariate analysis (P < 0.05) were included in the multivariate logistic regression. A nomogram incorporating independent predictors was validated for its discrimination, calibration, and clinical utility. Results The overall pCR rate was 47.2% (217/460). The pCR rates were significantly higher for those aged ≥ 50 years (53.4% vs < 50:38.7%), those with estrogen receptor (ER)- (54.6% vs ER+:39.1%), those with progesterone receptor (PR)- (52.0% vs PR+: 36.2%), those with HER2 IHC3+ (51.9% vs IHC2+/FISH+:26.2%), those with dual HER2 blockade (54.9% vs chemotherapy alone:15.9%), and those with high PLRs (≥ 206 vs < 206:61.0% vs 45.1%) (all P < 0.05). Univariate analysis of the training cohort revealed that age, ER status, PR status, HER2 status, NAC regimens, and the PLR were significant predictors. Multivariate analysis confirmed that age ≥ 50 years (OR = 1.789, 95% CI: 1.098–2.933, p = 0.021), HER2 IHC3 + status (OR = 2.734, 95% CI: 1.414–5.460, p = 0.003), dual HER2 blockade (OR = 6.483, 95% CI: 2.482–20.390, p < 0.001), and high PLR (OR = 2.121, 95% CI: 1.040–4.485, p = 0.043) were independent predictors. The nomogram demonstrated good discrimination (training AUC = 0.755; testing AUC = 0.708), satisfactory calibration (Hosmer–Lemeshow test: training P = 0.203, testing P = 0.459), and favorable net clinical benefit. Conclusion Age ≥ 50 years, HER2 IHC 3 + status, dual HER2 blockade, and high PLR independently predict pCR in patients with HER2 + breast cancer. The developed nomogram provides a clinically applicable tool for pCR prediction, which may aid in optimizing personalized NAC strategies for this patient population. breast cancer neoadjuvant chemotherapy pathological complete response inflammatory biomarkers nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Breast cancer is the most common malignancy among women worldwide and has the highest incidence and the second-highest mortality rate. Globally, its incidence has increased by 0.6–1% annually( 1 ). In China, breast cancer also constitutes a significant public health burden, with an annual incidence of approximately 60 per 100,000 women and a mortality rate of 11 per 100,000 women( 2 ). Among its subtypes, HER2 + breast cancer is particularly notable; it accounts for 20–25% of invasive cases and is highly aggressive( 3 , 4 ). For such subtypes, NAC has emerged as a key treatment strategy( 5 ). By reducing the tumor burden in the breast and axillary lymph nodes, NAC enables surgical downstaging. This allows eligible patients to undergo breast-conserving lumpectomy instead of mastectomy and may even exclude select early-stage HER2 + or triple-negative breast cancer patients who achieve excellent pathological responses from surgery entirely( 6 – 8 ). The key clinical benefits of NAC include diminished surgical extent and enhanced prognostic stratification, especially in patients with HER2 + breast cancer ( 9 ). Treatment response is the primary method for evaluating the efficacy of NAC, with pCR regarded as the sole standard( 10 ). pCR following NAC is strongly associated with improved long-term outcomes, including prolonged survival and reduced distant metastasis risk( 11 ). This association is particularly pronounced in HER2 + and triple-negative breast cancer subtypes( 12 ). Thus, developing robust methods to predict pCR before initiating NAC is pivotal for personalizing therapeutic regimens for patients with breast cancer. Multiple clinicopathological factors may influence the NAC response. In our previous study, the pCR rate varied significantly across molecular subtypes, with rates of 58.9% in HR-HER2 + patients, 40.7% in HR + HER2 + patients, 35.6% in HR-HER2- patients, and 9.8% in HR + HER2- patients( 13 ). Notably, a clinicopathological model combining age, T stage, nodal status, the Ki-67 index, and hormone receptor status has exhibited reliable predictive capacity for pCR( 14 , 15 ). Clinicopathological factors contribute substantially to predicting pCR following NAC in patients with breast cancer, and exploring their utility in patients with the HER2 + subtype is critical for clinical decision-making. Inflammation constitutes a driving force in tumorigenesis, affecting every stage of cancer development. It can promote tumor initiation and drive progression by inducing genomic instability, creating a protumorigenic microenvironment, and facilitating immune evasion, ultimately leading to malignant transformation( 16 , 17 ). Peripheral blood inflammatory markers serve as critical indicators of breast cancer progression and provide noninvasive prognostic insights( 18 ). In addition, inflammatory biomarkers—including the NLR, PLR, and LMR—demonstrate potential utility in predicting pCR following NAC for breast cancer patients. A lower pretreatment NLR was significantly inversely associated with pCR in the lymph nodes( 19 ). Moreover, lower pretherapeutic values of the NLR, PLR, and LMR were significantly correlated with pCR than nonpCR was( 20 ). Although inflammatory markers have shown predictive value, the results of different studies vary widely. Conflicting findings exist: Wang et al. ( 21 ) reported that higher pre-NLR, pre-PLR and post-LMR values were associated with a greater likelihood of achieving pCR. The discordant findings across studies underscore the need for further investigation to elucidate the predictive utility of inflammatory biomarkers. Given the heterogeneity in clinicopathological predictors and conflicting findings on inflammatory biomarkers in pCR prediction, the present study aimed to collect clinicopathological and inflammatory biomarker data from patients with HER2 + breast cancer following NAC and analyze the associations of age, tumor location, clinical stage, ER status, PR status, HER2 status, the Ki-67 index, the NAC regimen, the NLR, the PLR, the MLR and the NMR with pCR. Furthermore, a nomogram was constructed on the basis of the results of univariate and multivariate logistic regression analyses to predict the probability of pCR. Methods Study population This retrospective analysis involved 460 HER2 + breast cancer patients who received NAC at Nanchang People’s Hospital between January 2017 and March 2025. This study obtained ethical approval from the Ethics Committee of Nanchang People’s Hospital (Approval No. K-kt2024005). As the current study is retrospective and patient information was processed anonymously, written informed consent was not obtained from patients. All personal data were treated with strict confidentiality, and the research was carried out in accordance with the Declaration of Helsinki. The inclusion criteria were as follows. (a) Pathologically confirmed HER2 + invasive breast cancer by core needle biopsy. (b) Baseline imaging (US/CT/MRI) and blood collection ≤ 7 days before NAC. (c) Received all cycles of NAC. (d) All patients underwent surgery. (e) Comprehensive postoperative pathological assessment confirming pCR status. (f) Complete clinicopathological and inflammatory biomarker data records. The exclusion criteria were as follows. (a) Occult breast cancer or stage IV breast cancer. (b) Bilateral breast cancer or cancer history. (c) Transfusion within 1 month of pretreatment. Data collection Clinicopathological data and inflammatory marker data for all enrolled patients were retrieved from the medical records system of Nanchang People’s Hospital. The variables included age, tumor location, surgical procedure, histopathologic type, tumor grade, lymphovascular invasion, clinical T stage, nodal status, cTNM stage, ER/PR status, Ki-67 status, HER2 + status, NAC regimen, NLR, PLR, MLR, and NMR. Age was stratified using 50 years as the cutoff value, which is consistent with the findings of a previous study( 22 ). The clinical T stage, nodal status and cTNM stages were classified according to the 8th edition of the AJCC( 23 ). NAC regimens The NAC regimens were formulated on the basis of the National Comprehensive Cancer Network (NCCN) Guidelines for Breast Cancer and the Chinese Society of Clinical Oncology (CSCO) Guidelines for Breast Cancer of the corresponding year, combined with the clinical judgment of attending physicians and the preferences of patients. The NAC regimens administered included chemotherapy alone, chemotherapy combined with trastuzumab, and chemotherapy combined with both trastuzumab and pertuzumab. The chemotherapy regimens used were as follows: four cycles of anthracyclines and cyclophosphamide followed by four cycles of taxanes (AC-T); six cycles of taxanes and anthracyclines and cyclophosphamide (TAC); and six cycles of taxanes and carboplatin (Tcb). The following regimens were used for chemotherapy combined with trastuzumab: four cycles of anthracyclines and cyclophosphamide followed by four cycles of taxanes with trastuzumab (AC-TH) and six cycles of taxanes and carboplatin with trastuzumab (TcbH). The following chemotherapy regimens were combined with trastuzumab and pertuzumab: six cycles of taxanes with trastuzumab and pertuzumab (THP); four cycles of anthracyclines and cyclophosphamide followed by four cycles of taxanes with trastuzumab and pertuzumab (AC-THP); and six cycles of taxanes and carboplatin with trastuzumab and pertuzumab (TcbHP). Pathological assessment All patients were pathologically confirmed to have HER2 + breast cancer by core needle biopsy prior to NAC. Both the core needle biopsy specimens and surgical samples were subjected to histopathological and immunohistochemical evaluations. ER or PR positivity was defined as immunohistochemical staining showing that at least 1% of infiltrating tumor cells had positive expression( 24 ). According to the evaluation guidelines of the International Ki-67 in the Breast Cancer Working Group, the Ki-67 threshold was set at 30%( 25 ). HER2 positivity was defined as either immunohistochemical analysis showing 3 + protein overexpression or 2 + protein overexpression accompanied by HER2 gene amplification confirmed via in situ hybridization( 26 ). A pathological complete response was defined as the absence of invasive lesions in both the primary tumor and regional lymph nodes, with only in situ carcinoma remaining after treatment (ypT0/is ypN0), in accordance with the criteria outlined in the 8th Edition of the AJCC Cancer Staging Manual( 23 ). Inflammatory biomarkers Peripheral blood tests were performed within 1 week before NAC, and the values of inflammatory markers, including the NLR, PLR, MLR, and NMR, were recorded. The NLR, PLR, LMR, and NMR were calculated as the ratios of the neutrophil count to the lymphocyte count, the platelet count to the lymphocyte count, the lymphocyte count to the monocyte count, and the neutrophil count to the monocyte count, respectively (all cell counts ×10⁹/L). Receiver operating characteristic (ROC) curve analysis was applied to determine the optimal cutoff values for the NLR, PLR, MLR, and NMR via the package ‘pROC’ in RStudio. Values below each cutoff were categorized as "low", whereas those above were defined as "high". Nomogram development and validation The workflow for the development and validation of the model is illustrated in Fig. 1 . Patients were randomly assigned to the training cohort or testing cohort at a ratio of 7:3 via the ‘sample’ function in RStudio. Univariate logistic regression analysis was performed on patients in the training cohort to identify clinicopathological factors and inflammatory markers associated with pCR. Variables with p < 0.05 from the univariate logistic regression analysis were included in the multivariate regression analysis to further determine independent predictors of pCR (using the ‘glm’ function in RStudio). A nomogram was developed on the basis of the regression coefficients of each selected factor from multivariate regression analysis to quantitatively predict the probability of pCR in each patient receiving NAC (using the ‘nomogram’ function in the ‘rms’ package). The performance of the constructed nomogram was assessed in terms of discrimination, calibration, and clinical utility in both the training and testing cohorts. The area under the receiver operating characteristic (ROC) curve (AUC) for both the training and testing cohorts was calculated via the "pROC" package in RStudio, and the ROC curves were plotted accordingly. Calibration curves were generated to compare the agreement between the predicted probabilities and actual observed frequencies. For the training cohort, 1000 bootstrap resamplings were performed via the 'calibrate' function from the "rms" package, whereas for the testing cohort, the 'val.prob' function (from the "rms" package) was utilized. Decision curve analysis (DCA) curves for both the training and testing cohorts were plotted via the "rmda" package to evaluate the clinical net benefit of the model across different threshold probabilities. Statistical analysis Continuous variables are presented as the means ± standard deviations (SDs) if normally distributed. Categorical variables are expressed as frequencies (percentages, %). For continuous variables, significant differences were analyzed via Student’s t test (for normally distributed data) or the Mann–Whitney U test (for nonnormally distributed data). Categorical variables were compared via Fisher’s exact test or the chi-square test. In the training cohort, univariate and multivariate logistic regression analyses were conducted to identify the most valuable predictors. A two-tailed p value < 0.05 was considered statistically significant. All the statistical analyses and visualizations were performed via RStudio (version 4.5.0). Results Baseline characteristics of patients The baseline clinicopathological characteristics and inflammatory biomarkers of the 460 patients are summarized in Table 1 ( Table 1 is at the end of the text) . Overall, 217 of the 460 (47.2%) patients achieved pCR. The results indicated that there were significant differences in the pCR rate among patients stratified by age, ER status, PR status, HER2 status, NAC regimen, and PLR. For age stratification, the pCR rates were 38.7% in patients aged < 50 years and 53.4% in those aged ≥ 50 years (Fig. 2 A, p = 0.002). With respect to ER status, patients with ER- status had a pCR rate of 54.6%, whereas those with ER + status had a pCR rate of 39.1% (Fig. 2 B, p = 0.001), and a similar trend was observed for PR status—with pCR rates of 52.0% in patients with PR- status and 36.2% in those with PR + status (Fig. 2 C, p = 0.002). For HER2 status, a more pronounced difference was noted: patients with HER2 IHC2+/FISH + status had a pCR rate of 26.2%, whereas this rate reached 51.9% in those with HER2 IHC3 + status (Fig. 2 D, p < 0.001). In terms of NAC regimens, the pCR rates increased with the addition of targeted agents, with 15.9% for chemotherapy alone, 27.3% for chemotherapy + H, and 54.9% for chemotherapy + H + P (Fig. 2 E, p < 0.001). Finally, for the PLR, patients with a low PLR had a pCR rate of 45.1%, whereas those with a high PLR had a higher rate of 61.0% (Fig. 2 F, p = 0.023). Although differences in the pCR rate were also observed across subgroups stratified by histopathologic type, tumor grade, and lymphovascular invasion, these variables had a high proportion of missing values, rendering the results nonreferable. Table 1 Baseline characteristics of 460 patients who achieved pCR and non‑pCR. Characteristics Non-pCR,n(%) pCR,n(%) Total,n P value N 243(52.8) 217(47.2) 460 Age (years) 50 (26–76) 52 (25–73) 51(25–76) 0.002 < 50 119 (49.0) 75 (34.6) 194 ≥ 50 124 (51.0) 142 (65.4) 266 Laterality 0.873 Left 125 (51.4) 110 (50.7) 235 Right 118 (48.6) 107 (49.3) 225 Quadrant 0.547 Center 25 (10.3) 28 (12.9) 53 Upper outer 124 (51.0) 109 (50.2) 233 Lower outer 29 (11.9) 33 (15.2) 62 Upper inner 53 (21.8) 40 (18.4) 93 Lower inner 12 (4.9) 7 (3.2) 19 Surgical procedure 0.719 BCS + SLNB 8 (3.3) 9 (4.1) 17 BCS + ALND 14 (5.8) 14 (6.5) 28 MRM + SLNB 6 (2.5) 5 (2.3) 11 MRM + ALND 213 (87.7) 184 (84.8) 397 MRM + SLNB + ALND 2 (0.8) 5 (2.3) 7 Histopathologic type < 0.001 invasive carcinoma 55 (22.6) 198 (91.2) 253 invasive ductal carcinoma 174 (71.6) 16 (7.4) 190 invasive lobular carcinoma 4 (1.6) 0 (0.0) 4 others 10 (4.1) 3 (1.4) 13 Tumor grade < 0.001 1 7 (2.9) 1 (0.5) 8 2 108 (44.4) 29 (13.4) 137 3 78 (32.1) 11 (5.1) 89 Unknow 50 (20.6) 176 (81.1) 226 Lymphovascular invasion < 0.001 1+ 27 (11.1) 1 (0.5) 28 2+ 5 (2.1) 0 (0.0) 5 3+ 38 (15.6) 1 (0.5) 39 Unknow 173 (71.2) 215 (99.1) 388 cT 0.207 1 19 (7.8) 13 (6.0) 32 2 135 (55.6) 137 (63.1) 272 3 66 (27.2) 43 (19.8) 109 4 23 (9.5) 24 (11.1) 47 cN 0.708 Negative 20 (8.2) 20 (9.2) 40 Positive 223 (91.8) 197 (90.8) 420 cTNM 0.653 I 3 (1.2) 1 (0.5) 4 II 83 (34.2) 77 (35.5) 160 III 157 (64.6) 139 (64.1) 296 ER status 0.001 Negative 109 (44.9) 131 (60.4) 240 Positive 134 (55.1) 86 (39.6) 220 PR status 0.002 Negative 153 (63.0) 166 (76.5) 319 Positive 90 (37.0) 51 (23.5) 141 Ki-67 index 0.852 < 30% 52 (21.4) 48 (22.1) 100 ≥ 30% 191 (78.6) 169 (77.9) 360 HER2 status IHC2+/FISH+ 62 (25.5) 22 (10.1) 84 < 0.001 IHC3+ 181 (74.5) 195 (89.9) 376 NAC regimen < 0.001 Chemotherapy 37 (15.2) 7 (3.2) 44 Chemotherapy + H 48 (19.8) 18 (8.3) 66 Chemotherapy + H + P 158 (65.0) 192 (88.5) 350 NLR 0.161 Low 178 (73.3) 146 (67.3) 324 High 65 (26.7) 71 (32.7) 136 PLR Low 220 (90.5) 181 (83.4) 401 0.023 High 23 (9.5) 36 (16.6) 59 MLR Low 157 (64.6) 121 (55.8) 278 0.053 High 86 (35.4) 96 (44.2) 182 NMR Low 129 (53.1) 98 (45.2) 227 0.09 High 114 (46.9) 119 (54.8) 233 BCS, breast conserving surgery; MRM, modified radical mastectomy; SLNB, sentinel lymph node biopsy; ALND, axillary lymph node dissection; ER, estrogen receptor; PR, progesterone receptor; pCR, pathological complete response; HER2, human epidermal growth factor receptor2. IHC, immunohistochemistry; FISH, Fluorescence in situ hybridization; H, Trastuzumab; P, Pertuzumab; NAC, neoadjuvant chemotherapy; NLR, neutrophil to lymphocyte ratio, Low(NLR<2.68), High(NLR ≥ 2.68); PLR, platelet to lymphocyte ratio, Low(PLR<206.0),High(PLR ≥ 206.0); MLR, monocyte to lymphocyte ratio, Low(MLR<0.26),High(MLR ≥ 0.26); NMR, neutrophil to monocyte ratio, Low(NLR<9.32),High(NLR ≥ 9.32). Cutoff values of inflammatory biomarkers Figure 3 presents the distribution of inflammatory biomarkers between the pCR group and the nonpCR group. No significant differences were observed in the uncategorized NLR, PLR, MLR, or NMR between the two groups (Figs. 3 A–D, all p values > 0.05). The optimal cutoff values for the four inflammatory ratios were as follows: the NLR was 2.675 (Fig. 3 E, AUC = 0.520; 95% confidence interval [95% CI]: 0.467–0.573; sensitivity = 0.327; specificity = 0.733), the PLR was 206 (Fig. 3 F, AUC = 0.528; 95% CI: 0.475–0.581; sensitivity = 0.166; specificity = 0.905), the MLR was 0.257 (Fig. 3 G, AUC = 0.504; 95% CI: 0.450–0.557; sensitivity = 0.442; specificity = 0.646), and the NMR was 9.316 (Fig. 3 H, AUC = 0.531; 95% CI: 0.479–0.584; sensitivity = 0.548; specificity = 0.531). Characteristics of the training and testing cohorts The 460 patients were randomly allocated into the training cohort (n = 322) or testing cohort (n = 138) at a 7:3 ratio. The characteristics of the training cohort and testing cohort are presented in Table 2 ( Table 2 is at the end of the text) . The distributions of all clinicopathological factors and inflammatory biomarkers were similar between the two cohorts, with no significant differences observed. This confirms that the random allocation strategy employed in the present study was effective. Table 2 Baseline characteristics of patients in different cohort Characteristics Training cohort,n(%) Testing cohort,n(%) Total,n(%) P value N 322 138 460 Age (years) 50 (25–76) 53 (27–74) 51 (25–76) 0.058 <50 145 (45.0) 49 (35.5) 194 (42.2) ≥ 50 177 (55.0) 89 (64.5) 266 (57.8) Laterality 0.919 Left 164 (50.9) 71 (51.4) 235 (51.1) Right 158 (49.1) 67 (48.6) 225 (48.9) Quadrant 0.209 Center 41 (12.7) 12 (8.7) 53 (11.5) Upper outer 169 (52.5) 64 (46.4) 233 (50.7) Lower outer 43 (13.4) 19 (13.8) 62 (13.5) Upper inner 57 (17.7) 36 (26.1) 93 (20.2) Lower inner 12 (3.7) 7 (5.1) 19 (4.1) Surgical procedure 0.138 BCS + SLNB 12 (3.7) 5 (3.6) 17 (3.7) BCS + ALND 24 (7.5) 4 (2.9) 28 (6.1) MRM + SLNB 8 (2.5) 3 (2.2) 11 (2.4) MRM + ALND 271 (84.2) 126 (91.3) 397 (86.3) MRM + SLNB + ALND 7 (2.2) 0 (0.0) 7 (1.5) Histopathologic type 0.108 invasive carcinoma 171 (53.1) 82 (59.4) 253 (55.0) invasive ductal carcinoma 140 (43.5) 50 (36.2) 190 (41.3) invasive lobular carcinoma 1 (0.3) 3 (2.2) 4 (0.9) others 10 (3.1) 3 (2.2) 13 (2.8) Tumor grade 0.662 1 6 (1.9) 2 (1.4) 8 (1.7) 2 95 (29.5) 42 (30.4) 137 (29.8) 3 67 (20.8) 22 (15.9) 89 (19.3) Unknow 154 (47.8) 72 (52.2) 226 (49.1) Lymphovascular invasion 0.710 1+ 17 (5.3) 11 (8.0) 28 (6.1) 2+ 4 (1.2) 1 (0.7) 5 (1.1) 3+ 28 (8.7) 11 (8.0) 39 (8.5) Unknow 273 (84.8) 115 (83.3) 388 (84.3) cT 0.437 1 19 (5.9) 13 (9.4) 32 (7.0) 2 190 (59.0) 82 (59.4) 272 (59.1) 3 77 (23.9) 32 (23.2) 109 (23.7) 4 36 (11.2) 11 (8.0) 47 (10.2) cN 0.470 Negative 30 (9.3) 10 (7.2) 40 (8.7) Positive 292 (90.7) 128 (92.8) 420 (91.3) cTNM 0.444 I 4 (1.2) 0 (0.0) 4 (0.9) II 109 (33.9) 51 (37.0) 160 (34.8) III 209 (64.9) 87 (63.0) 296 (64.3) ER status 0.541 Negative 171 (53.1) 69 (50.0) 240 (52.2) Positive 151 (46.9) 69 (50.0) 220 (47.8) PR status 0.774 Negative 222 (68.9) 97 (70.3) 319 (69.3) Positive 100 (31.1) 41 (29.7) 141 (30.7) Ki-67 index > 0.999 < 30% 70 (21.7) 30 (21.7) 100 (21.7) ≥ 30% 252 (78.3) 108 (78.3) 360 (78.3) HER2 status 0.562 IHC2+/FISH+ 61 (18.9) 23 (16.7) 84 (18.3) IHC3+ 261 (81.1) 115 (83.3) 376 (81.7) NAC regimen 0.232 Chemothrapy 31 (9.6) 13 (9.4) 44 (9.6) Chemothrapy + H 52 (16.1) 14 (10.1) 66 (14.3) Chemothrapy + H + P 239 (74.2) 111 (80.4) 350 (76.1) NLR 0.858 Low 226 (70.2) 98 (71.0) 324 (70.4) High 96 (29.8) 40 (29.0) 136 (29.6) PLR 0.411 Low 278 (86.3) 123 (89.1) 401 (87.2) High 44 (13.7) 15 (10.9) 59 (12.8) MLR 0.934 Low 195 (60.6) 83 (60.1) 278 (60.4) High 127 (39.4) 55 (39.9) 182 (39.6) NMR 0.699 Low 157 (48.8) 70 (50.7) 227 (49.3) High 165 (51.2) 68 (49.3) 233 (50.7) BCS, breast conserving surgery; MRM, modified radical mastectomy; SLNB, sentinel lymph node biopsy; ALND, axillary lymph node dissection; ER, estrogen receptor; PR, progesterone receptor; pCR, pathological complete response; HER2, human epidermal growth factor receptor2.IHC, immunohistochemistry; FISH, Fluorescence in situ hybridization; H, Trastuzumab; P, Pertuzumab; NAC, neoadjuvant chemotherapy; NLR, neutrophil to lymphocyte ratio, Low(NLR<2.68),High(NLR ≥ 2.68); PLR, platelet to lymphocyte ratio, Low(PLR<206.0),High(PLR ≥ 206.0); MLR, monocyte to lymphocyte ratio, Low(MLR<0.26),High(MLR ≥ 0.26); NMR, neutrophil to monocyte ratio, Low(NLR<9.32),High(NLR ≥ 9.32). Univariate and multivariate analyses As shown in Table 3 ( Table 3 is at the end of the text) , the results of univariate logistic regression analysis in the training cohort indicated that HER2 + breast cancer patients aged ≥ 50 years (OR = 1.778; 95% CI: 1.140–2.774; p = 0.012), ER- (ER+, OR = 0.514; 95% CI: 0.329–0.801; p = 0.004), PR- (PR+, OR = 0.496; 95% CI: 0.305–0.807; p = 0.005), HER2 IHC3+ (OR = 2.860; 95% CI: 1.554–5.264; p = 0.001), the NAC regimen of chemotherapy + H + P (OR = 6.750; 95% CI: 2.507–18.179; p = 0.001), or high PLR (OR = 2.112; 95% CI: 1.094–4.077; p = 0.027) were more likely to achieve pCR. These variables were incorporated into the multivariate logistic regression analysis, and the results demonstrated that age (age ≥ 50 years; OR = 1.789; 95% CI: 1.098–2.933; p = 0.021), HER2 status (IHC3+; OR = 2.734; 95% CI: 1.414–5.460; p = 0.003), NAC regimen (chemotherapy + H + P; OR = 6.483; 95% CI: 2.482–20.390; p < 0.001), and PLR (high; OR = 2.121; 95% CI: 1.040–4.485; p = 0.043) were independent predictors of pCR in patients with HER2 + breast cancer after NAC. Table 3 Univariate and multivariate logistic regression analysis of pCR after NAC Characteristics Univariate Multivariate Odds Radio (95%CI) P value Odds Radio (95%CI) P value Age (years) <50 Reference Reference ≥ 50 1.778(1.140–2.774) 0.012 1.789(1.098–2.933) 0.021 Laterality Left Reference Right 1.022(0.660–1.583) 0.923 Quadrant Center Reference Upper outer 0.759(0.383–1.503) 0.428 Lower outer 0.751(0.319–1.771) 0.513 Upper inner 0.834(0.374–1.864) 0.658 Lower inner 0.617(0.168–2.267) 0.467 cT 1 Reference 2 2.167(0.791–5.939) 0.133 3 1.625(0.559–4.726) 0.373 4 2.709(0.841–8.723) 0.095 cN Negative Reference Positive 0.582(0.271–1.250) 0.165 cTNM I Reference II 2.638(0.267–26.160) 0.408 III 2.860(0.293–27.942) 0.367 ER status Negative Reference Reference Positive 0.514(0.329–0.801) 0.004 0.601(0.316–1.136) 0.118 PR status Negative Reference Reference Positive 0.496(0.305–0.807) 0.005 0.869(0.425–1.783) 0.699 Ki-67 index < 30% Reference ≥ 30% 0.773(0.455–1.314) 0.342 HER2 status IHC2+/FISH+ Reference Reference IHC3+ 2.860(1.554–5.264) 0.001 2.734(1.414–5.460) 0.003 NAC regimen Chemothrapy Reference Reference Chemothrapy + H 1.916(0.615–5.970) 0.263 1.589(0.509–5.618) 0.443 Chemothrapy + H + P 6.750(2.507–18.179) 0.001 6.483(2.482–20.390) < 0.001 NLR Low Reference High 1.354(0.839–2.185) 0.216 PLR Low Reference Reference High 2.112(1.094–4.077) 0.027 2.121(1.040–4.485) 0.043 MLR Low Reference High 1.316(0.841–2.060) 0.231 NMR Low Reference High 1.425(0.919–2.211) 0.115 ER, estrogen receptor; PR, progesterone receptor; pCR, pathological complete response; HER2, human epidermal growth factor receptor2.IHC, immunohistochemistry; FISH, Fluorescence in situ hybridization; H, Trastuzumab; P, Pertuzumab; NAC, neoadjuvant chemotherapy; NLR, neutrophil to lymphocyte ratio, Low(NLR<2.68),High(NLR ≥ 2.68); PLR, platelet to lymphocyte ratio, Low(PLR<206.0),High(PLR ≥ 206.0); MLR, monocyte to lymphocyte ratio, Low(MLR<0.26),High(MLR ≥ 0.26); NMR, neutrophil to monocyte ratio, Low(NLR<9.32), High(NLR ≥ 9.32). Development of the nomogram As shown in Fig. 4 and Table S1 (Table S1 is at the end of the text) , on the basis of the regression coefficients of the multivariate logistic regression model, the variables included in the model—age, ER status, PR status, HER2 status, NAC regimen, and PLR—were weighted to develop a nomogram using the aforementioned predictors. This nomogram was designed to quantitatively predict the probability of pCR in each patient with HER2 + breast cancer who received NAC. Each value of every variable was assigned a corresponding score on the scale; the scores of all the variables were summed to obtain a total score, and the probability value corresponding to this total score represented the predicted pCR probability for each patient. Validation of the nomogram The performance of the nomogram was evaluated in the training and testing cohorts, with assessments focusing on three key aspects: discrimination, calibration, and clinical utility. The nomogram demonstrated good discriminative ability in the training cohort (AUC = 0.755, 95% CI: 0.702–0.808; cutoff value = 0.569; sensitivity = 0.675; specificity = 0.756; Fig. 5 A), with maintained performance in the testing cohort (AUC = 0.708, 95% CI: 0.622–0.794; cutoff value = 0.503; sensitivity = 0.778; specificity = 0.547; Fig. 5 D). The calibration curves revealed strong model reliability, with a slope of 1 (Brier = 0.203; HL p = 0.203; Fig. 5 B) in the training cohort and a slope of 0.93 (Brier = 0.218; HL p = 0.459; Fig. 5 E). DCA confirmed its clinical utility, yielding superior net benefit compared with default strategies across threshold probabilities of 21–83% (training cohort) and 0–70% (testing cohort), with peaks at 47% and 45%, respectively (Figs. 5 C and 5 F). These results validate the nomogram’s robustness in predicting the probability of pCR and guiding NAC decision-making. Discussion A pathological complete response is the standard for evaluating the efficacy of NAC in patients with breast cancer. For patients with HER2 + breast cancer, achieving pCR after NAC is associated with favorable long-term prognosis( 12 ). Therefore, exploring predictors of pCR is conducive to guiding individualized treatment for patients with HER2 + breast cancer. This study explored the predictive value of clinicopathological factors and inflammatory biomarkers for the efficacy of NAC in patients with HER2 + breast cancer through retrospective analysis. In the present study, age, HER2 status, the NAC regimen, and the PLR were identified as independent predictors of pCR in patients with HER2 + breast cancer after NAC. Specifically, patients aged ≥ 50 years, with HER2 IHC3 + status, treated with the NAC regimen of dual HER2 blockade, or with high PLR were more likely to achieve pCR. A nomogram integrating clinicopathological factors and inflammatory indicators for predicting pCR was developed on the basis of the results of univariate and multivariate logistic regression analyses. Age is a crucial prognostic factor in breast cancer patients. Younger breast cancer patients have a greater risk of recurrence and mortality than older breast cancer patients do( 27 ). Additionally, different age groups of breast cancer patients have different pCR rates after NAC. Among patients with triple-negative breast cancer, younger patients aged ≤ 40 years had higher pCR rates (52% vs 35% in those aged 41–60 years and 29% in those aged ≥ 61 years), likely because they have a greater proportion of BRCA carriers and TIL-rich tumors, which are associated with enhanced chemosensitivity( 28 ). Similarly, another retrospective study confirmed that age influences the pCR rate; in HR+/HER2- breast cancer, patients aged < 50 years had a higher axillary lymph node pCR rate than those aged ≥ 50 years did (31.8% vs 17.7%) ( 29 ). However, the effect of age on pCR was not significant in HER2 + patients. Specifically, Li et al. ( 30 ) reported that in the HR-/HER2 + subtype, older patients presented a higher pCR rate than younger patients did (33.2% vs 26.1%), whereas no significant difference was noted in the HR+/HER2 + subtype of breast cancer. In our study, age was identified as an independent predictor of pCR in patients with HER2 + breast cancer after NAC. Unlike those with HR+/HER2- and TNBC subtypes, patients aged ≥ 50 years with HER2 + breast cancer were more likely to achieve pCR than were those aged < 50 years (53.4% vs 38.7%). This finding is consistent with the results of Li’s study; however, the increase in the pCR rate in the older group compared with the younger group was more pronounced in our study, which may be attributed to the different age cutoff values used. Another factor contributing to the difference in pCR rates is that the older group had a greater proportion of patients with HR- and Ki-67 ≥ 30%. The transmembrane tyrosine kinase receptor HER2 is associated with the growth, differentiation, and angiogenesis of breast cancer cells, conferring aggressive biological behavior to tumors and resulting in poor prognosis( 31 ). Clinically, chemotherapy combined with dual antibody therapy has become the standard preoperative or postoperative treatment for early-stage HER2 + breast cancer, with a 3-year treatment survival rate exceeding 90%( 5 , 32 ). The efficacy of tyrosine kinase inhibitors may be associated with the expression level of the HER2 protein. Compared with patients with HER2 IHC 2+/FISH + results, those with HER2 IHC 3 + results exhibit superior efficacy( 33 , 34 ). Similarly, a retrospective study demonstrated that among HER2 + breast cancer patients receiving NAC, those with HER2 IHC 3 + status had higher unadjusted pCR rates in the breast (54% vs. 22%) and lymph nodes (69% vs. 37%) than did those with HER2 IHC 2+/FISH + status( 35 ). In our study, the pCR rate in patients with HER2 IHC 3 + status was significantly greater than that in patients with HER2 IHC 2+/FISH + status (51.6% vs. 26.2%). Multivariate logistic regression analysis revealed that HER2 status was an independent predictor of pCR, which was consistent with the aforementioned study results, further confirming that HER2 expression status is a robust predictive biomarker for pCR in HER2 + breast cancer patients. The NAC regimen, as the most critical factor influencing the pCR rate following NAC, is an independent predictor of pCR. In the present study, the pCR rates of chemotherapy alone, chemotherapy + H, and chemotherapy + H + P were 15.9%, 27.3%, and 54.9%, respectively. These data clearly demonstrate a stepwise increase in pCR rates with the addition of targeted agents (from single H to dual H + P), which aligns with findings from previous key trials. In the NeoSphere trial, the pCR rate of dual-target therapy combined with docetaxel reached 45.8%, which was significantly greater than the 29% reported in the chemotherapy plus single-target therapy group( 36 ). Similarly, in the Asian population, the PEONY trial confirmed this trend: the pCR rate of the NAC regimen with dual-target therapy combined with docetaxel was significantly higher than that of the single-target therapy group (39.3% vs. 21.8%)( 37 ). Collectively, our findings, together with data from the NeoSphere and PEONY trials, consistently confirm that the pCR rate of HER2 + breast cancer patients treated with NAC increases with the addition of targeted agents. In support of the pivotal role of NAC regimens, in our nomogram, the NAC regimen contributed the most to predicting the probability of pCR, with the score for "chemotherapy + H + P" reaching 100—significantly higher than that of other predictive factors. Inflammation can promote cancer initiation and progression by facilitating tumor cell proliferation, invasion, and metastasis, and it can also suppress the antitumor immune response by altering the tumor microenvironment( 38 ). Among the cellular components involved in inflammatory responses, platelets play pivotal roles in tumor progression. They can release growth factors, cytokines, and proangiogenic factors to promote cancer cell proliferation, survival, and tumor angiogenesis( 39 ). Additionally, platelets assist cancer cells in adhering to the vascular endothelium, extravasating from vessels to form distant metastases, and evading the body’s immune attack via interactions with them ( 40 ). In contrast, lymphocytes—key effector cells of the immune system—reflect the body’s immune status and play crucial roles in cancer immunosurveillance and antitumor therapy( 41 ). Notably, the ratios of different inflammatory factors, such as the NLR, PLR, and LMR, are positively correlated with tumor risk( 42 ). The predictive value of the PLR for the efficacy of NAC in patients with breast cancer remains controversial. A retrospective study by Hu et al.( 43 ) found that in patients with luminal B (HER2−) breast cancer, an elevated PLR was associated with a lower pCR rate after NAC (15.8% in the low PLR group vs. 9.2% in the high PLR group; P = 0.027). Similarly, another retrospective study reported that patients with low PLRs ( 181.7) (68.6% vs. 33.4%; P < 0.001)( 44 ). Notably, our findings contradict those of the above studies. Using a PLR cutoff value of 260, we observed that the pCR rate was significantly greater in patients with high PLRs than in those with low PLRs (61% vs. 45.1%; p = 0.023). Additionally, the PLR was identified as an independent predictor of pCR. Consistent with our results, two recent studies have also concluded that the PLR can serve as an independent predictor of pCR, with a higher pCR rate in patients with a high PLR than in those with a low PLR( 21 , 45 ). Our study and previous research revealed an association between the PLR and the pCR rate. However, substantial discrepancies exist in terms of PLR cutoff values and results. Thus, additional studies are still needed to clarify the predictive value of the PLR. Although the ER and PR statuses were significantly associated with pCR in the univariate analysis, they were not retained as independent predictors in the final multivariate model. This may be attributed to the fact that the predictive information conveyed by ER/PR status was captured by stronger covariates in the model, particularly HER2 IHC status and the dual HER2 blockade regimen—a finding that is consistent with the notion that HER2-driven signaling and targeted therapy exert more dominant effects on the NAC response in HER2 + breast cancer than does hormone receptor status( 46 ). Finally, we developed a nomogram to predict pCR by integrating clinicopathological factors and inflammatory biomarkers. This model exhibited good discriminative ability, with an AUC of 0.755 in the training cohort and 0.708 in the testing cohort. The calibration curves further confirmed strong model reliability. The training cohort had a slope of 1 (Brier score = 0.203; HL test, p = 0.203), whereas the testing cohort had a slope of 0.93 (Brier score = 0.218; HL test, p = 0.459). DCA verified the nomogram’s clinical utility. Across threshold probabilities of 21–83% (training cohort) and 0–70% (testing cohort), it achieved a superior net benefit compared with default strategies, with peak net benefits observed at 47% and 45%, respectively. Notably, the nomogram maintained favorable performance in the validation cohort, indicating adequate robustness and generalizability. In practice, each variable in the nomogram is assigned a corresponding score. By summing these scores, clinicians can calculate the probability of an individual patient achieving pCR after NAC. This personalized probability estimation may ultimately assist clinicians in optimizing individualized treatment decisions for patients with HER2 + breast cancer. This study has several limitations. First, as a retrospective study, it is inherently subject to inherent biases and uncontrollable confounding factors. Second, the study was based on single-center data with a limited sample size—particularly in the validation cohort—and the model lacked independent data from other centers for external validation. Third, owing to insufficient data, the model did not include potential predictive factors such as tumor grade, lymphovascular invasion, or tumor-infiltrating lymphocytes. Fourth, the cutoff values for inflammatory markers were determined on the basis of the ROC curve of the study population and thus require external validation. Fifth, long-term survival data were not available. Conclusion This study identified age ≥ 50 years, HER2 IHC 3 + status, dual HER2 blockade regimen, and high PLR as independent predictors of pCR following NAC in patients with HER2 + breast cancer. On the basis of these independent predictors, we successfully developed and internally validated a novel nomogram for HER2 + breast cancer. This nomogram exhibits robust predictive performance, good calibration, and favorable clinical utility for individualized pCR prediction, which may ultimately assist clinicians in optimizing treatment decision-making for patients undergoing NAC. Declarations Acknowledgments Not applicable. Funding The present study was supported by grants from the National Natural Science Foundation of China (grant no. 82060482) and the Natural Science Foundation of Jiangxi Province (grant no. 20171BAB205057). Availability of data and materials The data generated in the present study may be requested from the corresponding author. Authors ’ contributions JD and YC contributed to the conception and design of the manuscript. XJ, ZH, XW, JL, and LJ were responsible for the acquisition, analysis and interpretation of the data. XJ and JD edited, drafted and wrote the manuscript. All the authors confirm the authenticity of all the raw data and read and approved the final manuscript. Ethics approval and consent to participate The present study was approved by the review board ethics committee of Nanchang People’s Hospital (approval no. K-kt2024005; Nanchang, China). The requirement for patient approval or written informed consent was waived because of the retrospective nature of the study. Patient consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74:12–49. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024;4:47–53. Loibl S, Gianni L. HER2-positive breast cancer. Lancet. 2017;389:2415–29. Schlam I, Swain SM. HER2-positive breast cancer and tyrosine kinase inhibitors: the time is now. NPJ Breast Cancer. 2021;7:56. Gradishar WJ, Moran MS, Abraham J, et al. Breast Cancer, Version 3.2024, NCCN Clinical Practice Guidelines in Oncology. 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06:49:51","extension":"xml","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":189393,"visible":true,"origin":"","legend":"","description":"","filename":"ade32ed8969146a4862e31a00e08cd661structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7500826/v1/4043ecba171552a2ef8be432.xml"},{"id":92473088,"identity":"0dbbf8a6-3e09-4374-92e8-fb3e17a7ad15","added_by":"auto","created_at":"2025-09-30 06:57:51","extension":"html","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196172,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7500826/v1/0258f168405c382389cf3c0a.html"},{"id":92471450,"identity":"dfeed8f2-489a-4c8e-b024-2f4ea8cbd802","added_by":"auto","created_at":"2025-09-30 06:49:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53128,"visible":true,"origin":"","legend":"\u003cp\u003ePatient selection and flow diagram for prediction model development and validation\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7500826/v1/dd0d3f59a1394453fc7d524e.png"},{"id":92471453,"identity":"7d692805-a7f6-4623-b0b9-fcfb16ad1c3c","added_by":"auto","created_at":"2025-09-30 06:49:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":253131,"visible":true,"origin":"","legend":"\u003cp\u003epCR rates stratified by Age, ER, PR, HER2 status, NAC regimen, and PLR. (A) pCR rates across age groups. (B) pCR rates by ER status. (C) pCR rates by PR status. (D) pCR rates byHER2 status. (E) pCR rates by NAC regimen. (F) pCR rates by PLR status. *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ns, not significant.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7500826/v1/7b9c0518fefb5958a18bbe4b.png"},{"id":92471454,"identity":"40adf381-dedb-4990-ba0a-8c12aa46240f","added_by":"auto","created_at":"2025-09-30 06:49:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226434,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of inflammatory biomarkers stratified by pathological response and ROC curves of these biomarkers. (A) Distribution of neutrophil-to-lymphocyte ratio (NLR); (B) Distribution of platelet-to-lymphocyte ratio (PLR); (C) Distribution of monocyte-to-lymphocyte ratio (MLR); (D) Distribution of neutrophil-to-monocyte ratio (NMR); (E) ROC curve of NLR; (F) ROC curve of PLR; (G) ROC curve of MLR; (H) ROC curve of NMR. P-values were derived from the Wilcoxon rank-sum test; ns, no significant.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7500826/v1/8a7254fcfe2b544f69abcaac.png"},{"id":92473080,"identity":"0798cfc0-a49a-4235-aaa1-b83d0de82a7c","added_by":"auto","created_at":"2025-09-30 06:57:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":115134,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting pCR probability.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7500826/v1/63eea1192657c3ecb639514b.png"},{"id":92473081,"identity":"c6d4e688-a1a9-43e0-8b05-1642b0cb8401","added_by":"auto","created_at":"2025-09-30 06:57:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":404905,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the nomogram for predicting pCR following NAC. (A) ROC curve of the nomogram in the training cohort. (B) Calibration curve of the nomogram in the training cohort (Hosmer-Lemeshow test, p=0.203, brier score=0.203, intercept＜0.0001, slope=1). (C) Decision curve analysis for the nomogram in the training cohort (Net benefit superiority at 21-83% thresholds, peak:47%). (D) ROC curve of the nomogram in the testing cohort. (E) Calibration curve of the nomogram in the testing cohort (Hosmer-Lemeshow test, p=0.459, brier score=0.218, intercept=-0.203, slope=0.930). (F) Decision curve analysis for the nomogram in the testing cohort (Net benefit at 0-70% thresholds, peak:45%).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7500826/v1/721a3139dbd8d3a1ff624c80.png"},{"id":93754570,"identity":"8469a0a8-31ce-48d5-8c6b-cf4b5645abcb","added_by":"auto","created_at":"2025-10-17 08:32:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2515102,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7500826/v1/f4ebe10d-0f78-41d3-911a-349aa153644d.pdf"},{"id":92473079,"identity":"54153f87-ba86-4b3a-87ee-7b7ab50d11bd","added_by":"auto","created_at":"2025-09-30 06:57:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16187,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7500826/v1/66c30dba8b0da0d66c0c9463.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePredictors of pathological complete response to neoadjuvant chemotherapy in HER2-positive breast cancer: development and validation of a clinical-inflammatory nomogram\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most common malignancy among women worldwide and has the highest incidence and the second-highest mortality rate. Globally, its incidence has increased by 0.6\u0026ndash;1% annually(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In China, breast cancer also constitutes a significant public health burden, with an annual incidence of approximately 60 per 100,000 women and a mortality rate of 11 per 100,000 women(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Among its subtypes, HER2\u0026thinsp;+\u0026thinsp;breast cancer is particularly notable; it accounts for 20\u0026ndash;25% of invasive cases and is highly aggressive(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). For such subtypes, NAC has emerged as a key treatment strategy(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). By reducing the tumor burden in the breast and axillary lymph nodes, NAC enables surgical downstaging. This allows eligible patients to undergo breast-conserving lumpectomy instead of mastectomy and may even exclude select early-stage HER2\u0026thinsp;+\u0026thinsp;or triple-negative breast cancer patients who achieve excellent pathological responses from surgery entirely(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The key clinical benefits of NAC include diminished surgical extent and enhanced prognostic stratification, especially in patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTreatment response is the primary method for evaluating the efficacy of NAC, with pCR regarded as the sole standard(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). pCR following NAC is strongly associated with improved long-term outcomes, including prolonged survival and reduced distant metastasis risk(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This association is particularly pronounced in HER2\u0026thinsp;+\u0026thinsp;and triple-negative breast cancer subtypes(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Thus, developing robust methods to predict pCR before initiating NAC is pivotal for personalizing therapeutic regimens for patients with breast cancer. Multiple clinicopathological factors may influence the NAC response. In our previous study, the pCR rate varied significantly across molecular subtypes, with rates of 58.9% in HR-HER2\u0026thinsp;+\u0026thinsp;patients, 40.7% in HR\u0026thinsp;+\u0026thinsp;HER2\u0026thinsp;+\u0026thinsp;patients, 35.6% in HR-HER2- patients, and 9.8% in HR\u0026thinsp;+\u0026thinsp;HER2- patients(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Notably, a clinicopathological model combining age, T stage, nodal status, the Ki-67 index, and hormone receptor status has exhibited reliable predictive capacity for pCR(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Clinicopathological factors contribute substantially to predicting pCR following NAC in patients with breast cancer, and exploring their utility in patients with the HER2\u0026thinsp;+\u0026thinsp;subtype is critical for clinical decision-making.\u003c/p\u003e\u003cp\u003eInflammation constitutes a driving force in tumorigenesis, affecting every stage of cancer development. It can promote tumor initiation and drive progression by inducing genomic instability, creating a protumorigenic microenvironment, and facilitating immune evasion, ultimately leading to malignant transformation(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Peripheral blood inflammatory markers serve as critical indicators of breast cancer progression and provide noninvasive prognostic insights(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In addition, inflammatory biomarkers\u0026mdash;including the NLR, PLR, and LMR\u0026mdash;demonstrate potential utility in predicting pCR following NAC for breast cancer patients. A lower pretreatment NLR was significantly inversely associated with pCR in the lymph nodes(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Moreover, lower pretherapeutic values of the NLR, PLR, and LMR were significantly correlated with pCR than nonpCR was(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Although inflammatory markers have shown predictive value, the results of different studies vary widely. Conflicting findings exist: Wang et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) reported that higher pre-NLR, pre-PLR and post-LMR values were associated with a greater likelihood of achieving pCR. The discordant findings across studies underscore the need for further investigation to elucidate the predictive utility of inflammatory biomarkers.\u003c/p\u003e\u003cp\u003eGiven the heterogeneity in clinicopathological predictors and conflicting findings on inflammatory biomarkers in pCR prediction, the present study aimed to collect clinicopathological and inflammatory biomarker data from patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer following NAC and analyze the associations of age, tumor location, clinical stage, ER status, PR status, HER2 status, the Ki-67 index, the NAC regimen, the NLR, the PLR, the MLR and the NMR with pCR. Furthermore, a nomogram was constructed on the basis of the results of univariate and multivariate logistic regression analyses to predict the probability of pCR.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThis retrospective analysis involved 460 HER2\u0026thinsp;+\u0026thinsp;breast cancer patients who received NAC at Nanchang People\u0026rsquo;s Hospital between January 2017 and March 2025. This study obtained ethical approval from the Ethics Committee of Nanchang People\u0026rsquo;s Hospital (Approval No. K-kt2024005). As the current study is retrospective and patient information was processed anonymously, written informed consent was not obtained from patients. All personal data were treated with strict confidentiality, and the research was carried out in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003eThe inclusion criteria were as follows. (a) Pathologically confirmed HER2\u0026thinsp;+\u0026thinsp;invasive breast cancer by core needle biopsy. (b) Baseline imaging (US/CT/MRI) and blood collection\u0026thinsp;\u0026le;\u0026thinsp;7 days before NAC. (c) Received all cycles of NAC. (d) All patients underwent surgery. (e) Comprehensive postoperative pathological assessment confirming pCR status. (f) Complete clinicopathological and inflammatory biomarker data records. The exclusion criteria were as follows. (a) Occult breast cancer or stage IV breast cancer. (b) Bilateral breast cancer or cancer history. (c) Transfusion within 1 month of pretreatment.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eClinicopathological data and inflammatory marker data for all enrolled patients were retrieved from the medical records system of Nanchang People\u0026rsquo;s Hospital. The variables included age, tumor location, surgical procedure, histopathologic type, tumor grade, lymphovascular invasion, clinical T stage, nodal status, cTNM stage, ER/PR status, Ki-67 status, HER2\u0026thinsp;+\u0026thinsp;status, NAC regimen, NLR, PLR, MLR, and NMR. Age was stratified using 50 years as the cutoff value, which is consistent with the findings of a previous study(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The clinical T stage, nodal status and cTNM stages were classified according to the 8th edition of the AJCC(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eNAC regimens\u003c/h3\u003e\n\u003cp\u003e The NAC regimens were formulated on the basis of the National Comprehensive Cancer Network (NCCN) Guidelines for Breast Cancer and the Chinese Society of Clinical Oncology (CSCO) Guidelines for Breast Cancer of the corresponding year, combined with the clinical judgment of attending physicians and the preferences of patients. The NAC regimens administered included chemotherapy alone, chemotherapy combined with trastuzumab, and chemotherapy combined with both trastuzumab and pertuzumab. The chemotherapy regimens used were as follows: four cycles of anthracyclines and cyclophosphamide followed by four cycles of taxanes (AC-T); six cycles of taxanes and anthracyclines and cyclophosphamide (TAC); and six cycles of taxanes and carboplatin (Tcb). The following regimens were used for chemotherapy combined with trastuzumab: four cycles of anthracyclines and cyclophosphamide followed by four cycles of taxanes with trastuzumab (AC-TH) and six cycles of taxanes and carboplatin with trastuzumab (TcbH). The following chemotherapy regimens were combined with trastuzumab and pertuzumab: six cycles of taxanes with trastuzumab and pertuzumab (THP); four cycles of anthracyclines and cyclophosphamide followed by four cycles of taxanes with trastuzumab and pertuzumab (AC-THP); and six cycles of taxanes and carboplatin with trastuzumab and pertuzumab (TcbHP).\u003c/p\u003e\n\u003ch3\u003ePathological assessment\u003c/h3\u003e\n\u003cp\u003eAll patients were pathologically confirmed to have HER2\u0026thinsp;+\u0026thinsp;breast cancer by core needle biopsy prior to NAC. Both the core needle biopsy specimens and surgical samples were subjected to histopathological and immunohistochemical evaluations. ER or PR positivity was defined as immunohistochemical staining showing that at least 1% of infiltrating tumor cells had positive expression(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). According to the evaluation guidelines of the International Ki-67 in the Breast Cancer Working Group, the Ki-67 threshold was set at 30%(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). HER2 positivity was defined as either immunohistochemical analysis showing 3\u0026thinsp;+\u0026thinsp;protein overexpression or 2\u0026thinsp;+\u0026thinsp;protein overexpression accompanied by HER2 gene amplification confirmed via in situ hybridization(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). A pathological complete response was defined as the absence of invasive lesions in both the primary tumor and regional lymph nodes, with only in situ carcinoma remaining after treatment (ypT0/is ypN0), in accordance with the criteria outlined in the 8th Edition of the AJCC Cancer Staging Manual(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eInflammatory biomarkers\u003c/h3\u003e\n\u003cp\u003ePeripheral blood tests were performed within 1 week before NAC, and the values of inflammatory markers, including the NLR, PLR, MLR, and NMR, were recorded. The NLR, PLR, LMR, and NMR were calculated as the ratios of the neutrophil count to the lymphocyte count, the platelet count to the lymphocyte count, the lymphocyte count to the monocyte count, and the neutrophil count to the monocyte count, respectively (all cell counts \u0026times;10⁹/L). Receiver operating characteristic (ROC) curve analysis was applied to determine the optimal cutoff values for the NLR, PLR, MLR, and NMR via the package \u0026lsquo;pROC\u0026rsquo; in RStudio. Values below each cutoff were categorized as \"low\", whereas those above were defined as \"high\".\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eNomogram development and validation\u003c/h2\u003e\u003cp\u003eThe workflow for the development and validation of the model is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients were randomly assigned to the training cohort or testing cohort at a ratio of 7:3 via the \u0026lsquo;sample\u0026rsquo; function in RStudio. Univariate logistic regression analysis was performed on patients in the training cohort to identify clinicopathological factors and inflammatory markers associated with pCR. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from the univariate logistic regression analysis were included in the multivariate regression analysis to further determine independent predictors of pCR (using the \u0026lsquo;glm\u0026rsquo; function in RStudio). A nomogram was developed on the basis of the regression coefficients of each selected factor from multivariate regression analysis to quantitatively predict the probability of pCR in each patient receiving NAC (using the \u0026lsquo;nomogram\u0026rsquo; function in the \u0026lsquo;rms\u0026rsquo; package). The performance of the constructed nomogram was assessed in terms of discrimination, calibration, and clinical utility in both the training and testing cohorts. The area under the receiver operating characteristic (ROC) curve (AUC) for both the training and testing cohorts was calculated via the \"pROC\" package in RStudio, and the ROC curves were plotted accordingly. Calibration curves were generated to compare the agreement between the predicted probabilities and actual observed frequencies. For the training cohort, 1000 bootstrap resamplings were performed via the 'calibrate' function from the \"rms\" package, whereas for the testing cohort, the 'val.prob' function (from the \"rms\" package) was utilized. Decision curve analysis (DCA) curves for both the training and testing cohorts were plotted via the \"rmda\" package to evaluate the clinical net benefit of the model across different threshold probabilities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) if normally distributed. Categorical variables are expressed as frequencies (percentages, %). For continuous variables, significant differences were analyzed via Student\u0026rsquo;s t test (for normally distributed data) or the Mann\u0026ndash;Whitney U test (for nonnormally distributed data). Categorical variables were compared via Fisher\u0026rsquo;s exact test or the chi-square test. In the training cohort, univariate and multivariate logistic regression analyses were conducted to identify the most valuable predictors. A two-tailed p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All the statistical analyses and visualizations were performed via RStudio (version 4.5.0).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics of patients\u003c/h2\u003e\u003cp\u003eThe baseline clinicopathological characteristics and inflammatory biomarkers of the 460 patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eis at the end of the text)\u003c/b\u003e. Overall, 217 of the 460 (47.2%) patients achieved pCR. The results indicated that there were significant differences in the pCR rate among patients stratified by age, ER status, PR status, HER2 status, NAC regimen, and PLR. For age stratification, the pCR rates were 38.7% in patients aged\u0026thinsp;\u0026lt;\u0026thinsp;50 years and 53.4% in those aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, p\u0026thinsp;=\u0026thinsp;0.002). With respect to ER status, patients with ER- status had a pCR rate of 54.6%, whereas those with ER\u0026thinsp;+\u0026thinsp;status had a pCR rate of 39.1% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, p\u0026thinsp;=\u0026thinsp;0.001), and a similar trend was observed for PR status\u0026mdash;with pCR rates of 52.0% in patients with PR- status and 36.2% in those with PR\u0026thinsp;+\u0026thinsp;status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, p\u0026thinsp;=\u0026thinsp;0.002). For HER2 status, a more pronounced difference was noted: patients with HER2 IHC2+/FISH\u0026thinsp;+\u0026thinsp;status had a pCR rate of 26.2%, whereas this rate reached 51.9% in those with HER2 IHC3\u0026thinsp;+\u0026thinsp;status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of NAC regimens, the pCR rates increased with the addition of targeted agents, with 15.9% for chemotherapy alone, 27.3% for chemotherapy\u0026thinsp;+\u0026thinsp;H, and 54.9% for chemotherapy\u0026thinsp;+\u0026thinsp;H\u0026thinsp;+\u0026thinsp;P (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Finally, for the PLR, patients with a low PLR had a pCR rate of 45.1%, whereas those with a high PLR had a higher rate of 61.0% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, p\u0026thinsp;=\u0026thinsp;0.023). Although differences in the pCR rate were also observed across subgroups stratified by histopathologic type, tumor grade, and lymphovascular invasion, these variables had a high proportion of missing values, rendering the results nonreferable.\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\u003eBaseline characteristics of 460 patients who achieved pCR and non‑pCR.\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\u003eNon-pCR,n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003epCR,n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal,n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e243(52.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e217(47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e460\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\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (26\u0026ndash;76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (25\u0026ndash;73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51(25\u0026ndash;76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (34.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e194\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\u0026ge;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124 (51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142 (65.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e266\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\u003eLaterality\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\u003e0.873\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125 (51.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (50.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e235\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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118 (48.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (49.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e225\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\u003eQuadrant\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\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCenter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53\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\u003eUpper outer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124 (51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (50.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e233\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\u003eLower outer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62\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\u003eUpper inner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (21.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93\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\u003eLower inner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19\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\u003eSurgical procedure\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\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u0026thinsp;+\u0026thinsp;SLNB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\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\u003eBCS\u0026thinsp;+\u0026thinsp;ALND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\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\u003eMRM\u0026thinsp;+\u0026thinsp;SLNB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\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\u003eMRM\u0026thinsp;+\u0026thinsp;ALND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213 (87.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e184 (84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e397\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\u003eMRM\u0026thinsp;+\u0026thinsp;SLNB\u0026thinsp;+\u0026thinsp;ALND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\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\u003eHistopathologic type\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einvasive carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e198 (91.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e253\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\u003einvasive ductal carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174 (71.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e190\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\u003einvasive lobular carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\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\u003e10 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\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\u003eTumor grade\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\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\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137\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\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89\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\u003eUnknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e176 (81.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e226\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\u003eLymphovascular invasion\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\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\u003e2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\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\u003e3+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39\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\u003eUnknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173 (71.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e215 (99.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e388\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\u003ecT\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\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32\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\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137 (63.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e272\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\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109\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\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47\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\u003ecN\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\u003e0.708\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\u003e20 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40\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\u003e223 (91.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e197 (90.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e420\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\u003ecTNM\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\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\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\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (35.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160\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\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157 (64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139 (64.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e296\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\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\u003e0.001\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\u003e109 (44.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 (60.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e240\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\u003e134 (55.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (39.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e220\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\u003ePR 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\u003e0.002\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\u003e153 (63.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166 (76.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e319\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\u003e90 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e141\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\u003eKi-67 index\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\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\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\u0026ge;\u0026thinsp;30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e191 (78.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e169 (77.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e360\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHC2+/FISH+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (25.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (10.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eIHC3+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e181 (74.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195 (89.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e376\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\u003eNAC regimen\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44\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\u003eChemotherapy\u0026thinsp;+\u0026thinsp;H\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66\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\u003eChemotherapy\u0026thinsp;+\u0026thinsp;H\u0026thinsp;+\u0026thinsp;P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192 (88.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e350\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\u003eNLR\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\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e178 (73.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146 (67.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e324\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\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136\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\u003ePLR\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\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220 (90.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181 (83.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59\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\u003eMLR\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\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157 (64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121 (55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86 (35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96 (44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e182\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\u003eNMR\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\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129 (53.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (45.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (46.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e233\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\"\u003eBCS, breast conserving surgery; MRM, modified radical mastectomy; SLNB, sentinel lymph node biopsy; ALND, axillary lymph node dissection; ER, estrogen receptor; PR, progesterone receptor; pCR, pathological complete response; HER2, human epidermal growth factor receptor2. IHC, immunohistochemistry; FISH, Fluorescence in situ hybridization; H, Trastuzumab; P, Pertuzumab; NAC, neoadjuvant chemotherapy; NLR, neutrophil to lymphocyte ratio, Low(NLR\u0026lt;2.68), High(NLR\u0026thinsp;\u0026ge;\u0026thinsp;2.68); PLR, platelet to lymphocyte ratio, Low(PLR\u0026lt;206.0),High(PLR\u0026thinsp;\u0026ge;\u0026thinsp;206.0); MLR, monocyte to lymphocyte ratio, Low(MLR\u0026lt;0.26),High(MLR\u0026thinsp;\u0026ge;\u0026thinsp;0.26); NMR, neutrophil to monocyte ratio, Low(NLR\u0026lt;9.32),High(NLR\u0026thinsp;\u0026ge;\u0026thinsp;9.32).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCutoff values of inflammatory biomarkers\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the distribution of inflammatory biomarkers between the pCR group and the nonpCR group. No significant differences were observed in the uncategorized NLR, PLR, MLR, or NMR between the two groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;D, all p values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The optimal cutoff values for the four inflammatory ratios were as follows: the NLR was 2.675 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, AUC\u0026thinsp;=\u0026thinsp;0.520; 95% confidence interval [95% CI]: 0.467\u0026ndash;0.573; sensitivity\u0026thinsp;=\u0026thinsp;0.327; specificity\u0026thinsp;=\u0026thinsp;0.733), the PLR was 206 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, AUC\u0026thinsp;=\u0026thinsp;0.528; 95% CI: 0.475\u0026ndash;0.581; sensitivity\u0026thinsp;=\u0026thinsp;0.166; specificity\u0026thinsp;=\u0026thinsp;0.905), the MLR was 0.257 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, AUC\u0026thinsp;=\u0026thinsp;0.504; 95% CI: 0.450\u0026ndash;0.557; sensitivity\u0026thinsp;=\u0026thinsp;0.442; specificity\u0026thinsp;=\u0026thinsp;0.646), and the NMR was 9.316 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH, AUC\u0026thinsp;=\u0026thinsp;0.531; 95% CI: 0.479\u0026ndash;0.584; sensitivity\u0026thinsp;=\u0026thinsp;0.548; specificity\u0026thinsp;=\u0026thinsp;0.531).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of the training and testing cohorts\u003c/h2\u003e\u003cp\u003eThe 460 patients were randomly allocated into the training cohort (n\u0026thinsp;=\u0026thinsp;322) or testing cohort (n\u0026thinsp;=\u0026thinsp;138) at a 7:3 ratio. The characteristics of the training cohort and testing cohort are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eis at the end of the text)\u003c/b\u003e. The distributions of all clinicopathological factors and inflammatory biomarkers were similar between the two cohorts, with no significant differences observed. This confirms that the random allocation strategy employed in the present study was effective.\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\u003eBaseline characteristics of patients in different cohort\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 cohort,n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTesting cohort,n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal,n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e460\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\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (25\u0026ndash;76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (27\u0026ndash;74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51 (25\u0026ndash;76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145 (45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49 (35.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e194 (42.2)\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\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177 (55.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (64.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e266 (57.8)\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\u003eLaterality\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\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164 (50.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (51.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e235 (51.1)\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\u003eRight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158 (49.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (48.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e225 (48.9)\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\u003eQuadrant\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\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCenter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (11.5)\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\u003eUpper outer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (46.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e233 (50.7)\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\u003eLower outer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (13.5)\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\u003eUpper inner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93 (20.2)\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\u003eLower inner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (4.1)\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\u003eSurgical procedure\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\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u0026thinsp;+\u0026thinsp;SLNB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (3.7)\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\u003eBCS\u0026thinsp;+\u0026thinsp;ALND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (6.1)\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\u003eMRM\u0026thinsp;+\u0026thinsp;SLNB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (2.4)\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\u003eMRM\u0026thinsp;+\u0026thinsp;ALND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e271 (84.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (91.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e397 (86.3)\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\u003eMRM\u0026thinsp;+\u0026thinsp;SLNB\u0026thinsp;+\u0026thinsp;ALND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (1.5)\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\u003eHistopathologic type\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\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einvasive carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171 (53.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (59.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e253 (55.0)\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\u003einvasive ductal carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140 (43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (36.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e190 (41.3)\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\u003einvasive lobular carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (0.9)\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\u003e10 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (2.8)\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\u003eTumor grade\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\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (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\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (29.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137 (29.8)\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\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (19.3)\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\u003eUnknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154 (47.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e226 (49.1)\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\u003eLymphovascular invasion\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\u003e0.710\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (6.1)\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\u003e2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (1.1)\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\u003e3+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (8.5)\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\u003eUnknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e273 (84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e388 (84.3)\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\u003ecT\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\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (7.0)\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\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190 (59.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (59.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e272 (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\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109 (23.7)\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\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (10.2)\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\u003ecN\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\u003e0.470\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\u003e30 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e292 (90.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128 (92.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e420 (91.3)\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\u003ecTNM\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\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (0.9)\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\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160 (34.8)\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\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e209 (64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87 (63.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e296 (64.3)\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\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\u003e0.541\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\u003e171 (53.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e240 (52.2)\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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e151 (46.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e220 (47.8)\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\u003ePR 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\u003e0.774\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\u003e222 (68.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97 (70.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e319 (69.3)\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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100 (31.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e141 (30.7)\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\u003eKi-67 index\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\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100 (21.7)\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\u003e\u0026ge;\u0026thinsp;30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e252 (78.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 (78.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e360 (78.3)\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\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\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHC2+/FISH+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (18.3)\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\u003eIHC3+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e261 (81.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e376 (81.7)\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\u003eNAC regimen\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\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemothrapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (9.6)\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\u003eChemothrapy\u0026thinsp;+\u0026thinsp;H\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (10.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 (14.3)\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\u003eChemothrapy\u0026thinsp;+\u0026thinsp;H\u0026thinsp;+\u0026thinsp;P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e239 (74.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111 (80.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e350 (76.1)\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\u003eNLR\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\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e226 (70.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (71.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e324 (70.4)\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\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96 (29.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (29.6)\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\u003ePLR\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\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e278 (86.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (89.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e401 (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\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (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\u003eMLR\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\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (60.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83 (60.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e278 (60.4)\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\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127 (39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e182 (39.6)\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\u003eNMR\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\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157 (48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (50.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e227 (49.3)\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\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e165 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68 (49.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e233 (50.7)\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\"\u003eBCS, breast conserving surgery; MRM, modified radical mastectomy; SLNB, sentinel lymph node biopsy; ALND, axillary lymph node dissection; ER, estrogen receptor; PR, progesterone receptor; pCR, pathological complete response; HER2, human epidermal growth factor receptor2.IHC, immunohistochemistry; FISH, Fluorescence in situ hybridization; H, Trastuzumab; P, Pertuzumab; NAC, neoadjuvant chemotherapy; NLR, neutrophil to lymphocyte ratio, Low(NLR\u0026lt;2.68),High(NLR\u0026thinsp;\u0026ge;\u0026thinsp;2.68); PLR, platelet to lymphocyte ratio, Low(PLR\u0026lt;206.0),High(PLR\u0026thinsp;\u0026ge;\u0026thinsp;206.0); MLR, monocyte to lymphocyte ratio, Low(MLR\u0026lt;0.26),High(MLR\u0026thinsp;\u0026ge;\u0026thinsp;0.26); NMR, neutrophil to monocyte ratio, Low(NLR\u0026lt;9.32),High(NLR\u0026thinsp;\u0026ge;\u0026thinsp;9.32).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eUnivariate and multivariate analyses\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eis at the end of the text)\u003c/b\u003e, the results of univariate logistic regression analysis in the training cohort indicated that HER2\u0026thinsp;+\u0026thinsp;breast cancer patients aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years (OR\u0026thinsp;=\u0026thinsp;1.778; 95% CI: 1.140\u0026ndash;2.774; p\u0026thinsp;=\u0026thinsp;0.012), ER- (ER+, OR\u0026thinsp;=\u0026thinsp;0.514; 95% CI: 0.329\u0026ndash;0.801; p\u0026thinsp;=\u0026thinsp;0.004), PR- (PR+, OR\u0026thinsp;=\u0026thinsp;0.496; 95% CI: 0.305\u0026ndash;0.807; p\u0026thinsp;=\u0026thinsp;0.005), HER2 IHC3+ (OR\u0026thinsp;=\u0026thinsp;2.860; 95% CI: 1.554\u0026ndash;5.264; p\u0026thinsp;=\u0026thinsp;0.001), the NAC regimen of chemotherapy\u0026thinsp;+\u0026thinsp;H\u0026thinsp;+\u0026thinsp;P (OR\u0026thinsp;=\u0026thinsp;6.750; 95% CI: 2.507\u0026ndash;18.179; p\u0026thinsp;=\u0026thinsp;0.001), or high PLR (OR\u0026thinsp;=\u0026thinsp;2.112; 95% CI: 1.094\u0026ndash;4.077; p\u0026thinsp;=\u0026thinsp;0.027) were more likely to achieve pCR. These variables were incorporated into the multivariate logistic regression analysis, and the results demonstrated that age (age\u0026thinsp;\u0026ge;\u0026thinsp;50 years; OR\u0026thinsp;=\u0026thinsp;1.789; 95% CI: 1.098\u0026ndash;2.933; p\u0026thinsp;=\u0026thinsp;0.021), HER2 status (IHC3+; OR\u0026thinsp;=\u0026thinsp;2.734; 95% CI: 1.414\u0026ndash;5.460; p\u0026thinsp;=\u0026thinsp;0.003), NAC regimen (chemotherapy\u0026thinsp;+\u0026thinsp;H\u0026thinsp;+\u0026thinsp;P; OR\u0026thinsp;=\u0026thinsp;6.483; 95% CI: 2.482\u0026ndash;20.390; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and PLR (high; OR\u0026thinsp;=\u0026thinsp;2.121; 95% CI: 1.040\u0026ndash;4.485; p\u0026thinsp;=\u0026thinsp;0.043) were independent predictors of pCR in patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer after NAC.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate logistic regression analysis of pCR after NAC\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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds Radio (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\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOdds Radio (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\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\u003eAge (years)\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\u003e\u0026lt;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\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\u0026ge;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.778(1.140\u0026ndash;2.774)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.789(1.098\u0026ndash;2.933)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaterality\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\u003eLeft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003eRight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.022(0.660\u0026ndash;1.583)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.923\u003c/p\u003e\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\u003eQuadrant\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\u003eCenter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003eUpper outer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.759(0.383\u0026ndash;1.503)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.428\u003c/p\u003e\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\u003eLower outer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.751(0.319\u0026ndash;1.771)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.513\u003c/p\u003e\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\u003eUpper inner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.834(0.374\u0026ndash;1.864)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.658\u003c/p\u003e\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\u003eLower inner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.617(0.168\u0026ndash;2.267)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.467\u003c/p\u003e\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\u003ecT\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\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.167(0.791\u0026ndash;5.939)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.133\u003c/p\u003e\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\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.625(0.559\u0026ndash;4.726)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.373\u003c/p\u003e\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\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.709(0.841\u0026ndash;8.723)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.095\u003c/p\u003e\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\u003ecN\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\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.582(0.271\u0026ndash;1.250)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.165\u003c/p\u003e\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\u003ecTNM\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\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.638(0.267\u0026ndash;26.160)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.408\u003c/p\u003e\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\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.860(0.293\u0026ndash;27.942)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.367\u003c/p\u003e\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\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\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\u003e0.514(0.329\u0026ndash;0.801)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.601(0.316\u0026ndash;1.136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\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\u003e0.496(0.305\u0026ndash;0.807)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.869(0.425\u0026ndash;1.783)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKi-67 index\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\u003e\u0026lt;\u0026thinsp;30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e\u0026ge;\u0026thinsp;30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.773(0.455\u0026ndash;1.314)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.342\u003c/p\u003e\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\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHC2+/FISH+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\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\u003eIHC3+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.860(1.554\u0026ndash;5.264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.734(1.414\u0026ndash;5.460)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNAC regimen\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\u003eChemothrapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\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\u003eChemothrapy\u0026thinsp;+\u0026thinsp;H\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.916(0.615\u0026ndash;5.970)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.589(0.509\u0026ndash;5.618)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemothrapy\u0026thinsp;+\u0026thinsp;H\u0026thinsp;+\u0026thinsp;P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.750(2.507\u0026ndash;18.179)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.483(2.482\u0026ndash;20.390)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eNLR\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\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.354(0.839\u0026ndash;2.185)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.216\u003c/p\u003e\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\u003ePLR\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\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\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\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.112(1.094\u0026ndash;4.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.121(1.040\u0026ndash;4.485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\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\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.316(0.841\u0026ndash;2.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.231\u003c/p\u003e\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\u003eNMR\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\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.425(0.919\u0026ndash;2.211)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.115\u003c/p\u003e\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eER, estrogen receptor; PR, progesterone receptor; pCR, pathological complete response; HER2, human epidermal growth factor receptor2.IHC, immunohistochemistry; FISH, Fluorescence in situ hybridization; H, Trastuzumab; P, Pertuzumab; NAC, neoadjuvant chemotherapy; NLR, neutrophil to lymphocyte ratio, Low(NLR\u0026lt;2.68),High(NLR\u0026thinsp;\u0026ge;\u0026thinsp;2.68); PLR, platelet to lymphocyte ratio, Low(PLR\u0026lt;206.0),High(PLR\u0026thinsp;\u0026ge;\u0026thinsp;206.0); MLR, monocyte to lymphocyte ratio, Low(MLR\u0026lt;0.26),High(MLR\u0026thinsp;\u0026ge;\u0026thinsp;0.26); NMR, neutrophil to monocyte ratio, Low(NLR\u0026lt;9.32), High(NLR\u0026thinsp;\u0026ge;\u0026thinsp;9.32).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eDevelopment of the nomogram\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003cb\u003e(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e is at the end of the text)\u003c/b\u003e, on the basis of the regression coefficients of the multivariate logistic regression model, the variables included in the model\u0026mdash;age, ER status, PR status, HER2 status, NAC regimen, and PLR\u0026mdash;were weighted to develop a nomogram using the aforementioned predictors. This nomogram was designed to quantitatively predict the probability of pCR in each patient with HER2\u0026thinsp;+\u0026thinsp;breast cancer who received NAC. Each value of every variable was assigned a corresponding score on the scale; the scores of all the variables were summed to obtain a total score, and the probability value corresponding to this total score represented the predicted pCR probability for each patient.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eValidation of the nomogram\u003c/h2\u003e\u003cp\u003eThe performance of the nomogram was evaluated in the training and testing cohorts, with assessments focusing on three key aspects: discrimination, calibration, and clinical utility. The nomogram demonstrated good discriminative ability in the training cohort (AUC\u0026thinsp;=\u0026thinsp;0.755, 95% CI: 0.702\u0026ndash;0.808; cutoff value\u0026thinsp;=\u0026thinsp;0.569; sensitivity\u0026thinsp;=\u0026thinsp;0.675; specificity\u0026thinsp;=\u0026thinsp;0.756; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), with maintained performance in the testing cohort (AUC\u0026thinsp;=\u0026thinsp;0.708, 95% CI: 0.622\u0026ndash;0.794; cutoff value\u0026thinsp;=\u0026thinsp;0.503; sensitivity\u0026thinsp;=\u0026thinsp;0.778; specificity\u0026thinsp;=\u0026thinsp;0.547; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The calibration curves revealed strong model reliability, with a slope of 1 (Brier\u0026thinsp;=\u0026thinsp;0.203; HL p\u0026thinsp;=\u0026thinsp;0.203; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) in the training cohort and a slope of 0.93 (Brier\u0026thinsp;=\u0026thinsp;0.218; HL p\u0026thinsp;=\u0026thinsp;0.459; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). DCA confirmed its clinical utility, yielding superior net benefit compared with default strategies across threshold probabilities of 21\u0026ndash;83% (training cohort) and 0\u0026ndash;70% (testing cohort), with peaks at 47% and 45%, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). These results validate the nomogram\u0026rsquo;s robustness in predicting the probability of pCR and guiding NAC decision-making.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eA pathological complete response is the standard for evaluating the efficacy of NAC in patients with breast cancer. For patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer, achieving pCR after NAC is associated with favorable long-term prognosis(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, exploring predictors of pCR is conducive to guiding individualized treatment for patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer. This study explored the predictive value of clinicopathological factors and inflammatory biomarkers for the efficacy of NAC in patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer through retrospective analysis. In the present study, age, HER2 status, the NAC regimen, and the PLR were identified as independent predictors of pCR in patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer after NAC. Specifically, patients aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years, with HER2 IHC3\u0026thinsp;+\u0026thinsp;status, treated with the NAC regimen of dual HER2 blockade, or with high PLR were more likely to achieve pCR. A nomogram integrating clinicopathological factors and inflammatory indicators for predicting pCR was developed on the basis of the results of univariate and multivariate logistic regression analyses.\u003c/p\u003e\u003cp\u003eAge is a crucial prognostic factor in breast cancer patients. Younger breast cancer patients have a greater risk of recurrence and mortality than older breast cancer patients do(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Additionally, different age groups of breast cancer patients have different pCR rates after NAC. Among patients with triple-negative breast cancer, younger patients aged\u0026thinsp;\u0026le;\u0026thinsp;40 years had higher pCR rates (52% vs 35% in those aged 41\u0026ndash;60 years and 29% in those aged\u0026thinsp;\u0026ge;\u0026thinsp;61 years), likely because they have a greater proportion of BRCA carriers and TIL-rich tumors, which are associated with enhanced chemosensitivity(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Similarly, another retrospective study confirmed that age influences the pCR rate; in HR+/HER2- breast cancer, patients aged\u0026thinsp;\u0026lt;\u0026thinsp;50 years had a higher axillary lymph node pCR rate than those aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years did (31.8% vs 17.7%) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, the effect of age on pCR was not significant in HER2\u0026thinsp;+\u0026thinsp;patients. Specifically, Li et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) reported that in the HR-/HER2\u0026thinsp;+\u0026thinsp;subtype, older patients presented a higher pCR rate than younger patients did (33.2% vs 26.1%), whereas no significant difference was noted in the HR+/HER2\u0026thinsp;+\u0026thinsp;subtype of breast cancer. In our study, age was identified as an independent predictor of pCR in patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer after NAC. Unlike those with HR+/HER2- and TNBC subtypes, patients aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years with HER2\u0026thinsp;+\u0026thinsp;breast cancer were more likely to achieve pCR than were those aged\u0026thinsp;\u0026lt;\u0026thinsp;50 years (53.4% vs 38.7%). This finding is consistent with the results of Li\u0026rsquo;s study; however, the increase in the pCR rate in the older group compared with the younger group was more pronounced in our study, which may be attributed to the different age cutoff values used. Another factor contributing to the difference in pCR rates is that the older group had a greater proportion of patients with HR- and Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;30%.\u003c/p\u003e\u003cp\u003eThe transmembrane tyrosine kinase receptor HER2 is associated with the growth, differentiation, and angiogenesis of breast cancer cells, conferring aggressive biological behavior to tumors and resulting in poor prognosis(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Clinically, chemotherapy combined with dual antibody therapy has become the standard preoperative or postoperative treatment for early-stage HER2\u0026thinsp;+\u0026thinsp;breast cancer, with a 3-year treatment survival rate exceeding 90%(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The efficacy of tyrosine kinase inhibitors may be associated with the expression level of the HER2 protein. Compared with patients with HER2 IHC 2+/FISH\u0026thinsp;+\u0026thinsp;results, those with HER2 IHC 3\u0026thinsp;+\u0026thinsp;results exhibit superior efficacy(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Similarly, a retrospective study demonstrated that among HER2\u0026thinsp;+\u0026thinsp;breast cancer patients receiving NAC, those with HER2 IHC 3\u0026thinsp;+\u0026thinsp;status had higher unadjusted pCR rates in the breast (54% vs. 22%) and lymph nodes (69% vs. 37%) than did those with HER2 IHC 2+/FISH\u0026thinsp;+\u0026thinsp;status(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In our study, the pCR rate in patients with HER2 IHC 3\u0026thinsp;+\u0026thinsp;status was significantly greater than that in patients with HER2 IHC 2+/FISH\u0026thinsp;+\u0026thinsp;status (51.6% vs. 26.2%). Multivariate logistic regression analysis revealed that HER2 status was an independent predictor of pCR, which was consistent with the aforementioned study results, further confirming that HER2 expression status is a robust predictive biomarker for pCR in HER2\u0026thinsp;+\u0026thinsp;breast cancer patients.\u003c/p\u003e\u003cp\u003eThe NAC regimen, as the most critical factor influencing the pCR rate following NAC, is an independent predictor of pCR. In the present study, the pCR rates of chemotherapy alone, chemotherapy\u0026thinsp;+\u0026thinsp;H, and chemotherapy\u0026thinsp;+\u0026thinsp;H\u0026thinsp;+\u0026thinsp;P were 15.9%, 27.3%, and 54.9%, respectively. These data clearly demonstrate a stepwise increase in pCR rates with the addition of targeted agents (from single H to dual H\u0026thinsp;+\u0026thinsp;P), which aligns with findings from previous key trials. In the NeoSphere trial, the pCR rate of dual-target therapy combined with docetaxel reached 45.8%, which was significantly greater than the 29% reported in the chemotherapy plus single-target therapy group(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Similarly, in the Asian population, the PEONY trial confirmed this trend: the pCR rate of the NAC regimen with dual-target therapy combined with docetaxel was significantly higher than that of the single-target therapy group (39.3% vs. 21.8%)(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Collectively, our findings, together with data from the NeoSphere and PEONY trials, consistently confirm that the pCR rate of HER2\u0026thinsp;+\u0026thinsp;breast cancer patients treated with NAC increases with the addition of targeted agents. In support of the pivotal role of NAC regimens, in our nomogram, the NAC regimen contributed the most to predicting the probability of pCR, with the score for \"chemotherapy\u0026thinsp;+\u0026thinsp;H\u0026thinsp;+\u0026thinsp;P\" reaching 100\u0026mdash;significantly higher than that of other predictive factors.\u003c/p\u003e\u003cp\u003eInflammation can promote cancer initiation and progression by facilitating tumor cell proliferation, invasion, and metastasis, and it can also suppress the antitumor immune response by altering the tumor microenvironment(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Among the cellular components involved in inflammatory responses, platelets play pivotal roles in tumor progression. They can release growth factors, cytokines, and proangiogenic factors to promote cancer cell proliferation, survival, and tumor angiogenesis(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Additionally, platelets assist cancer cells in adhering to the vascular endothelium, extravasating from vessels to form distant metastases, and evading the body\u0026rsquo;s immune attack via interactions with them (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). In contrast, lymphocytes\u0026mdash;key effector cells of the immune system\u0026mdash;reflect the body\u0026rsquo;s immune status and play crucial roles in cancer immunosurveillance and antitumor therapy(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Notably, the ratios of different inflammatory factors, such as the NLR, PLR, and LMR, are positively correlated with tumor risk(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The predictive value of the PLR for the efficacy of NAC in patients with breast cancer remains controversial. A retrospective study by Hu et al.(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) found that in patients with luminal B (HER2\u0026minus;) breast cancer, an elevated PLR was associated with a lower pCR rate after NAC (15.8% in the low PLR group vs. 9.2% in the high PLR group; P\u0026thinsp;=\u0026thinsp;0.027). Similarly, another retrospective study reported that patients with low PLRs (\u0026lt;\u0026thinsp;181.7) achieved a significantly higher pCR rate than did those with high PLRs (\u0026gt;\u0026thinsp;181.7) (68.6% vs. 33.4%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Notably, our findings contradict those of the above studies. Using a PLR cutoff value of 260, we observed that the pCR rate was significantly greater in patients with high PLRs than in those with low PLRs (61% vs. 45.1%; p\u0026thinsp;=\u0026thinsp;0.023). Additionally, the PLR was identified as an independent predictor of pCR. Consistent with our results, two recent studies have also concluded that the PLR can serve as an independent predictor of pCR, with a higher pCR rate in patients with a high PLR than in those with a low PLR(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Our study and previous research revealed an association between the PLR and the pCR rate. However, substantial discrepancies exist in terms of PLR cutoff values and results. Thus, additional studies are still needed to clarify the predictive value of the PLR.\u003c/p\u003e\u003cp\u003eAlthough the ER and PR statuses were significantly associated with pCR in the univariate analysis, they were not retained as independent predictors in the final multivariate model. This may be attributed to the fact that the predictive information conveyed by ER/PR status was captured by stronger covariates in the model, particularly HER2 IHC status and the dual HER2 blockade regimen\u0026mdash;a finding that is consistent with the notion that HER2-driven signaling and targeted therapy exert more dominant effects on the NAC response in HER2\u0026thinsp;+\u0026thinsp;breast cancer than does hormone receptor status(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, we developed a nomogram to predict pCR by integrating clinicopathological factors and inflammatory biomarkers. This model exhibited good discriminative ability, with an AUC of 0.755 in the training cohort and 0.708 in the testing cohort. The calibration curves further confirmed strong model reliability. The training cohort had a slope of 1 (Brier score\u0026thinsp;=\u0026thinsp;0.203; HL test, p\u0026thinsp;=\u0026thinsp;0.203), whereas the testing cohort had a slope of 0.93 (Brier score\u0026thinsp;=\u0026thinsp;0.218; HL test, p\u0026thinsp;=\u0026thinsp;0.459). DCA verified the nomogram\u0026rsquo;s clinical utility. Across threshold probabilities of 21\u0026ndash;83% (training cohort) and 0\u0026ndash;70% (testing cohort), it achieved a superior net benefit compared with default strategies, with peak net benefits observed at 47% and 45%, respectively. Notably, the nomogram maintained favorable performance in the validation cohort, indicating adequate robustness and generalizability. In practice, each variable in the nomogram is assigned a corresponding score. By summing these scores, clinicians can calculate the probability of an individual patient achieving pCR after NAC. This personalized probability estimation may ultimately assist clinicians in optimizing individualized treatment decisions for patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, as a retrospective study, it is inherently subject to inherent biases and uncontrollable confounding factors. Second, the study was based on single-center data with a limited sample size\u0026mdash;particularly in the validation cohort\u0026mdash;and the model lacked independent data from other centers for external validation. Third, owing to insufficient data, the model did not include potential predictive factors such as tumor grade, lymphovascular invasion, or tumor-infiltrating lymphocytes. Fourth, the cutoff values for inflammatory markers were determined on the basis of the ROC curve of the study population and thus require external validation. Fifth, long-term survival data were not available.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified age\u0026thinsp;\u0026ge;\u0026thinsp;50 years, HER2 IHC 3\u0026thinsp;+\u0026thinsp;status, dual HER2 blockade regimen, and high PLR as independent predictors of pCR following NAC in patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer. On the basis of these independent predictors, we successfully developed and internally validated a novel nomogram for HER2\u0026thinsp;+\u0026thinsp;breast cancer. This nomogram exhibits robust predictive performance, good calibration, and favorable clinical utility for individualized pCR prediction, which may ultimately assist clinicians in optimizing treatment decision-making for patients undergoing NAC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by grants from the National Natural Science Foundation of China (grant no. 82060482) and the Natural Science Foundation of Jiangxi Province (grant no. 20171BAB205057).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated in the present study may be requested from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJD and YC contributed to the conception and design of the manuscript. XJ, ZH, XW, JL, and LJ were responsible for the acquisition, analysis and interpretation of the data. XJ and JD edited, drafted and wrote the manuscript. All the authors confirm the authenticity of all the raw data and read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was approved by the review board ethics committee of Nanchang People\u0026rsquo;s Hospital (approval no. K-kt2024005; Nanchang, China). The requirement for patient approval or written informed consent was waived because of the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74:12\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024;4:47\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoibl S, Gianni L. HER2-positive breast cancer. 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Heliyon. 2024;10:e34361.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTian W, Wei W, Qin G, et al. Lymphocyte homing and recirculation with tumor tertiary lymphoid structure formation: predictions for successful cancer immunotherapy. Front Immunol. 2024;15:1403578.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eN\u0026oslash;st TH, Alcala K, Urbarova I, et al. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur J Epidemiol. 2021;36:841\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu Y, Wang S, Ding N, Li N, Huang J, Xiao Z. Platelet/Lymphocyte Ratio Is Superior to Neutrophil/Lymphocyte Ratio as a Predictor of Chemotherapy Response and Disease-free Survival in Luminal B-like (HER2(-)) Breast Cancer. Clin Breast Cancer. 2020;20:e403\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAcikgoz O, Yildiz A, Bilici A, Olmez OF, Basim P, Cakir A. Pretreatment platelet-to-lymphocyte ratio and neutrophil-to-lymphocyte ratio as a predictor of pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: single center experience from Turkey. Anticancer Drugs. 2022;33:1150\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin X, Wang K, Shao X, Huang J. Prognostic implications of the peripheral platelet-to-lymphocyte ratio and neutrophil-to-lymphocyte ratio in predicting pathologic complete response after neoadjuvant chemotherapy in breast cancer patients. Gland Surg. 2022;11:1057\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePegram M, Jackisch C, Johnston SRD. Estrogen/HER2 receptor crosstalk in breast cancer: combination therapies to improve outcomes for patients with hormone receptor-positive/HER2-positive breast cancer. NPJ Breast Cancer. 2023;9:45.\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 chemotherapy, pathological complete response, inflammatory biomarkers, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7500826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7500826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePathological complete response (pCR) following neoadjuvant chemotherapy (NAC) strongly predicts favorable prognosis in patients with breast cancer. However, significant gaps remain in identifying reliable predictors of pCR\u0026mdash;particularly regarding inflammatory biomarkers. This study aimed to identify clinicopathological and inflammatory factors associated with pCR in human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients and develop a predictive nomogram.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe retrospectively analyzed 460 patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer who received NAC at Nanchang People's Hospital (January 2017\u0026ndash;May 2024). Patients were randomly allocated to the training (n\u0026thinsp;=\u0026thinsp;322) or testing (n\u0026thinsp;=\u0026thinsp;138) cohorts at a ratio of 7:3. Variables with significant associations in the univariate analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were included in the multivariate logistic regression. A nomogram incorporating independent predictors was validated for its discrimination, calibration, and clinical utility.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe overall pCR rate was 47.2% (217/460). The pCR rates were significantly higher for those aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years (53.4% vs\u0026thinsp;\u0026lt;\u0026thinsp;50:38.7%), those with estrogen receptor (ER)- (54.6% vs ER+:39.1%), those with progesterone receptor (PR)- (52.0% vs PR+: 36.2%), those with HER2 IHC3+ (51.9% vs IHC2+/FISH+:26.2%), those with dual HER2 blockade (54.9% vs chemotherapy alone:15.9%), and those with high PLRs (\u0026ge;\u0026thinsp;206 vs\u0026thinsp;\u0026lt;\u0026thinsp;206:61.0% vs 45.1%) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Univariate analysis of the training cohort revealed that age, ER status, PR status, HER2 status, NAC regimens, and the PLR were significant predictors. Multivariate analysis confirmed that age\u0026thinsp;\u0026ge;\u0026thinsp;50 years (OR\u0026thinsp;=\u0026thinsp;1.789, 95% CI: 1.098\u0026ndash;2.933, p\u0026thinsp;=\u0026thinsp;0.021), HER2 IHC3\u0026thinsp;+\u0026thinsp;status (OR\u0026thinsp;=\u0026thinsp;2.734, 95% CI: 1.414\u0026ndash;5.460, p\u0026thinsp;=\u0026thinsp;0.003), dual HER2 blockade (OR\u0026thinsp;=\u0026thinsp;6.483, 95% CI: 2.482\u0026ndash;20.390, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and high PLR (OR\u0026thinsp;=\u0026thinsp;2.121, 95% CI: 1.040\u0026ndash;4.485, p\u0026thinsp;=\u0026thinsp;0.043) were independent predictors. The nomogram demonstrated good discrimination (training AUC\u0026thinsp;=\u0026thinsp;0.755; testing AUC\u0026thinsp;=\u0026thinsp;0.708), satisfactory calibration (Hosmer\u0026ndash;Lemeshow test: training P\u0026thinsp;=\u0026thinsp;0.203, testing P\u0026thinsp;=\u0026thinsp;0.459), and favorable net clinical benefit.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;50 years, HER2 IHC 3\u0026thinsp;+\u0026thinsp;status, dual HER2 blockade, and high PLR independently predict pCR in patients with HER2\u0026thinsp;+\u0026thinsp;breast cancer. The developed nomogram provides a clinically applicable tool for pCR prediction, which may aid in optimizing personalized NAC strategies for this patient population.\u003c/p\u003e","manuscriptTitle":"Predictors of pathological complete response to neoadjuvant chemotherapy in HER2-positive breast cancer: development and validation of a clinical-inflammatory nomogram","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 06:49:44","doi":"10.21203/rs.3.rs-7500826/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":"71cdef33-2f3b-40fa-bf9a-ac956ee32b89","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T08:24:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 06:49:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7500826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7500826","identity":"rs-7500826","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}