Development of Predictive Models for Pathological Response Status in Breast Cancer after Neoadjuvant Therapy Based on Peripheral Blood Inflammatory Indexes

Development of Predictive Models for Pathological Response Status in Breast Cancer after Neoadjuvant Therapy Based on Peripheral Blood Inflammatory Indexes | 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 Development of Predictive Models for Pathological Response Status in Breast Cancer after Neoadjuvant Therapy Based on Peripheral Blood Inflammatory Indexes Shuqiang Liu, Cong Jiang, Danping Wu, Shiyuan Zhang, Kun Qiao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4917041/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Oct, 2024 Read the published version in BMC Women's Health → Version 1 posted 19 You are reading this latest preprint version Abstract Background. Achieving a pathological complete response (pCR) after neoadjuvant therapy (NAT) is considered to be a critical factor for a favourable prognosis in breast cancer. However, discordant pathological complete response (DpCR), characterised by isolated responses in the breast or axillary, represents an intermediate pathological response category between no response and complete response. This study aims to investigate predictive factors and develop models based on peripheral blood inflammatory indexes to more accurately predict NAT outcomes. Method. A total of 789 eligible patients were enrolled in this retrospective study. The patients were randomized into training and validation cohort according to a 7:3 ratio. Lasso and uni/multivariate logistic regression analysis were applied to identify the predictor variables. Two Nomograms combining clinico-pathologic features and peripheral blood inflammatory indexes were developed. Result. Molecular Subtype, HALP, P53, and FAR were used to construct the predictive models for traditional non-pCR (T-NpCR) and total-pCR (TpCR). The T-NpCR group was divided into DpCR and non-pCR (NpCR) subgroups to construct a new model to more accurately predict NAT outcomes. cN, HALP, FAR, Molecular Subtype, and RMC were used to construct the predictive models for NpCR and DpCR. The receiver operating characteristic (ROC) curves indicate that the model exhibits robust predictive capacity. Clinical Impact Curves (CIC) and Decision Curve Analysis (DCA) indicate that the models present a superior clinical utility. Conclusion. HALP and FAR were identified as peripheral blood inflammatory index predictors for accurately predicting NAT outcomes. Breast Cancer Neoadjuvant Therapy Pathological Response FAR HALP Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Lymph node metastasis constitutes a pivotal determinant in the recurrence and metastatic progression of breast cancer. Neoadjuvant therapy (NAT) has become the preferred treatment for node-positive breast cancer [ 1 ] . Total-pathological complete response (TpCR) is defined as the absence of significant residual tumor in both the breast and lymph nodes by post-NAT pathological examination, serving as a critical indicator of favorable prognosis [ 2 ] . However, previous studies have indicated the potential for discrepancies in the response of breast lesions and lymph nodes to neoadjuvant therapy. The American College of Surgeons Oncology Group (ACOSOG) reported a pCR rate of 27.8% in the breast and axilla in the Z1071 study, compared with only 5.7% in the breast alone and 13.1% in the lymph nodes alone [ 3 ] . This phenomenon, where the tumor disappears in only one part of the breast and axillary lymph nodes, while remaining in another part, is known as a discordant pathological complete response (DpCR). Previous studies have generally considered DpCR as not having achieved complete pathological remission and therefore classified them into the NpCR group. However, in fact, patients with DpCR represent a unique prognostic group whose prognosis is intermediate between that of TpCR and non-pathological complete remission (NpCR) group [ 4 , 5 ] . DpCR is a transitional state which could partly response to neoadjuvant therapy. It suggests that it may be possible to convert DpCR to TpCR [ 6 ] .Therefore, DpCR patients should be studied in isolation from traditional NpCR (T-NpCR). [ 7 – 9 ] A more precise estimation of the pathological response of breast cancer prior to NAT has significant implications for the assessment of disease risk and the development of therapeutic strategies. Research on the clinic features and prediction model for DpCR status is currently insufficient, requiring further scientific investigations to address this deficiency. Breast cancer cells can interact with peripheral stromal and inflammatory cells to form inflammatory tumor microenvironment, which contributes to tumorigenesis, progression, invasion and chemoresistance, affecting patient prognosis [ 10 ] . Peripheral blood inflammatory index including platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and systemic immuno-inflammatory index (SII) exhibit a significant correlation with the prognosis of BC patients [ 11 – 15 ] . Numerous retrospective clinical trials in breast cancer have used peripheral blood inflammatory indices to predict TpCR in NAT [ 16 ] . However, there is a lack of research investigating the predictive potential of peripheral blood inflammatory index for DpCR. This study aimed to identify clinic features and peripheral blood inflammatory indexes that could be used to predict pathological response degree following NAT. We developed two prediction models and validated the predictive ability of the models. Methods and materials Patient Selection Female patients with breast cancer who were treated with NAT from January 2011 to December 2023 at Harbin Medical University Cancer Hospital were recruited into this retrospective study. The inclusion criteria are as follows: (1) all patients underwent ultrasound-guided aspiration biopsy of the breast and associated lymph nodes before NAT. The biopsy pathology confirmed the presence of tumour metastases in these lymph nodes; (2) received standard neoadjuvant treatment program. All neoadjuvant regimens are based on NCCN guidelines; (3) Receive surgical resection and axillary lymph node dissection or sentinel lymph node biopsy. Patients with either of the following situations were excluded: (1) history of prior or concurrent breast cancer; (2) evidence of distant metastasis; (3) receiving less than two cycles of NAT; (4) abandoning NAT in the middle of process due to intolerable side-effects. This study was approved by the Clinical Research Ethics Committee of the Harbin Medical University Cancer Hospital. This research complies with the 1964 World Medical Association Declaration of Helsinki and subsequently amended versions. All patients were provided with written informed consent prior to participation. Definition of different pathological reaction statuses after NAT All samples were evaluated using the AJCC ypTNM criteria. TpCR was defined as postoperative pathology in which no evidence of tumor residue was found in either the breast lesion or the lymph node (ypT0/ypN0). DpCR was defined as postoperative pathology in which tumor residue was found in one of the two, but not the other, in either the breast lesion or the lymph node (ypT ≥ 1/ypN0 or ypT0/ypN ≥ 1). Breast pCR or lymph node pCR are collectively known as DpCR, as previous studies have shown that they have the same survival [ 7 ] . NpCR was defined as postoperative pathology in which tumor residue was found in both the breast lesion and the lymph node. The combination of DpCR and NpCR was described as T-NpCR, which is the traditional meaning of not achieving pCR after NAT. In addition, previous studies have demonstrated that survival rates for ductal carcinoma in situ (DCIS) or minimal residual disease (MRD) in the breast or lymph nodes show no significant difference from those observed for TpCR. Consequently, these two conditions have been included in the TpCR group [ 6 , 17 ] . Pathologic features Referring to the 2020 edition of the ASCO guidelines, an IHC score of ≥ 1% for ER or PR was defined as positive. IHC score of 0 or 1 + for HER2 was considered negative. In cases with an IHC score of 2+, fluorescence in situ hybridization (FISH) must be performed in addition to IHC. IHC score of 3 (> 10% of cells showing high-intensity periplasmic staining) or FISH positivity was considered positive. Ki-67 was defined as low expression if the proportion of positively stained cells was less than 15%. It was defined as moderate expression when the percentage of positively stained cells was between 15–29%. The high expression was defined when the percentage of positively stained cells was ≥ 29%. Peripheral blood inflammation index All peripheral blood inflammation index were calculated based on the haematology of the patients 7 days before they underwent NAT. The calculation of each index of peripheral blood inflammation was as follow: NLR (Neutrophil count/ Lymphocyte count), Pan-Immune-Inflammation-value (PIV, Platelet count ×Neutrophil count × Monocytes count/ Lymphocyte count), Systemic inflammatory response index (SIRI, Neutrophil count ×Monocyte count/ Lymphocyte count), the Hemoglobin, Albumin, Lymphocyte, Platelet Score (HALP, Hemoglobin count × Albumin × Lymphocyte count/ Platelet count ), the Fibrinogen-Albumin Ratio (FAR, Fibrinogen×100/ Albumin), the Fibrinogen-Platelet Ratio (FPR, Fibrinogen/ Platelet count), the Systemic Immunoinflammatory Index (SII, Platelet count × Neutrophil count/ Lymphocyte count), LMR (Lymphocyte count/ Monocyte count), PLR (Platelet count/ Lymphocyte count). Follow-up All patients received regular follow-up after completing treatment according to clinical guidelines. The follow-up programme was conducted at the following intervals: every three to four months for the initial two years, every six months for the subsequent three years, and annually thereafter. Participants were defined as having lost to follow-up if there was an interval of more than two years between the last visit and the final follow-up. All patients followed up until 30 January 2024. Disease-free survival (DFS) was defined as the interval between diagnosis and either breast cancer recurrence, death from any cause, or the date of the final follow-up. Similarly, overall survival (OS) was defined as the time from diagnosis to either death or the date of the final follow-up. Statistical analysis This study applied RStudio (version 4.3.2; https://www.r-project.org/ ) software for statistical analysis. The correlation between different groups was analyzed using the chi-square test. Lasso regression was employed to identify predictive features based on non-zero coefficients. The optimal parameter (lambda) selection in the Lasso model was cross-validated ten-fold based on the minimum criterion. The receiver operating characteristic curve (ROC) was employed to determine the best cut-off value. Kaplan-Meier curves and log-rank tests were employed to plot and compare the DFS and OS curves. The patients were randomly divided into a training and validation set in a 7:3 ratio. Lasso regression analysis, uni/multivariate logistic regression analysis were performed to identify factors associated with pCR status after NAT. P < 0.05 was considered a statistically significant. Results Baseline of patients achieve T-NpCR and TpCR A total of 789 patients were included in the study. Of 207 (26.24%) achieved TPCR, while 582 (73.76%) were evaluated for T-NpCR. The baseline clinic features and peripheral blood inflammatory indexes of patients are presented in Table 1 . A chi-square test was performed for all variables. Molecular Subtype, Ki67, P53, NAT cycle, NAT regimen, HER2 Targeted Therapy, Carboplatin, SII, NLR, PLR, PIV, REC, RNC, RMC, FPR, FAR, HALP was found to be significantly different between the two groups. Therefore, these 17 variables were included in the screening process of predictors. Table 1 Clinical baseline characteristics between T-NpCR and TpCR patient. Characteristic Levels T-NpCR (N = 582) TpCR (N = 207) P Age > 60 104 (17.9%) 29 (14%) .244 ≤ 60 478 (82.1%) 178 (86%) BMI Obese 40 (6.9%) 11 (5.3%) .735 Overweight 198 (34%) 72 (34.8%) Lean 344 (59.1%) 124 (59.9%) Menstrual state Menopause 320 (55%) 106 (51.2%) .393 Premenopause 262 (45%) 101 (48.8%) Lesion number Mono 462 (79.4%) 171 (82.6%) .368 Multi 120 (20.6%) 36 (17.4%) cT 1 + 2 476 (81.8%) 179 (86.5%) .151 3 + 4 106 (18.2%) 28 (13.5%) cN 1 + 2 381 (65.5%) 126 (60.9%) .271 3 201 (34.5%) 81 (39.1%) Molecular Subtype HR(+)HER2(-) 343 (58.9%) 26 (12.6%) < .001 HR(+)HER2(+) 74 (12.7%) 37 (17.9%) HR(-)HER2(+) 66 (11.3%) 68 (32.9%) HR(-)HER2(-) 99 (17%) 76 (36.7%) Ki67 ≥ 30% 287 (49.3%) 147 (71%) < .001 15% − 30% 172 (29.6%) 41 (19.8%) < 15% 123 (21.1%) 19 (9.2%) P53 0 247 (42.4%) 141 (68.1%) 6 182 (31.3%) 74 (35.7%) .273 ≤ 6 400 (68.7%) 133 (64.3%) HER2 Targeted Therapy Yes 110 (18.9%) 72 (34.8%) 13.45 257 (44.2%) 84 (40.6%) .417 ≤ 13.45 325 (55.8%) 123 (59.4%) SIRI > 1.08 130 (22.3%) 54 (26.1%) .317 ≤ 1.08 452 (77.7%) 153 (73.9%) SII > 839.22 112 (19.2%) 20 (9.7%) .002 ≤ 839.22 470 (80.8%) 187 (90.3%) NLR > 1.51 437 (75.1%) 139 (67.1%) .034 ≤ 1.51 145 (24.9%) 68 (32.9%) PLR > 162.59 201 (34.5%) 49 (23.7%) .005 ≤ 162.59 381 (65.5%) 158 (76.3%) LMR > 3.12 521 (89.5%) 191 (92.3%) .313 ≤ 3.12 61 (10.5%) 16 (7.7%) PIV > 462.21 63 (10.8%) 12 (5.8%) .048 ≤ 462.21 519 (89.2%) 195 (94.2%) REC > 0.87 349 (60%) 145 (70%) .013 ≤ 0.87 233 (40%) 62 (30%) RLC > 37 134 (23%) 60 (29%) .106 ≤ 37 448 (77%) 147 (71%) RNC > 57.69 373 (64.1%) 111 (53.6%) .010 ≤ 57.69 209 (35.9%) 96 (46.4%) RMC > 6.32 227 (39%) 102 (49.3%) .013 ≤ 6.32 355 (61%) 105 (50.7%) FPR > 1.11 322 (55.3%) 76 (36.7%) 6.6 363 (62.4%) 63 (30.4%) 37.32 336 (57.7%) 160 (77.3%) < .001 ≤ 37.32 246 (42.3%) 47 (22.7%) T-NpCR: traditional non-pathologic complete response, DpCR: discordant-pathologic complete response, TpCR: total-pathologic complete response,BMI: body mass index, PDW: platelet distribution width, REC: relative eosinophilic count, RLC: relative lymphocyte count, RNC: relative neutrophil count, RMC: relative macrophage cell count. Identification of predictors of the T-NpCR and TpCR group All patients were randomly divided into validation set (n = 267) and training set (n = 522) according to a 7:3 ratio. Four parameters (molecular subtypes, P53, FAR, and HALP) were identified as potential predictors by Lasso (Fig. 1 A-B) and uni/multivariate (Fig. 1 C) logistic regression analysis. Based on these predictors, nomogram were constructed (Fig. 1 D). Evaluating the prediction performance of T-NpCR and TpCR group model in train set The ROC curve showed that AUC value of the model was 0.803 (95% CI: 0.761–0.846), and the AUC values of Molecular Subtype, FAR, HALP and P53 were 0.728 (95%CI: 0.684–0.772), 0.701 (95%CI: 0.653–0.749), 0.612 (95%CI: 0.56–0.664), 0.602 (95%CI: 0.549–0.656) (Fig. 2 A). The calibration curve demonstrated that the average absolute error of the model was 0.014, indicating that the model exhibited enhanced predictive capability (Fig. 2 B). To assess the clinical utility and predictive capacity of nomograms, DCA and CIC were plotted. The models in the T-NpCR and TpCR group exhibited superior clinical utility when the threshold probability values were within the range of 0.35 to 0.77 (Fig. 2 C-D). Baseline and prognosis of patients achieve NpCR and DpCR To further understand the prognosis of patients with different extent of pathological response after NAT for breast cancer, the T-NpCR cohort was divided into two subgroups: NpCR and DpCR. The mean follow-up period for the entire cohort was 49 months (3–88 months). Based on the follow-up data, K-M curves were plotted for patients in the three groups (Fig. 3 A-B). The result demonstrated that patients in the TpCR group exhibited the best OS and DFS, followed by the DpCR group, and the NpCR group proved to be the worst. The observed differences in survival between each two groups were statistically significant. Thus, DpCR represents a subgroup with a distinct prognosis, making it scientifically meaningful to study it separately from T-NpCR. The baseline clinical characteristics of the NpCR and DpCR groups are shown in Table 2 . Among them, 314 patients were assessed as NpCR, and 268 patients were assessed as DpCR. Chi-square tests revealed statistically significant differences between the groups for the variables Lesion number, cN, Molecular Subtype, P53, NAT regimen, HER2 targeted therapy, RMC, FPR, FAR, and HALP. These 10 variables were included in the predictor selection process for model construction. Five parameters (cN, Molecular Subtype, RMC, HALP and FAR) were identified as potential predictors by Lasso (Fig. 3 C-D) and uni/multivariate (Fig. 3 E) logistic regression analysis. Based on these predictors, nomogram were constructed (Fig. 3 F). Table 2 Clinical baseline characteristics between NpCR and DpCR group. Characteristic Levels NpCR (N = 314) DpCR (N = 268) P Age > 60 58 (18.5%) 46 (17.2%) .763 ≤ 60 256 (81.5%) 222 (82.8%) BMI Obese 20 (6.4%) 20 (7.5%) .623 Overweight 112 (35.7%) 86 (32.1%) Lean 182 (58%) 162 (60.4%) Menstrual state Menopause 182 (58%) 138 (51.5%) .139 Premenopause 132 (42%) 130 (48.5%) Lesion number Mono 234 (74.5%) 228 (85.1%) .002 Multi 80 (25.5%) 40 (14.9%) cT 1 255 (81.2%) 221 (82.5%) .778 2 59 (18.8%) 47 (17.5%) cN 1 + 2 190 (60.5%) 191 (71.3%) .008 3 124 (39.5%) 77 (28.7%) Molecular Subtype HR(+)HER2(-) 218 (69.4%) 125 (46.6%) < .001 HR(+)HER2(+) 27 (8.6%) 47 (17.5%) HR(-)HER2(+) 22 (7%) 44 (16.4%) HR(-)HER2(-) 47 (15%) 52 (19.4%) Ki67 ≥ 30% 144 (45.9%) 143 (53.4%) .192 15% − 30% 100 (31.8%) 72 (26.9%) 8 70 (22.3%) 86 (32.1%) .010 ≤ 8 244 (77.7%) 182 (67.9%) HER2 targeted therapy Yes 51 (16.2%) 64 (23.9%) .028 No 263 (83.8%) 204 (76.1%) PDW > 13.05 138 (43.9%) 119 (44.4%) .979 ≤ 13.05 176 (56.1%) 149 (55.6%) SIRI > 0.54 74 (23.6%) 56 (20.9%) .502 ≤ 0.54 240 (76.4%) 212 (79.1%) SII > 406.8 62 (19.7%) 50 (18.7%) .821 ≤ 406.8 252 (80.3%) 218 (81.3%) NLR > 1.96 236 (75.2%) 201 (75%) 1.000 ≤ 1.96 78 (24.8%) 67 (25%) PLR > 137.55 107 (34.1%) 94 (35.1%) .869 ≤ 137.55 207 (65.9%) 174 (64.9%) LMR > 6.01 281 (89.5%) 240 (89.6%) 1.000 130.77 34 (10.8%) 29 (10.8%) 1.000 ≤ 130.77 280 (89.2%) 239 (89.2%) REC > 1.14 185 (58.9%) 164 (61.2%) .636 ≤ 1.14 129 (41.1%) 104 (38.8%) RLC > 44.82 75 (23.9%) 59 (22%) .663 ≤ 44.82 239 (76.1%) 209 (78%) RNC > 61.26 204 (65%) 169 (63.1%) .695 ≤ 61.26 110 (35%) 99 (36.9%) RMC > 6.33 110 (35%) 117 (43.7%) .041 ≤ 6.33 204 (65%) 151 (56.3%) FPR > 0.95 198 (63.1%) 124 (46.3%) 6.57 242 (77.1%) 121 (45.1%) 53.15 195 (62.1%) 141 (52.6%) .026 ≤ 53.15 119 (37.9%) 127 (47.4%) NpCR: non-pathologic complete response, DpCR: discordant-pathologic complete response. Evaluating the prediction performance of NpCR and DpCR subgroups model in train set The ROC curve demonstrated that the overall AUC value of the model was 0.74 (95% CI: 0.691–0.788). The AUC values for the variables cN, HALP, FAR, Molecular Subtype, and RMC were 0.559 (95% CI: 0.5136–0.6042), 0.6232 (95% CI: 0.5748–0.6715), 0.5663 (95% CI: 0.5106–0.6221), 0.6023 (95% CI: 0.5481–0.6565), and 0.6665 (95% CI: 0.6132–0.7198), respectively (Fig. 4 A). The calibration curve showed that the average absolute error of the model was 0.044, indicating a relatively reliable predictive capability (Fig. 4 B). The DCA and CIC analysis demonstrated that the model exhibited good clinical utility for the NpCR and DpCR subgroups when the threshold probability was larger than 0.43 (Fig. 4 C-D). Evaluation of models prediction performance in validation set The formulas for the two multivariate logistic regression models were derived and their predictive performance was evaluated in the validation set. The ROC curve demonstrated an AUC of 0.7437 (95% CI: 0.6638–0.8236) for the T-NpCR vs TpCR group and 0.798 (95% CI: 0.7273–0.8704) for the NpCR vs DPCR group (Fig. 5 A, E). Calibration curves based on the validation set data showed that the observed value curves were close to the actual values (Fig. 5 B, F). Additionally, DCA and CIC curves indicated that nomograms of the models of T-NpCR vs TpCR group (Fig. 5 C, D) and NpCR vs DpCR group (Fig. 5 J, H) provided high predictive accuracy for the pathological response status of patients treated with NAT. Discussion This study included 789 patients with breast-related lymph node metastases at baseline status who underwent NAT. Molecular Subtype, HALP, P53, and FAR were identified as predictors of T-NpCR and TpCR groups by regression analysis, and prediction models were constructed and validated. The T-NpCR group was divided into two subgroups, NpCR and DpCR, for subgroup analyses. Kaplan-Meier curves demonstrated that the TpCR group had the greatest survival for OS and DFS, followed by the DpCR group, while the NpCR group had the worst. NpCR and DpCR prediction models based on cN, HALP, FAR, molecular subtype and RMC were constructed and their predictive performance was verified in the training and validation sets. Chen et al. showed that HR(-)HER2(+) subtype of breast cancer had the highest rate of BpCR and NpCR, and HR(+)HER2(-) subtype had the lowest rate of BpCR [ 18 ] . This indicates that the heterogeneity of different subtypes of tumors, different biological behaviors, and NAT regimens contributing to DpCR. It has been suggested that cancer cells in lymph nodes may have a tumor immune tolerance, and potential explanations include differences in chemotherapy sensitivity of metastatic tumor cells or the protective effect of the lymph node microenvironment on the tumor [ 19 ] . A study by Rene et al. showed that lymphatic dysfunction were more likely to have DpCR [ 20 ] . Previous studies have indicated that fibrinogen deposition, diminished immune response, or combination of chronic systemic diseases (e.g., diabetes mellitus) may contribute to lymphatic dysfunction. This ultimately results in the inadequate delivery of NAT drugs within the lymphatic system, or in the failure of to interact with tumor foci [ 21 , 22 ] . In this study, elevating peripheral blood fibrinogen was associated with a worse pathological response status. The two predictive models developed in this study indicated that molecular subtype of breast cancer was associated with different pathological response status. The analysis of the baseline characteristics revealed that patients with HER2-positive or triple-negative types were more likely to have better pathological response. Nevertheless, the precise mechanism remains to be elucidated through further investigation. To ascertain the mechanisms underlying DpCR, more in-depth studies are needed to identify which neoadjuvant treatment strategies may transform DpCR into TpCR status. Additionally, the axillary lymph node management strategies employed for patients undergoing NAT prove pivotal in enhancing long-term survival and reducing recurrence in breast cancer patients. [ 23 ] Peripheral blood immune cells can partially respond to the inflammatory state in the immune microenvironment, which is the theoretical basis for the hypothesis that peripheral blood inflammatory markers may predict tumor prognosis [ 24 ] . Abnormalities in coagulation can increase the risk of thrombosis and have a pro-tumorigenic effect, and indicators such as albumin and haemoglobin can reflect the overall nutritional status of the patient [ 25 – 27 ] . Peripheral blood inflammatory indexes have been shown in several studies to potentially predict prognostic status or NAT outcome in breast cancer [ 28 , 29 ] . However, the capability of these peripheral blood parameters to predict between the three different pCR status of NpCR, DpCR, and TpCR is still unclear. Our study screened peripheral blood inflammatory indexes and clinico-pathological features that might predict pathological response status after NAT by lasso regression, univariate and multivariate regression analyses, and screened parameters that had good ability to predict the three pathological response statuses two by two. Based on these parameters we plotted nomograms, and the AUC values of the two model groups were as follows: in the T-NpCR vs TpCR group: 0.803 (95% CI: 0.761–0.846) and in the NpCR vs DpCR group: 0.74 (95% CI: 0.691–0.788). In addition, we noticed that HALP and FAR appeared in both predictive models and had a longer share of the scoring axis in the nomogram compared to other predictors in the same group. This suggests that the combination of HALP and FAR may have a better ability to predict the extent of tumor remission after NAT in breast cancer. The HALP integrates four routinely collected indicators of immune and nutritional status and has been used as a new prognostic biomarker to predict many clinical outcomes in a variety of tumors. A meta analysis that included tumors such as gastric and cervical cancers showed that low HALP at baseline status was associated with poor prognosis of the tumor [ 30 ] . Lou et al. demonstrated that baseline HALP could be a predictor of whether or not to pCR after NAT in breast cancer. Using a cut-off value of 24.14, the OR for low HALP was 0.518 (95% CI: 0.365–0.734), and the area under the ROC curve for HALP was 0.847 [ 31 ] . Another 2022 study discussed whether HALP could be used as a predictor for the presence or absence of axillary lymph node involvement, and demonstrated that the rate of axillary lymph node involvement for HALP less than 29.01 was 67.7% and 53.3% for HALP greater than or equal to 29.01 (p = 0.038) [ 32 ] . In our study, the level of HALP was significantly lower in the NpCR group than in the TpCR group (45.7 ± 19.2 VS 56.6 ± 24.2). This phenomenon is consistent with previous studies. Interestingly, the HALP level in the DpCR group were lower than which in the NpCR group (45.7 ± 19.2 VS 40.9 ± 14.7) and the difference was statistically significant. The results of univariate and multivariate regression analyses also matched this trend. This suggests that the relationship between HALP score and prognosis may not be strictly positive. Also, we noted that platelets were highest in the DpCR group, followed by the NpCR group, and smallest in the TpCR group. The peripheral blood inflammation indexes associated with platelets were broadly consistent with this trend. Apart from platelets, haemoglobin, albumin and lymphocytes could not explain this trend. This implies that platelets and the coagulation system may play a role in the formation of DpCR, making neoadjuvant therapy less responsive in a subset of patients who may achieve TpCR or making oncological treatment slightly more effective in patients who may NpCR. FAR is a coagulation-inflammation-nutritional indicator of prognosis in a variety of solid tumors [ 33 – 37 ] . Since infection, blood coagulation, and so on affect plasma fibrinogen values, fibrinogen can somewhat represent the degree of inflammatory response [ 38 ] . Hwang et al. showed that patients with high FAR (cut-off value of 7.1) had a worse prognosis, and that univariate ( HR: 2.722, 95% CI: 1.659–4.468, P < 0.001) and multivariate (HR: 2.622, 95% CI: 1.455–4.724, P = 0.001) regression analyses also confirmed this [ 39 ] . Yang et al. set the cut-off value of FAR at 6.6 in their study, and survival analyses showed that high FAR implied worse OS and DFS [ 40 ] . In contrast, however, Zheng et al. reached the opposite result. This study concluded that low FAR (≤ 8.4) was protective for patients and that OS and DFS were worse with high FAR-PLR scores [ 41 ] . To date there are no studies discussing whether FAR can predict DpCR status after NAT. In our study, it was found that low FAR predict patients with better NAT responsiveness. The cut-off values of FAR were calculated from ROC curves to be 6.572 (NpCR vs. DpCR) and 5.513 (NpCR vs. TpCR) in the two groups, respectively. Unlike HALP, there was a more direct correlation between FAR and NAT outcome. That is, lower FAR indicate a better pathological response. Based on our findings, the coagulation system-related components, especially platelets and fibrinogen, may correlate with pathological response status after NAT. However, the mechanisms behind these findings still need to be supplemented and validated by further research. Despite the encouraging results, this study has several limitations: (1) There is a lack of an external validation cohort to test the conclusions; (2) The findings of this retrospective study should be validated by further prospective studies. What’s more, further validation in larger cohorts is required before the models can be applied in routine clinical practice. Conclusion In conclusion, this study identified potential factors affecting the outcome of NAT in breast cancer. FAR and HALP were found to be potential indicators that could be used to accurately predict pathological responses to NAT in breast cancer. Abbreviations Pathological complete response pCR Neoadjuvant therapy NAT Traditional NpCR T-NpCR Total pathological complete response-TpCR Discordant pathological complete response DpCR Non pCR NpCR American College of Surgeons Oncology Group ACOSOG Platelet to-lymphocyte ratio-PLR Neutrophil to-lymphocyte ratio-NLR Lymphocyte to-monocyte ratio-LMR Systemic immuno inflammatory index-SII Ductal carcinoma in situ DCIS Minimal residual disease MRD Fluorescence in situ hybridization FISH Pan Immune-Inflammation-value-PIV Systemic inflammatory response index SIRI The Hemoglobin, Albumin, Lymphocyte, Platelet Score HALP Fibrinogen Albumin Ratio-FAR Fibrinogen Platelet Ratio-FPR Disease free survival-DFS Overall survival OS Receiver operating characteristic curve ROC Clinical Impact Curves CIC Decision Curve Analysis DCA Declarations Acknowledgements Not applicable. Authors ’ contributions Shuqiang Liu, Cong Jiang, Shiyuan Zhang, Kun Qiao and Yuanxi Huang contributed to the concept and design of the study. Danping Wu, Xiaotian Yang and Boqian Yu contributed to the acquisition and interpretation of data and drafting the article. All authors read and approved the final version of the article. Funding The authors did not receive support from any organization for the submitted work. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethical approval and consent to participate This study was approved by the Clinical Research Ethics Committee of the Harbin Medical University Cancer Hospital. This research complies with the 1964 World Medical Association Declaration of Helsinki and subsequently amended versions. All patients were provided with written informed consent prior to participation. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China, 2 Department of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China References Conforti F, Pala L, Sala I, et al. Evaluation of pathological complete response as surrogate endpoint in neoadjuvant randomised clinical trials of early stage breast cancer: systematic review and meta-analysis. BMJ. 2021;375:e066381. Long-term outcomes for. neoadjuvant versus adjuvant chemotherapy in early breast cancer: meta-analysis of individual patient data from ten randomised trials. Lancet Oncol. 2018;19(1):27–39. Boughey JC, Ballman KV, McCall LM, et al. Tumor Biology and Response to Chemotherapy Impact Breast Cancer-specific Survival in Node-positive Breast Cancer Patients Treated With Neoadjuvant Chemotherapy: Long-term Follow-up From ACOSOG Z1071 (Alliance). Ann Surg. 2017;266(4):667–76. Hennessy BT, Hortobagyi GN, Rouzier R, et al. Outcome after pathologic complete eradication of cytologically proven breast cancer axillary node metastases following primary chemotherapy. J Clin Oncol. 2005;23(36):9304–11. Bhargava R, Beriwal S, Dabbs DJ, et al. Immunohistochemical surrogate markers of breast cancer molecular classes predicts response to neoadjuvant chemotherapy: a single institutional experience with 359 cases. Cancer. 2010;116(6):1431–9. Go J, Ahn JH, Park JM, et al. Distinct Prognosis of Minimal Residual Disease According to Breast Cancer Subtype in Patients with Breast or Nodal Pathologic Complete Response After Neoadjuvant Chemotherapy. Ann Surg Oncol. 2023;30(12):7060–8. Fayanju OM, Ren Y, Thomas SM, et al. The Clinical Significance of Breast-only and Node-only Pathologic Complete Response (pCR) After Neoadjuvant Chemotherapy (NACT): A Review of 20,000 Breast Cancer Patients in the National Cancer Data Base (NCDB). Ann Surg. 2018;268(4):591–601. Lee MK, Srour MK, Walcott-Sapp S, et al. Impact of the extent of pathologic complete response on outcomes after neoadjuvant chemotherapy. J Surg Oncol. 2020;121(2):210–5. Samiei S, Simons JM, Engelen S, Beets-Tan R, Classe JM, Smidt ML. Axillary Pathologic Complete Response After Neoadjuvant Systemic Therapy by Breast Cancer Subtype in Patients With Initially Clinically Node-Positive Disease: A Systematic Review and Meta-analysis. JAMA Surg. 2021;156(6):e210891. Tanaka A, Sakaguchi S. Regulatory T cells in cancer immunotherapy. Cell Res. 2017;27(1):109–18. Qi Q, Zhuang L, Shen Y, et al. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122(14):2158–67. Chen L, Kong X, Yan C, Fang Y, Wang J. The Research Progress on the Prognostic Value of the Common Hematological Parameters in Peripheral Venous Blood in Breast Cancer. Onco Targets Ther. 2020;13:1397–412. Fucà G, Guarini V, Antoniotti C, et al. The Pan-Immune-Inflammation Value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the Valentino and TRIBE first-line trials. Br J Cancer. 2020;123(3):403–9. Rimini M, Casadei-Gardini A, Ravaioli A, et al. Could Inflammatory Indices and Metabolic Syndrome Predict the Risk of Cancer Development? Analysis from the Bagnacavallo Population Study. J Clin Med. 2020;9(4):1177. Lee SY, Lee SI, Min BW, Oh SC. Prognostic implication of systemic inflammatory markers in young patients with resectable colorectal cancer. Ann Surg Treat Res. 2021;100(1):25–32. Xie J, Guo Z, Zhu Y, Ma M, Jia G. Peripheral blood inflammatory indexes in breast cancer: A review. Med (Baltim). 2023;102(48):e36315. Jones RL, Lakhani SR, Ring AE, Ashley S, Walsh G, Smith IE. Pathological complete response and residual DCIS following neoadjuvant chemotherapy for breast carcinoma. Br J Cancer. 2006;94(3):358–62. Chen SC, Yu CC, Chang HK, Lin YC, Huang YT. Discrepancy of Breast and Axillary Pathologic Complete Response and Outcomes in Different Subtypes of Node-positive Breast Cancer after Neoadjuvant Chemotherapy. J Cancer. 12(17): 5365–74. Reticker-Flynn NE, Zhang W, Belk JA, et al. Lymph node colonization induces tumor-immune tolerance to promote distant metastasis. Cell. 2022;185(11):1924–e194223. Flores R, Roldan E, Pardo JA, et al. Discordant Breast and Axillary Pathologic Response to Neoadjuvant Chemotherapy. Ann Surg Oncol. 2023;30(13):8302–7. Costantini V, Zacharski LR, Memoli VA, et al. Fibrinogen deposition and macrophage-associated fibrin formation in malignant and nonmalignant lymphoid tissue. J Lab Clin Med. 1992;119(2):124–31. Kuerer HM, Newman LA, Smith TL, et al. Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicin-based neoadjuvant chemotherapy. J Clin Oncol. 1999;17(2):460–9. Bundred NJ, Barnes NL, Rutgers E, Donker M. Is axillary lymph node clearance required in node-positive breast cancer. Nat Rev Clin Oncol. 2015;12(1):55–61. Jalali A, Miresse D, Fahey MR, et al. Peripheral Blood Cell Ratios as Prognostic Indicators in a Neoadjuvant Chemotherapy-Treated Breast Cancer Cohort. Curr Oncol. 2022;29(10):7512–23. Neman J, Termini J, Wilczynski S, et al. Human breast cancer metastases to the brain display GABAergic properties in the neural niche. Proc Natl Acad Sci U S A. 2014;111(3):984–9. So CL, Saunus JM, Roberts-Thomson SJ, Monteith GR. Calcium signalling and breast cancer. Semin Cell Dev Biol. 2019;94:74–83. Zhang C, Yang Z, Zhou P, et al. Phosphatidylserine-exposing tumor-derived microparticles exacerbate coagulation and cancer cell transendothelial migration in triple-negative breast cancer. Theranostics. 2021;11(13):6445–60. Yuan Y, Vora N, Sun CL, et al. Association of pre-chemotherapy peripheral blood pro-inflammatory and coagulation factors with reduced relative dose intensity in women with breast cancer. Breast Cancer Res. 2017;19(1):101. Yamanouchi K, Maeda S. The Efficacy of Inflammatory and Immune Markers for Predicting the Prognosis of Patients with Stage IV Breast Cancer. Acta Med Okayama. 2023;77(1):37–43. Xu H, Zheng X, Ai J, Yang L. Hemoglobin, albumin, lymphocyte, and platelet (HALP) score and cancer prognosis: A systematic review and meta-analysis of 13,110 patients. Int Immunopharmacol. 2023;114:109496. Lou C, Jin F, Zhao Q, Qi H. Correlation of serum NLR, PLR and HALP with efficacy of neoadjuvant chemotherapy and prognosis of triple-negative breast cancer. Am J Transl Res. 2022;14(5):3240–6. Duran A, Pulat H, Cay F, Topal U. Importance of HALP Score in Breast Cancer and its Diagnostic Value in Predicting Axillary Lymph Node Status. J Coll Physicians Surg Pak. 2022;32(6):734–9. Acharya P, Jakobleff WA, Forest SJ, et al. Fibrinogen Albumin Ratio and Ischemic Stroke During Venoarterial Extracorporeal Membrane Oxygenation. ASAIO J. 2020;66(3):277–82. Deng S, Fan Z, Xia H, et al. Fibrinogen/Albumin Ratio as a Promising Marker for Predicting Survival in Pancreatic Neuroendocrine Neoplasms. Cancer Manag Res. 2021;13:107–15. Chen W, Shan B, Zhou S, Yang H, Ye S. Fibrinogen/albumin ratio as a promising predictor of platinum response and survival in ovarian clear cell carcinoma. BMC Cancer. 2022;22(1):92. Wang S, Feng Y, Xie Y, et al. High fibrinogen-albumin ratio index (FARI) predicts poor survival in head and neck squamous cell carcinoma patients treated with surgical resection. Eur Arch Otorhinolaryngol. 2022;279(9):4541–8. Li Q, Zhang J, Gao Q, et al. Preoperative Fibrinogen Albumin Ratio is an Effective Biomarker for Prognostic Evaluation of Gallbladder Carcinoma After Radical Resection: A 10-Year Retrospective Study at a Single Center. J Inflamm Res. 2023;16:677–89. Zhang J, Ding Y, Wang W et al. Combining the Fibrinogen/Albumin Ratio and Systemic Inflammation Response Index Predicts Survival in Resectable Gastric Cancer. Gastroenterol Res Pract. 2020. 2020: 3207345. Hwang KT, Chung JK, Roh EY, et al. Prognostic Influence of Preoperative Fibrinogen to Albumin Ratio for Breast Cancer. J Breast Cancer. 2017;20(3):254–63. Yang Q, Liang D, Yu Y, Lv F. The Prognostic Significance of the Fibrinogen-to-Albumin Ratio in Patients With Triple-Negative Breast Cancer: A Retrospective Study. Front Surg. 2022;9:916298. Zheng Y, Wu C, Yan H, Chen S. Prognostic value of combined preoperative fibrinogen-albumin ratio and platelet-lymphocyte ratio score in patients with breast cancer: A prognostic nomogram study. Clin Chim Acta. 2020;506:110–21. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Oct, 2024 Read the published version in BMC Women's Health → Version 1 posted Editorial decision: Revision requested 23 Aug, 2024 Reviews received at journal 21 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviews received at journal 21 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers agreed at journal 20 Aug, 2024 Reviewers agreed at journal 20 Aug, 2024 Reviewers agreed at journal 20 Aug, 2024 Reviewers agreed at journal 20 Aug, 2024 Reviews received at journal 20 Aug, 2024 Reviewers agreed at journal 20 Aug, 2024 Reviewers agreed at journal 20 Aug, 2024 Reviewers invited by journal 20 Aug, 2024 Editor invited by journal 20 Aug, 2024 Editor assigned by journal 15 Aug, 2024 Submission checks completed at journal 15 Aug, 2024 First submitted to journal 15 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4917041","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344000925,"identity":"ac4edda6-dfb5-4479-a5e5-590f52120574","order_by":0,"name":"Shuqiang Liu","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuqiang","middleName":"","lastName":"Liu","suffix":""},{"id":344000926,"identity":"62109ff2-4a60-4f8c-941d-d38942154e3c","order_by":1,"name":"Cong Jiang","email":"","orcid":"","institution":"The Third Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Jiang","suffix":""},{"id":344000927,"identity":"0e89df31-74a4-49bc-b21a-6194365ac562","order_by":2,"name":"Danping Wu","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Danping","middleName":"","lastName":"Wu","suffix":""},{"id":344000928,"identity":"b665b2a8-5b39-44d2-abe4-8bf96af7c2a5","order_by":3,"name":"Shiyuan Zhang","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shiyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":344000929,"identity":"c0b743eb-e16d-4307-bc36-747759c9e3ba","order_by":4,"name":"Kun Qiao","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Qiao","suffix":""},{"id":344000930,"identity":"7cac1aa6-8a98-4bef-89a8-8919df1f39c5","order_by":5,"name":"Xiaotian Yang","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaotian","middleName":"","lastName":"Yang","suffix":""},{"id":344000931,"identity":"53fccc19-2b7a-4dfb-8c6a-217337dbe452","order_by":6,"name":"Boqian Yu","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Boqian","middleName":"","lastName":"Yu","suffix":""},{"id":344000932,"identity":"f138eafa-1f43-404d-9f22-2020763f84e4","order_by":7,"name":"Yuanxi Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAlUlEQVRIiWNgGAWjYHACZoYPBjZ2pGlhnFGQlkyaFmaeD4cYG4hWbz4jx9jYxuAAMwP74aMbiNIic+aMcXKOwR0+Bp60tBtEaZFg7918OMfgGTODBI8ZkVqYeTcftjA4zNhAvBagLckMpGnhOf/ZsMcgLZmNeL9IpCVL/PhjY8fPfvgYcVrggI005aNgFIyCUTAK8AIA/JIpUl9WwIcAAAAASUVORK5CYII=","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yuanxi","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-08-15 04:42:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4917041/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4917041/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-024-03400-9","type":"published","date":"2024-10-12T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66528425,"identity":"df3baefe-9982-4364-8447-53af6816ba38","added_by":"auto","created_at":"2024-10-14 05:27:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":217197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of key predictors of T-NpCR and TpCR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) Distribution of coefficients of cross-validation and lasso regression between traditional NpCR and TpCR group. (C) Univariate and multivariate logistic regression analyses between traditional NpCR and TpCR group. (D) Nomogram that predict whether breast cancer patient will achieve traditional NpCR or TpCR status after NAT.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4917041/v1/9f1164c714fdc576c3cd792f.png"},{"id":66528428,"identity":"8eac1e8c-08e9-4678-bfb2-7481d93fb25a","added_by":"auto","created_at":"2024-10-14 05:27:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":204463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of the prediction model of traditional NpCR and TpCR in training set.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) ROC curves; (B) Calibration curves; (C) Decision curve analysis (DCA) and (D) Clinical Impact Curve (CIC).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4917041/v1/5f07744fbcbb84a78172f969.png"},{"id":66528361,"identity":"a700e0a6-174d-4b10-a32a-f5fad4ff2e8b","added_by":"auto","created_at":"2024-10-14 05:26:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":356503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival of breast cancer patients with different pathological response status and identification of key predictors of NpCR and DpCR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) K-M curve of OS. (B) K-M curve of DFS. (C-D) Distribution of coefficients of cross-validation and lasso regression between NpCR and DpCR group. (E) Univariate and multivariate logistic regression analyses between NpCR and DpCR group. (F) Nomogram that predict whether breast cancer patient will achieve NpCR or DpCR status after NAT.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4917041/v1/e512f5c6a04ce75e6ff53177.png"},{"id":66528359,"identity":"be7e3e94-a6e0-49ef-bef1-2ef194351476","added_by":"auto","created_at":"2024-10-14 05:26:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":192683,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of the prediction model of NpCR and DpCR in training set.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) ROC curves; (B) Calibration curves; (C) DCA and (D) CIC.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4917041/v1/6b3a472ac4fd98b9181336b4.png"},{"id":66528379,"identity":"e1b73426-2609-4500-bf1b-2e909780bedc","added_by":"auto","created_at":"2024-10-14 05:27:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":165679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluating the two prediction models separately in the validation set using ROC curves, calibration curves, DCA and CIC\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e(A-D) T-NpCR and TpCR model; (E-H) NpCR and DpCR model.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4917041/v1/e549cb30b234ce09e691a1b7.png"},{"id":66597866,"identity":"4f48bf2d-36be-4a73-acb8-ae9558be1bfd","added_by":"auto","created_at":"2024-10-14 16:11:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2038286,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4917041/v1/7453c5e5-6121-44fa-a5ae-dfc32bb107f3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of Predictive Models for Pathological Response Status in Breast Cancer after Neoadjuvant Therapy Based on Peripheral Blood Inflammatory Indexes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLymph node metastasis constitutes a pivotal determinant in the recurrence and metastatic progression of breast cancer. Neoadjuvant therapy (NAT) has become the preferred treatment for node-positive breast cancer\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Total-pathological complete response (TpCR) is defined as the absence of significant residual tumor in both the breast and lymph nodes by post-NAT pathological examination, serving as a critical indicator of favorable prognosis\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. However, previous studies have indicated the potential for discrepancies in the response of breast lesions and lymph nodes to neoadjuvant therapy. The American College of Surgeons Oncology Group (ACOSOG) reported a pCR rate of 27.8% in the breast and axilla in the Z1071 study, compared with only 5.7% in the breast alone and 13.1% in the lymph nodes alone\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. This phenomenon, where the tumor disappears in only one part of the breast and axillary lymph nodes, while remaining in another part, is known as a discordant pathological complete response (DpCR). Previous studies have generally considered DpCR as not having achieved complete pathological remission and therefore classified them into the NpCR group. However, in fact, patients with DpCR represent a unique prognostic group whose prognosis is intermediate between that of TpCR and non-pathological complete remission (NpCR) group\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. DpCR is a transitional state which could partly response to neoadjuvant therapy. It suggests that it may be possible to convert DpCR to TpCR\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.Therefore, DpCR patients should be studied in isolation from traditional NpCR (T-NpCR).\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e A more precise estimation of the pathological response of breast cancer prior to NAT has significant implications for the assessment of disease risk and the development of therapeutic strategies. Research on the clinic features and prediction model for DpCR status is currently insufficient, requiring further scientific investigations to address this deficiency.\u003c/p\u003e \u003cp\u003eBreast cancer cells can interact with peripheral stromal and inflammatory cells to form inflammatory tumor microenvironment, which contributes to tumorigenesis, progression, invasion and chemoresistance, affecting patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Peripheral blood inflammatory index including platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and systemic immuno-inflammatory index (SII) exhibit a significant correlation with the prognosis of BC patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Numerous retrospective clinical trials in breast cancer have used peripheral blood inflammatory indices to predict TpCR in NAT\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, there is a lack of research investigating the predictive potential of peripheral blood inflammatory index for DpCR.\u003c/p\u003e \u003cp\u003eThis study aimed to identify clinic features and peripheral blood inflammatory indexes that could be used to predict pathological response degree following NAT. We developed two prediction models and validated the predictive ability of the models.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Selection\u003c/h2\u003e \u003cp\u003eFemale patients with breast cancer who were treated with NAT from January 2011 to December 2023 at Harbin Medical University Cancer Hospital were recruited into this retrospective study. The inclusion criteria are as follows: (1) all patients underwent ultrasound-guided aspiration biopsy of the breast and associated lymph nodes before NAT. The biopsy pathology confirmed the presence of tumour metastases in these lymph nodes; (2) received standard neoadjuvant treatment program. All neoadjuvant regimens are based on NCCN guidelines; (3) Receive surgical resection and axillary lymph node dissection or sentinel lymph node biopsy. Patients with either of the following situations were excluded: (1) history of prior or concurrent breast cancer; (2) evidence of distant metastasis; (3) receiving less than two cycles of NAT; (4) abandoning NAT in the middle of process due to intolerable side-effects.\u003c/p\u003e \u003cp\u003e This study was approved by the Clinical Research Ethics Committee of the Harbin Medical University Cancer Hospital. This research complies with the 1964 World Medical Association Declaration of Helsinki and subsequently amended versions. All patients were provided with written informed consent prior to participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of different pathological reaction statuses after NAT\u003c/h2\u003e \u003cp\u003eAll samples were evaluated using the AJCC ypTNM criteria. TpCR was defined as postoperative pathology in which no evidence of tumor residue was found in either the breast lesion or the lymph node (ypT0/ypN0). DpCR was defined as postoperative pathology in which tumor residue was found in one of the two, but not the other, in either the breast lesion or the lymph node (ypT\u0026thinsp;\u0026ge;\u0026thinsp;1/ypN0 or ypT0/ypN\u0026thinsp;\u0026ge;\u0026thinsp;1). Breast pCR or lymph node pCR are collectively known as DpCR, as previous studies have shown that they have the same survival\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. NpCR was defined as postoperative pathology in which tumor residue was found in both the breast lesion and the lymph node. The combination of DpCR and NpCR was described as T-NpCR, which is the traditional meaning of not achieving pCR after NAT. In addition, previous studies have demonstrated that survival rates for ductal carcinoma in situ (DCIS) or minimal residual disease (MRD) in the breast or lymph nodes show no significant difference from those observed for TpCR. Consequently, these two conditions have been included in the TpCR group\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePathologic features\u003c/h2\u003e \u003cp\u003e Referring to the 2020 edition of the ASCO guidelines, an IHC score of \u0026ge;\u0026thinsp;1% for ER or PR was defined as positive. IHC score of 0 or 1\u0026thinsp;+\u0026thinsp;for HER2 was considered negative. In cases with an IHC score of 2+, fluorescence in situ hybridization (FISH) must be performed in addition to IHC. IHC score of 3 (\u0026gt;\u0026thinsp;10% of cells showing high-intensity periplasmic staining) or FISH positivity was considered positive. Ki-67 was defined as low expression if the proportion of positively stained cells was less than 15%. It was defined as moderate expression when the percentage of positively stained cells was between 15\u0026ndash;29%. The high expression was defined when the percentage of positively stained cells was \u0026ge;\u0026thinsp;29%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePeripheral blood inflammation index\u003c/h2\u003e \u003cp\u003eAll peripheral blood inflammation index were calculated based on the haematology of the patients 7 days before they underwent NAT. The calculation of each index of peripheral blood inflammation was as follow: NLR (Neutrophil count/ Lymphocyte count), Pan-Immune-Inflammation-value (PIV, Platelet count \u0026times;Neutrophil count \u0026times; Monocytes count/ Lymphocyte count), Systemic inflammatory response index (SIRI, Neutrophil count \u0026times;Monocyte count/ Lymphocyte count), the Hemoglobin, Albumin, Lymphocyte, Platelet Score (HALP, Hemoglobin count \u0026times; Albumin \u0026times; Lymphocyte count/ Platelet count ), the Fibrinogen-Albumin Ratio (FAR, Fibrinogen\u0026times;100/ Albumin), the Fibrinogen-Platelet Ratio (FPR, Fibrinogen/ Platelet count), the Systemic Immunoinflammatory Index (SII, Platelet count \u0026times; Neutrophil count/ Lymphocyte count), LMR (Lymphocyte count/ Monocyte count), PLR (Platelet count/ Lymphocyte count).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up\u003c/h2\u003e \u003cp\u003e All patients received regular follow-up after completing treatment according to clinical guidelines. The follow-up programme was conducted at the following intervals: every three to four months for the initial two years, every six months for the subsequent three years, and annually thereafter. Participants were defined as having lost to follow-up if there was an interval of more than two years between the last visit and the final follow-up. All patients followed up until 30 January 2024. Disease-free survival (DFS) was defined as the interval between diagnosis and either breast cancer recurrence, death from any cause, or the date of the final follow-up. Similarly, overall survival (OS) was defined as the time from diagnosis to either death or the date of the final follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThis study applied RStudio (version 4.3.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software for statistical analysis. The correlation between different groups was analyzed using the chi-square test. Lasso regression was employed to identify predictive features based on non-zero coefficients. The optimal parameter (lambda) selection in the Lasso model was cross-validated ten-fold based on the minimum criterion. The receiver operating characteristic curve (ROC) was employed to determine the best cut-off value. Kaplan-Meier curves and log-rank tests were employed to plot and compare the DFS and OS curves. The patients were randomly divided into a training and validation set in a 7:3 ratio. Lasso regression analysis, uni/multivariate logistic regression analysis were performed to identify factors associated with pCR status after NAT. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered a statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline of patients achieve T-NpCR and TpCR\u003c/h2\u003e \u003cp\u003eA total of 789 patients were included in the study. Of 207 (26.24%) achieved TPCR, while 582 (73.76%) were evaluated for T-NpCR. The baseline clinic features and peripheral blood inflammatory indexes of patients are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A chi-square test was performed for all variables. Molecular Subtype, Ki67, P53, NAT cycle, NAT regimen, HER2 Targeted Therapy, Carboplatin, SII, NLR, PLR, PIV, REC, RNC, RMC, FPR, FAR, HALP was found to be significantly different between the two groups. Therefore, these 17 variables were included in the screening process of predictors.\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\u003eClinical baseline characteristics between T-NpCR and TpCR patient.\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT-NpCR (N\u0026thinsp;=\u0026thinsp;582)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTpCR (N\u0026thinsp;=\u0026thinsp;207)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478 (82.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178 (86%)\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\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344 (59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (59.9%)\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\u003eMenstrual state\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePremenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (48.8%)\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\u003eLesion number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMono\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462 (79.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (82.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (17.4%)\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\u003ecT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e476 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (86.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026thinsp;+\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (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\u003ecN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (39.1%)\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\u003eMolecular Subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(+)HER2(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e343 (58.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(+)HER2(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (17.9%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(-)HER2(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (32.9%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(-)HER2(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (36.7%)\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\u003eKi67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e287 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15% \u0026minus;\u0026thinsp;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (19.8%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (9.2%)\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\u003eP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (10.1%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (7.2%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (14.5%)\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\u003eNAT cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400 (68.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133 (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\u003eHER2 Targeted Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e472 (81.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (65.2%)\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\u003ePDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;13.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (40.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;13.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e325 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (59.4%)\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\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e452 (77.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (73.9%)\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\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;839.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;839.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e470 (80.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (90.3%)\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\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e437 (75.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (24.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (32.9%)\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\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;162.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;162.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158 (76.3%)\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\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e521 (89.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191 (92.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (7.7%)\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\u003ePIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;462.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;462.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e519 (89.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195 (94.2%)\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\u003eREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (30%)\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\u003eRLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e448 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147 (71%)\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\u003eRNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;57.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e373 (64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;57.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (46.4%)\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\u003eRMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e355 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (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\u003eFPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322 (55.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (63.3%)\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\u003eFAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (69.6%)\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\u003eHALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;37.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e336 (57.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160 (77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;37.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (22.7%)\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\"\u003eT-NpCR: traditional non-pathologic complete response, DpCR: discordant-pathologic complete response, TpCR: total-pathologic complete response,BMI: body mass index, PDW: platelet distribution width, REC: relative eosinophilic count, RLC: relative lymphocyte count, RNC: relative neutrophil count, RMC: relative macrophage cell count.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of predictors of the T-NpCR and TpCR group\u003c/h2\u003e \u003cp\u003e All patients were randomly divided into validation set (n\u0026thinsp;=\u0026thinsp;267) and training set (n\u0026thinsp;=\u0026thinsp;522) according to a 7:3 ratio. Four parameters (molecular subtypes, P53, FAR, and HALP) were identified as potential predictors by Lasso (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B) and uni/multivariate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) logistic regression analysis. Based on these predictors, nomogram were constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEvaluating the prediction performance of T-NpCR and TpCR group model in train set\u003c/h2\u003e \u003cp\u003eThe ROC curve showed that AUC value of the model was 0.803 (95% CI: 0.761\u0026ndash;0.846), and the AUC values of Molecular Subtype, FAR, HALP and P53 were 0.728 (95%CI: 0.684\u0026ndash;0.772), 0.701 (95%CI: 0.653\u0026ndash;0.749), 0.612 (95%CI: 0.56\u0026ndash;0.664), 0.602 (95%CI: 0.549\u0026ndash;0.656) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The calibration curve demonstrated that the average absolute error of the model was 0.014, indicating that the model exhibited enhanced predictive capability (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). To assess the clinical utility and predictive capacity of nomograms, DCA and CIC were plotted. The models in the T-NpCR and TpCR group exhibited superior clinical utility when the threshold probability values were within the range of 0.35 to 0.77 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBaseline and prognosis of patients achieve NpCR and DpCR\u003c/h2\u003e \u003cp\u003eTo further understand the prognosis of patients with different extent of pathological response after NAT for breast cancer, the T-NpCR cohort was divided into two subgroups: NpCR and DpCR. The mean follow-up period for the entire cohort was 49 months (3\u0026ndash;88 months). Based on the follow-up data, K-M curves were plotted for patients in the three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). The result demonstrated that patients in the TpCR group exhibited the best OS and DFS, followed by the DpCR group, and the NpCR group proved to be the worst. The observed differences in survival between each two groups were statistically significant. Thus, DpCR represents a subgroup with a distinct prognosis, making it scientifically meaningful to study it separately from T-NpCR. The baseline clinical characteristics of the NpCR and DpCR groups are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among them, 314 patients were assessed as NpCR, and 268 patients were assessed as DpCR. Chi-square tests revealed statistically significant differences between the groups for the variables Lesion number, cN, Molecular Subtype, P53, NAT regimen, HER2 targeted therapy, RMC, FPR, FAR, and HALP. These 10 variables were included in the predictor selection process for model construction. Five parameters (cN, Molecular Subtype, RMC, HALP and FAR) were identified as potential predictors by Lasso (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D) and uni/multivariate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) logistic regression analysis. Based on these predictors, nomogram were constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical baseline characteristics between NpCR and DpCR group.\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNpCR (N\u0026thinsp;=\u0026thinsp;314)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDpCR (N\u0026thinsp;=\u0026thinsp;268)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256 (81.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222 (82.8%)\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\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (32.1%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (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\u003eMenstrual state\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePremenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (48.5%)\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\u003eLesion number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMono\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234 (74.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228 (85.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (14.9%)\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\u003ecT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e255 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221 (82.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (17.5%)\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\u003ecN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191 (71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (28.7%)\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\u003eMolecular Subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(+)HER2(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (46.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(+)HER2(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (17.5%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(-)HER2(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (16.4%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(-)HER2(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (19.4%)\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\u003eKi67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143 (53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15% \u0026minus;\u0026thinsp;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (26.9%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (19.8%)\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\u003eP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (31.3%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (15.3%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (10.8%)\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\u003eNAT cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244 (77.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182 (67.9%)\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\u003eHER2 targeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263 (83.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204 (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\u003ePDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;13.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;13.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (56.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (55.6%)\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\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240 (76.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212 (79.1%)\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\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;406.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;406.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252 (80.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e218 (81.3%)\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\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236 (75.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (25%)\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 \u003cp\u003e\u0026gt;\u0026thinsp;137.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;137.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207 (65.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174 (64.9%)\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\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281 (89.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240 (89.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (10.4%)\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\u003ePIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;130.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;130.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 (89.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239 (89.2%)\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\u003eREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185 (58.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164 (61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (38.8%)\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\u003eRLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;44.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;44.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239 (76.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e209 (78%)\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\u003eRNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;61.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (63.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;61.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (36.9%)\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\u003eRMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151 (56.3%)\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\u003eFPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (63.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (36.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (53.7%)\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\u003eFAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242 (77.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147 (54.9%)\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\u003eHALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;53.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;53.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (47.4%)\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\"\u003eNpCR: non-pathologic complete response, DpCR: discordant-pathologic complete response.\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\u003eEvaluating the prediction performance of NpCR and DpCR subgroups model in train set\u003c/h2\u003e \u003cp\u003eThe ROC curve demonstrated that the overall AUC value of the model was 0.74 (95% CI: 0.691\u0026ndash;0.788). The AUC values for the variables cN, HALP, FAR, Molecular Subtype, and RMC were 0.559 (95% CI: 0.5136\u0026ndash;0.6042), 0.6232 (95% CI: 0.5748\u0026ndash;0.6715), 0.5663 (95% CI: 0.5106\u0026ndash;0.6221), 0.6023 (95% CI: 0.5481\u0026ndash;0.6565), and 0.6665 (95% CI: 0.6132\u0026ndash;0.7198), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The calibration curve showed that the average absolute error of the model was 0.044, indicating a relatively reliable predictive capability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The DCA and CIC analysis demonstrated that the model exhibited good clinical utility for the NpCR and DpCR subgroups when the threshold probability was larger than 0.43 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of models prediction performance in validation set\u003c/h2\u003e \u003cp\u003eThe formulas for the two multivariate logistic regression models were derived and their predictive performance was evaluated in the validation set. The ROC curve demonstrated an AUC of 0.7437 (95% CI: 0.6638\u0026ndash;0.8236) for the T-NpCR vs TpCR group and 0.798 (95% CI: 0.7273\u0026ndash;0.8704) for the NpCR vs DPCR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, E). Calibration curves based on the validation set data showed that the observed value curves were close to the actual values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, F). Additionally, DCA and CIC curves indicated that nomograms of the models of T-NpCR vs TpCR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D) and NpCR vs DpCR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ, H) provided high predictive accuracy for the pathological response status of patients treated with NAT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study included 789 patients with breast-related lymph node metastases at baseline status who underwent NAT. Molecular Subtype, HALP, P53, and FAR were identified as predictors of T-NpCR and TpCR groups by regression analysis, and prediction models were constructed and validated. The T-NpCR group was divided into two subgroups, NpCR and DpCR, for subgroup analyses. Kaplan-Meier curves demonstrated that the TpCR group had the greatest survival for OS and DFS, followed by the DpCR group, while the NpCR group had the worst. NpCR and DpCR prediction models based on cN, HALP, FAR, molecular subtype and RMC were constructed and their predictive performance was verified in the training and validation sets.\u003c/p\u003e \u003cp\u003eChen et al. showed that HR(-)HER2(+) subtype of breast cancer had the highest rate of BpCR and NpCR, and HR(+)HER2(-) subtype had the lowest rate of BpCR\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. This indicates that the heterogeneity of different subtypes of tumors, different biological behaviors, and NAT regimens contributing to DpCR. It has been suggested that cancer cells in lymph nodes may have a tumor immune tolerance, and potential explanations include differences in chemotherapy sensitivity of metastatic tumor cells or the protective effect of the lymph node microenvironment on the tumor\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. A study by Rene et al. showed that lymphatic dysfunction were more likely to have DpCR\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Previous studies have indicated that fibrinogen deposition, diminished immune response, or combination of chronic systemic diseases (e.g., diabetes mellitus) may contribute to lymphatic dysfunction. This ultimately results in the inadequate delivery of NAT drugs within the lymphatic system, or in the failure of to interact with tumor foci\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In this study, elevating peripheral blood fibrinogen was associated with a worse pathological response status. The two predictive models developed in this study indicated that molecular subtype of breast cancer was associated with different pathological response status. The analysis of the baseline characteristics revealed that patients with HER2-positive or triple-negative types were more likely to have better pathological response. Nevertheless, the precise mechanism remains to be elucidated through further investigation. To ascertain the mechanisms underlying DpCR, more in-depth studies are needed to identify which neoadjuvant treatment strategies may transform DpCR into TpCR status. Additionally, the axillary lymph node management strategies employed for patients undergoing NAT prove pivotal in enhancing long-term survival and reducing recurrence in breast cancer patients.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePeripheral blood immune cells can partially respond to the inflammatory state in the immune microenvironment, which is the theoretical basis for the hypothesis that peripheral blood inflammatory markers may predict tumor prognosis\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Abnormalities in coagulation can increase the risk of thrombosis and have a pro-tumorigenic effect, and indicators such as albumin and haemoglobin can reflect the overall nutritional status of the patient\u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Peripheral blood inflammatory indexes have been shown in several studies to potentially predict prognostic status or NAT outcome in breast cancer\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. However, the capability of these peripheral blood parameters to predict between the three different pCR status of NpCR, DpCR, and TpCR is still unclear. Our study screened peripheral blood inflammatory indexes and clinico-pathological features that might predict pathological response status after NAT by lasso regression, univariate and multivariate regression analyses, and screened parameters that had good ability to predict the three pathological response statuses two by two. Based on these parameters we plotted nomograms, and the AUC values of the two model groups were as follows: in the T-NpCR vs TpCR group: 0.803 (95% CI: 0.761\u0026ndash;0.846) and in the NpCR vs DpCR group: 0.74 (95% CI: 0.691\u0026ndash;0.788). In addition, we noticed that HALP and FAR appeared in both predictive models and had a longer share of the scoring axis in the nomogram compared to other predictors in the same group. This suggests that the combination of HALP and FAR may have a better ability to predict the extent of tumor remission after NAT in breast cancer.\u003c/p\u003e \u003cp\u003eThe HALP integrates four routinely collected indicators of immune and nutritional status and has been used as a new prognostic biomarker to predict many clinical outcomes in a variety of tumors. A meta analysis that included tumors such as gastric and cervical cancers showed that low HALP at baseline status was associated with poor prognosis of the tumor\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Lou et al. demonstrated that baseline HALP could be a predictor of whether or not to pCR after NAT in breast cancer. Using a cut-off value of 24.14, the OR for low HALP was 0.518 (95% CI: 0.365\u0026ndash;0.734), and the area under the ROC curve for HALP was 0.847\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Another 2022 study discussed whether HALP could be used as a predictor for the presence or absence of axillary lymph node involvement, and demonstrated that the rate of axillary lymph node involvement for HALP less than 29.01 was 67.7% and 53.3% for HALP greater than or equal to 29.01 (p\u0026thinsp;=\u0026thinsp;0.038)\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In our study, the level of HALP was significantly lower in the NpCR group than in the TpCR group (45.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2 VS 56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;24.2). This phenomenon is consistent with previous studies. Interestingly, the HALP level in the DpCR group were lower than which in the NpCR group (45.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2 VS 40.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7) and the difference was statistically significant. The results of univariate and multivariate regression analyses also matched this trend. This suggests that the relationship between HALP score and prognosis may not be strictly positive. Also, we noted that platelets were highest in the DpCR group, followed by the NpCR group, and smallest in the TpCR group. The peripheral blood inflammation indexes associated with platelets were broadly consistent with this trend. Apart from platelets, haemoglobin, albumin and lymphocytes could not explain this trend. This implies that platelets and the coagulation system may play a role in the formation of DpCR, making neoadjuvant therapy less responsive in a subset of patients who may achieve TpCR or making oncological treatment slightly more effective in patients who may NpCR.\u003c/p\u003e \u003cp\u003eFAR is a coagulation-inflammation-nutritional indicator of prognosis in a variety of solid tumors\u003csup\u003e[\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Since infection, blood coagulation, and so on affect plasma fibrinogen values, fibrinogen can somewhat represent the degree of inflammatory response\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Hwang et al. showed that patients with high FAR (cut-off value of 7.1) had a worse prognosis, and that univariate ( HR: 2.722, 95% CI: 1.659\u0026ndash;4.468, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and multivariate (HR: 2.622, 95% CI: 1.455\u0026ndash;4.724, P\u0026thinsp;=\u0026thinsp;0.001) regression analyses also confirmed this\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Yang et al. set the cut-off value of FAR at 6.6 in their study, and survival analyses showed that high FAR implied worse OS and DFS\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. In contrast, however, Zheng et al. reached the opposite result. This study concluded that low FAR (\u0026le;\u0026thinsp;8.4) was protective for patients and that OS and DFS were worse with high FAR-PLR scores\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. To date there are no studies discussing whether FAR can predict DpCR status after NAT. In our study, it was found that low FAR predict patients with better NAT responsiveness. The cut-off values of FAR were calculated from ROC curves to be 6.572 (NpCR vs. DpCR) and 5.513 (NpCR vs. TpCR) in the two groups, respectively. Unlike HALP, there was a more direct correlation between FAR and NAT outcome. That is, lower FAR indicate a better pathological response. Based on our findings, the coagulation system-related components, especially platelets and fibrinogen, may correlate with pathological response status after NAT. However, the mechanisms behind these findings still need to be supplemented and validated by further research.\u003c/p\u003e \u003cp\u003eDespite the encouraging results, this study has several limitations: (1) There is a lack of an external validation cohort to test the conclusions; (2) The findings of this retrospective study should be validated by further prospective studies. What\u0026rsquo;s more, further validation in larger cohorts is required before the models can be applied in routine clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study identified potential factors affecting the outcome of NAT in breast cancer. FAR and HALP were found to be potential indicators that could be used to accurately predict pathological responses to NAT in breast cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePathological complete response\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNeoadjuvant therapy\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNAT\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTraditional NpCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT-NpCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTotal\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epathological complete response-TpCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDiscordant pathological complete response\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDpCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNon pCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNpCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAmerican College of Surgeons Oncology Group\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eACOSOG\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePlatelet\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eto-lymphocyte ratio-PLR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNeutrophil\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eto-lymphocyte ratio-NLR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLymphocyte\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eto-monocyte ratio-LMR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSystemic immuno\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einflammatory index-SII\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDuctal carcinoma in situ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDCIS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMinimal residual disease\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMRD\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFluorescence in situ hybridization\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePan\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmune-Inflammation-value-PIV\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSystemic inflammatory response index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eThe Hemoglobin, Albumin, Lymphocyte, Platelet Score\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHALP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFibrinogen\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlbumin Ratio-FAR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFibrinogen\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet Ratio-FPR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDisease\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efree survival-DFS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOverall survival\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eReceiver operating characteristic curve\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eROC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eClinical Impact Curves\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCIC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDecision Curve Analysis\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDCA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\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\u003eShuqiang Liu,\u0026nbsp;Cong Jiang,\u0026nbsp;Shiyuan Zhang, Kun Qiao\u0026nbsp;and\u0026nbsp;Yuanxi Huang\u0026nbsp;contributed to the concept and design of the study.\u0026nbsp;Danping Wu, Xiaotian Yang\u0026nbsp;and\u0026nbsp;Boqian Yu\u0026nbsp;contributed to the acquisition and interpretation of data and drafting the article. All authors read and approved the final version of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Clinical Research Ethics Committee of the Harbin Medical University Cancer Hospital. This research complies with the 1964 World Medical Association Declaration of Helsinki and subsequently amended versions. All patients were provided with written informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent 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 no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China, \u003csup\u003e2\u003c/sup\u003eDepartment of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eConforti F, Pala L, Sala I, et al. Evaluation of pathological complete response as surrogate endpoint in neoadjuvant randomised clinical trials of early stage breast cancer: systematic review and meta-analysis. BMJ. 2021;375:e066381.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong-term outcomes for. neoadjuvant versus adjuvant chemotherapy in early breast cancer: meta-analysis of individual patient data from ten randomised trials. Lancet Oncol. 2018;19(1):27\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoughey JC, Ballman KV, McCall LM, et al. Tumor Biology and Response to Chemotherapy Impact Breast Cancer-specific Survival in Node-positive Breast Cancer Patients Treated With Neoadjuvant Chemotherapy: Long-term Follow-up From ACOSOG Z1071 (Alliance). Ann Surg. 2017;266(4):667\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHennessy BT, Hortobagyi GN, Rouzier R, et al. Outcome after pathologic complete eradication of cytologically proven breast cancer axillary node metastases following primary chemotherapy. J Clin Oncol. 2005;23(36):9304\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhargava R, Beriwal S, Dabbs DJ, et al. Immunohistochemical surrogate markers of breast cancer molecular classes predicts response to neoadjuvant chemotherapy: a single institutional experience with 359 cases. Cancer. 2010;116(6):1431\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGo J, Ahn JH, Park JM, et al. Distinct Prognosis of Minimal Residual Disease According to Breast Cancer Subtype in Patients with Breast or Nodal Pathologic Complete Response After Neoadjuvant Chemotherapy. Ann Surg Oncol. 2023;30(12):7060\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFayanju OM, Ren Y, Thomas SM, et al. The Clinical Significance of Breast-only and Node-only Pathologic Complete Response (pCR) After Neoadjuvant Chemotherapy (NACT): A Review of 20,000 Breast Cancer Patients in the National Cancer Data Base (NCDB). Ann Surg. 2018;268(4):591\u0026ndash;601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee MK, Srour MK, Walcott-Sapp S, et al. Impact of the extent of pathologic complete response on outcomes after neoadjuvant chemotherapy. J Surg Oncol. 2020;121(2):210\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamiei S, Simons JM, Engelen S, Beets-Tan R, Classe JM, Smidt ML. Axillary Pathologic Complete Response After Neoadjuvant Systemic Therapy by Breast Cancer Subtype in Patients With Initially Clinically Node-Positive Disease: A Systematic Review and Meta-analysis. JAMA Surg. 2021;156(6):e210891.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka A, Sakaguchi S. Regulatory T cells in cancer immunotherapy. Cell Res. 2017;27(1):109\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi Q, Zhuang L, Shen Y, et al. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122(14):2158\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Kong X, Yan C, Fang Y, Wang J. The Research Progress on the Prognostic Value of the Common Hematological Parameters in Peripheral Venous Blood in Breast Cancer. Onco Targets Ther. 2020;13:1397\u0026ndash;412.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuc\u0026agrave; G, Guarini V, Antoniotti C, et al. The Pan-Immune-Inflammation Value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the Valentino and TRIBE first-line trials. Br J Cancer. 2020;123(3):403\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRimini M, Casadei-Gardini A, Ravaioli A, et al. Could Inflammatory Indices and Metabolic Syndrome Predict the Risk of Cancer Development? Analysis from the Bagnacavallo Population Study. J Clin Med. 2020;9(4):1177.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SY, Lee SI, Min BW, Oh SC. Prognostic implication of systemic inflammatory markers in young patients with resectable colorectal cancer. Ann Surg Treat Res. 2021;100(1):25\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie J, Guo Z, Zhu Y, Ma M, Jia G. Peripheral blood inflammatory indexes in breast cancer: A review. Med (Baltim). 2023;102(48):e36315.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones RL, Lakhani SR, Ring AE, Ashley S, Walsh G, Smith IE. Pathological complete response and residual DCIS following neoadjuvant chemotherapy for breast carcinoma. Br J Cancer. 2006;94(3):358\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen SC, Yu CC, Chang HK, Lin YC, Huang YT. Discrepancy of Breast and Axillary Pathologic Complete Response and Outcomes in Different Subtypes of Node-positive Breast Cancer after Neoadjuvant Chemotherapy. J Cancer. 12(17): 5365\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReticker-Flynn NE, Zhang W, Belk JA, et al. Lymph node colonization induces tumor-immune tolerance to promote distant metastasis. Cell. 2022;185(11):1924\u0026ndash;e194223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlores R, Roldan E, Pardo JA, et al. Discordant Breast and Axillary Pathologic Response to Neoadjuvant Chemotherapy. Ann Surg Oncol. 2023;30(13):8302\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCostantini V, Zacharski LR, Memoli VA, et al. Fibrinogen deposition and macrophage-associated fibrin formation in malignant and nonmalignant lymphoid tissue. J Lab Clin Med. 1992;119(2):124\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuerer HM, Newman LA, Smith TL, et al. Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicin-based neoadjuvant chemotherapy. J Clin Oncol. 1999;17(2):460\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBundred NJ, Barnes NL, Rutgers E, Donker M. Is axillary lymph node clearance required in node-positive breast cancer. Nat Rev Clin Oncol. 2015;12(1):55\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJalali A, Miresse D, Fahey MR, et al. Peripheral Blood Cell Ratios as Prognostic Indicators in a Neoadjuvant Chemotherapy-Treated Breast Cancer Cohort. Curr Oncol. 2022;29(10):7512\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeman J, Termini J, Wilczynski S, et al. Human breast cancer metastases to the brain display GABAergic properties in the neural niche. Proc Natl Acad Sci U S A. 2014;111(3):984\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSo CL, Saunus JM, Roberts-Thomson SJ, Monteith GR. Calcium signalling and breast cancer. Semin Cell Dev Biol. 2019;94:74\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, Yang Z, Zhou P, et al. Phosphatidylserine-exposing tumor-derived microparticles exacerbate coagulation and cancer cell transendothelial migration in triple-negative breast cancer. Theranostics. 2021;11(13):6445\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan Y, Vora N, Sun CL, et al. Association of pre-chemotherapy peripheral blood pro-inflammatory and coagulation factors with reduced relative dose intensity in women with breast cancer. Breast Cancer Res. 2017;19(1):101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamanouchi K, Maeda S. The Efficacy of Inflammatory and Immune Markers for Predicting the Prognosis of Patients with Stage IV Breast Cancer. Acta Med Okayama. 2023;77(1):37\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu H, Zheng X, Ai J, Yang L. Hemoglobin, albumin, lymphocyte, and platelet (HALP) score and cancer prognosis: A systematic review and meta-analysis of 13,110 patients. Int Immunopharmacol. 2023;114:109496.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLou C, Jin F, Zhao Q, Qi H. Correlation of serum NLR, PLR and HALP with efficacy of neoadjuvant chemotherapy and prognosis of triple-negative breast cancer. Am J Transl Res. 2022;14(5):3240\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuran A, Pulat H, Cay F, Topal U. Importance of HALP Score in Breast Cancer and its Diagnostic Value in Predicting Axillary Lymph Node Status. J Coll Physicians Surg Pak. 2022;32(6):734\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcharya P, Jakobleff WA, Forest SJ, et al. Fibrinogen Albumin Ratio and Ischemic Stroke During Venoarterial Extracorporeal Membrane Oxygenation. ASAIO J. 2020;66(3):277\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng S, Fan Z, Xia H, et al. Fibrinogen/Albumin Ratio as a Promising Marker for Predicting Survival in Pancreatic Neuroendocrine Neoplasms. Cancer Manag Res. 2021;13:107\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Shan B, Zhou S, Yang H, Ye S. Fibrinogen/albumin ratio as a promising predictor of platinum response and survival in ovarian clear cell carcinoma. BMC Cancer. 2022;22(1):92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Feng Y, Xie Y, et al. High fibrinogen-albumin ratio index (FARI) predicts poor survival in head and neck squamous cell carcinoma patients treated with surgical resection. Eur Arch Otorhinolaryngol. 2022;279(9):4541\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Zhang J, Gao Q, et al. Preoperative Fibrinogen Albumin Ratio is an Effective Biomarker for Prognostic Evaluation of Gallbladder Carcinoma After Radical Resection: A 10-Year Retrospective Study at a Single Center. J Inflamm Res. 2023;16:677\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Ding Y, Wang W et al. Combining the Fibrinogen/Albumin Ratio and Systemic Inflammation Response Index Predicts Survival in Resectable Gastric Cancer. Gastroenterol Res Pract. 2020. 2020: 3207345.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang KT, Chung JK, Roh EY, et al. Prognostic Influence of Preoperative Fibrinogen to Albumin Ratio for Breast Cancer. J Breast Cancer. 2017;20(3):254\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Q, Liang D, Yu Y, Lv F. The Prognostic Significance of the Fibrinogen-to-Albumin Ratio in Patients With Triple-Negative Breast Cancer: A Retrospective Study. Front Surg. 2022;9:916298.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Y, Wu C, Yan H, Chen S. Prognostic value of combined preoperative fibrinogen-albumin ratio and platelet-lymphocyte ratio score in patients with breast cancer: A prognostic nomogram study. Clin Chim Acta. 2020;506:110\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer, Neoadjuvant Therapy, Pathological Response, FAR, HALP, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-4917041/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4917041/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eAchieving a pathological complete response (pCR) after neoadjuvant therapy (NAT) is considered to be a critical factor for a favourable prognosis in breast cancer. However, discordant pathological complete response (DpCR), characterised by isolated responses in the breast or axillary, represents an intermediate pathological response category between no response and complete response. This study aims to investigate predictive factors and develop models based on peripheral blood inflammatory indexes to more accurately predict NAT outcomes.\u003c/p\u003e\u003ch2\u003eMethod.\u003c/h2\u003e \u003cp\u003eA total of 789 eligible patients were enrolled in this retrospective study. The patients were randomized into training and validation cohort according to a 7:3 ratio. Lasso and uni/multivariate logistic regression analysis were applied to identify the predictor variables. Two Nomograms combining clinico-pathologic features and peripheral blood inflammatory indexes were developed.\u003c/p\u003e\u003ch2\u003eResult.\u003c/h2\u003e \u003cp\u003eMolecular Subtype, HALP, P53, and FAR were used to construct the predictive models for traditional non-pCR (T-NpCR) and total-pCR (TpCR). The T-NpCR group was divided into DpCR and non-pCR (NpCR) subgroups to construct a new model to more accurately predict NAT outcomes. cN, HALP, FAR, Molecular Subtype, and RMC were used to construct the predictive models for NpCR and DpCR. The receiver operating characteristic (ROC) curves indicate that the model exhibits robust predictive capacity. Clinical Impact Curves (CIC) and Decision Curve Analysis (DCA) indicate that the models present a superior clinical utility.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003eHALP and FAR were identified as peripheral blood inflammatory index predictors for accurately predicting NAT outcomes.\u003c/p\u003e","manuscriptTitle":"Development of Predictive Models for Pathological Response Status in Breast Cancer after Neoadjuvant Therapy Based on Peripheral Blood Inflammatory Indexes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 05:26:17","doi":"10.21203/rs.3.rs-4917041/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-23T06:03:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-21T22:25:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27584015807581985098686796380448550414","date":"2024-08-21T21:36:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-21T20:56:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326613927805036408650088399890542620522","date":"2024-08-21T15:32:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303871313186293960325423080246192032769","date":"2024-08-21T11:59:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42710559150535585682407243033533613232","date":"2024-08-21T04:39:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59996738379444922246319110396719609182","date":"2024-08-21T01:40:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312862367296831758772284067275336904148","date":"2024-08-21T01:03:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112385431081633031490848068148138718735","date":"2024-08-20T23:40:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134854001972907270595426839020495530267","date":"2024-08-20T21:04:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-20T20:28:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107451301938563535547493936911270615552","date":"2024-08-20T20:04:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-08-20T19:53:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-20T19:52:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-20T10:13:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-15T23:49:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-15T23:48:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2024-08-15T04:40:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0cbc22fa-15ff-4374-b3b8-2bbbcebcb4b6","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T16:08:59+00:00","versionOfRecord":{"articleIdentity":"rs-4917041","link":"https://doi.org/10.1186/s12905-024-03400-9","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2024-10-12 15:57:54","publishedOnDateReadable":"October 12th, 2024"},"versionCreatedAt":"2024-10-14 05:26:17","video":"","vorDoi":"10.1186/s12905-024-03400-9","vorDoiUrl":"https://doi.org/10.1186/s12905-024-03400-9","workflowStages":[]},"version":"v1","identity":"rs-4917041","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4917041","identity":"rs-4917041","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}