A nomogram based on the lymphocyte-to-monocyte ratio and changes in tumor blood supply and volume for predicting pathological complete response of triple negative breast cancer after neoadjuvant chemotherapy

A nomogram based on the lymphocyte-to-monocyte ratio and changes in tumor blood supply and volume for predicting pathological complete response of triple negative breast cancer after neoadjuvant chemotherapy | 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 A nomogram based on the lymphocyte-to-monocyte ratio and changes in tumor blood supply and volume for predicting pathological complete response of triple negative breast cancer after neoadjuvant chemotherapy Tingjian Zhang, Xueyun Zhao, Yuanping Li, Liang Huang, Qiang Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7883050/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Early and accurate prediction of the response to neoadjuvant chemotherapy (NACT) in triple negative breast cancer (TNBC) patients holds significant clinical value. This study investigated the role of the lymphocyte-to-monocyte ratio (LMR), changes in tumor blood supply and volume after two cycles of NACT, and their association with pathological complete response (pCR) in TNBC patients, aiming to establish and validate a nomogram for predicting pCR. Methods From January 2018 to May 2025, 379 TNBC patients were enrolled. The correlation between pCR and peripheral blood inflammatory markers, clinicopathological factors, and tumor ultrasound (US) features was analyzed using the chi-square test. Logistic regression analysis was performed to identify factors potentially influencing pCR. Based on the logistic regression analysis results, a nomogram was developed and validated to predict pCR. Results 42.74% (162/379) of TNBC patients achieved pCR after NACT. Logistic regression analysis identified Ki67 (OR: 4.228, 95% CI: 2.600–7.073, P < 0.0001), tumor volume reduction after two NACT cycles (OR: 3.052, 95% CI: 1.752–5.318, P < 0.0001), lymphocyte-to-monocyte ratio (LMR) (OR: 1.762, 95% CI: 1.076–2.884, P = 0.024), and decreased tumor blood supply after two NACT cycles (OR: 0.199, 95% CI: 0.122–0.324, P < 0.0001) as independent predictors of pCR after NACT. A nomogram prediction model was developed based on these positive indicators, demonstrating good predictive ability. Conclusion This predictive model will assist in early prediction of pCR after NACT in TNBC patients, helping clinicians optimize treatment regimens. Trial registration Not applicable. Triple negative breast cancer Pathological complete response Neoadjuvant chemotherapy Ultrasound Lymphocyte-to-monocyte ratio Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Breast cancer is the most common malignant tumor among women and poses a significant threat to their health and survival ( 1 ). Although survival outcomes for breast cancer patients have significantly improved in recent years owing to further understanding of the disease and advances in treatment methods, the prognosis for TNBC patients remains poor( 2 , 3 ). Triple-negative breast cancer accounts for approximately 15–20% of all breast cancer cases. The lack of expression of targetable proteins, such as the estrogen receptor, and the absence of HER2 amplification have made cytotoxic chemotherapy necessary for decades( 4 ). In recent years, the application of immune checkpoint inhibitors has significantly improved the prognosis of TNBC. The KEYNOTE-522 trial by Peter et al. has transformed the treatment landscape for TNBC. Adding pembrolizumab to NACT improved the pCR rate to 64.8% compared to 51.2% in the chemotherapy-only group, thereby improving patients’ DFS( 5 ). Despite this, triple-negative breast cancer remains the subtype with the highest recurrence and mortality rates among all breast cancer types and shows considerable heterogeneity in treatment response( 4 ). NACT is the standard treatment regimen for patients with locally advanced breast cancer. NACT downstages tumors, increases the likelihood of resectability and breast-conservation surgery, enables early eradication of micrometastatic disease, and permits in vivo assessment of chemosensitivity( 6 , 7 ). Accordingly, NACT is the preferred approach for triple-negative breast cancer (TNBC). Multiple studies show that the pCR after NACT predicts favorable long-term outcomes( 8 , 9 ). However, not all patients can benefit from the initial treatment regimen. TNBC is biologically and clinically heterogeneous; reported pCR rates after neoadjuvant therapy range from 18% to 60%( 10 , 11 ). Therefore, early prediction of pCR in TNBC is essential. Timely treatment adaptation—escalating therapy for non-responders to improve pCR rates and de-escalating for likely responders to reduce toxicity—may improve survival outcomes( 12 ). Previous studies have shown that the immune system is closely related to tumor initiation, progression, and metastasis( 13 , 14 ). Peripheral blood inflammatory markers—such as the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR)—are valuable predictors of treatment efficacy and prognosis( 15 – 17 ). For example, high NLR and PLR are associated with shorter overall survival (OS) and disease-free survival (DFS), whereas a low LMR predicts poorer DFS( 18 – 20 ). Furthermore, LMR serves as an independent predictor of pCR in breast cancer patients undergoing NACT, with multiple studies reporting higher pCR rates in patients with high LMR( 21 – 23 ). However, another study focusing on TNBC reported that patients with high LMR had lower pCR rates( 24 ). In contrast, some other studies have found no significant association between LMR and pCR rates( 25 ). Therefore, the value of LMR for predicting pCR in breast cancer after NACT remains controversial. Although the LMR is readily available in clinical practice and does not incur additional patient costs, reports on its application in NACT for TNBC remain scarce. Furthermore, LMR-based nomogram models for predicting pCR after NACT in TNBC patients are currently lacking. US imaging is another readily available source of clinical data. Owing to its low cost, noninvasiveness, and high accuracy, breast color doppler US has become the preferred method for assessing tumor changes in breast cancer patients. The Chinese Anti-Cancer Association Breast Cancer Guidelines strongly recommend performing breast color doppler US before NACT and after every two treatment cycles during therapy. However, few studies have reported using conventional color doppler US findings for the early prediction of pCR in TNBC( 26 ). This study aimed to develop an accurate, noninvasive model for early prediction of pCR after NACT in TNBC patients using readily available clinical data. We constructed a nomogram-based predictive model using pre-NACT peripheral blood inflammatory markers (LMR)and changes in tumor volume and blood supply after the initial two cycles of NACT. This model aimed to enable early prediction of pCR in TNBC patients, assisting clinicians in promptly adjusting treatment regimens and developing more personalized therapeutic strategies. Methods Population According to the inclusion and exclusion criteria, 379 patients with TNBC who received NACT at Leshan People’s Hospital were enrolled between January 2018 and March 2025. Inclusion criteria: ( 1 ) TNBC confirmed by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) before NACT; ( 2 ) Completion of the full planned NACT regimen; ( 3 ) Underwent surgery after NACT; ( 4 ) Had a single US-measurable lesion; ( 5 ) No acute or chronic inflammatory conditions, hematologic disorders, or autoimmune diseases before NACT; ( 6 ) Complete clinical-pathological and US data. Exclusion criteria:( 1 ) Receipt of any antitumor therapy before NACT;( 2 ) Inability to tolerate NACT or failure to complete the planned regimen;( 3 ) Bilateral breast lesions or multiple lesions in the ipsilateral breast. ༈4)No surgery performed after NACT;( 5 ) Initial diagnosis of advanced breast cancer or development of distant metastases during NACT; ( 6 ) Incomplete clinicopathologic or US data. The study complied with the Declaration of Helsinki and was approved and overseen by the Ethics Committee of Leshan People’s Hospital (No.LYLL-2024-KY150). This retrospective analysis did not involve identifiable personal information. Accordingly, the Ethics Committee granted a waiver of informed consent. No identifying information was collected at any stage of data acquisition or analysis. Data collection and processing We collected baseline, clinicopathologic, US, treatment-related, and pre-NACT laboratory data. Data were processed using Microsoft Excel and SPSS (version 26.0). Optimal cut-off values for continuous variables were determined from receiver operating characteristic (ROC) curves. Continuous variables were dichotomized according to these cut-offs. Histopathology review Before NACT, the primary tumor underwent US-guided core-needle biopsy, followed by immunohistochemical (IHC) staining and histopathological evaluation. Immunoreactivity for oestrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67 was quantified as the percentage of tumor cells with positive nuclear staining. ER and PR positivity was defined as ≥ 1% of invasive tumor cells showing nuclear staining. HER2 immunohistochemical scores were recorded as 0, 1+, 2+, or 3+. IHC 3 + was considered positive, whereas 0–and 1 + were considered negative. Equivocal (2+) cases underwent fluorescence in situ hybridization (FISH); an amplification ratio > 2.0 was considered positive, whereas a ratio < 2.0 was negative. TNBC was defined as ER and PR nuclear staining < 1% in invasive tumor cells and HER2-negative status. The Ki-67 index was defined as the proportion of tumor cells with nuclear staining, calculated by counting ≥ 1,000 cells across 10 high-power fields (×40). A high proliferative index was defined as Ki-67 ≥ 20%. Post-NACT surgical specimens were evaluated by breast-specialist pathologists. PCR was defined as no residual invasive carcinoma in the breast and no axillary lymph-node metastasis, with or without residual ductal carcinoma in situ (ypT0/Tis ypN0). US examination All patients underwent breast US before NACT. Follow-up US examinations were performed after every two NACT cycles. Three breast US specialists, each with more than 10 years of experience, independently reviewed images obtained at baseline (pre-NACT) and after two NACT cycles. Recorded US features included tumor size, shape, margins, calcifications, internal echo pattern, posterior acoustic features, Adler blood-flow grade, and changes in these features after two NACT cycles. Any disagreements were resolved by a fourth senior breast US specialist after re-assessment, and all four reviewers reached a final consensus. A standardized protocol was used for volume assessment: lesion outer margins were measured with calipers in three orthogonal planes, and the same sonographer preferentially performed follow-up measurements. All measurements were verified by a second radiologist. Tumor volume reduction (TVR) was calculated by comparing baseline with post-cycle two measurements. Three-dimensional measurements of the target lesion were recorded. Tumor volume was approximated as an ellipsoid: Volume = 0.523 × Length × Width × Height. TVR (%) = ((V1 - V2) / V1) ×100, where V1 is the baseline volume and V2 is the volume after two NACT cycles. Largest-diameter reduction rate: ∆Tx(%) = ((T1-T2)/T1) ×100, where T1 is the maximum tumor diameter at baseline and T2 is the maximum tumor diameter after two NACT cycles. Statistical Analysis Statistical analysis was conducted using SPSS 26.0 and RStudio software. The optimal cutoff was determined using the maximum Youden index, and continuous variables were converted into binary variables according to this threshold. Differences between the two groups were assessed using the chi-square test or Fisher’s exact test. Univariable and multivariable logistic regression were used to identify factors associated with pCR after NACT. Based on the multivariate logistic regression results, we constructed a nomogram to predict pCR after NACT. Discrimination was quantified by Harrell’s concordance index (C-index). We also calculated the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was assessed using internal validation with bootstrap resampling and visualized with a calibration curve. Decision-curve analysis (DCA) was performed to evaluate net clinical benefit across threshold probabilities. Two-sided P values < 0.05 were considered statistically significant. Results Clinicopathological and US characteristics of BC patients received NACT This study enrolled 379 patients (Fig. 1 ). The mean age of the patients was 48.2 ± 8.4 years, and the mean body mass index (BMI) was 24.3 ± 4.0. All patients completed 6 to 8 cycles of NACT according to the standard treatment protocol. Color Doppler ultrasonography was used to evaluate the primary lesions at baseline and after every two cycles of therapy. All patients underwent surgery after completion of NACT. Postoperative pathology confirmed that 42.74% (162/379) of patients achieved pCR. Tables 1 and 2 summarize the clinicopathological characteristics and ultrasonographic features of the tumors. The chi-square test indicated that Ki-67 expression, lymphocyte count (LYM), albumin level (ALB), lymphocyte-to-monocyte ratio (LMR), reduction in the largest tumor diameter, tumor volume reduction (TVR), and decreased tumor blood supply were significantly associated with pCR in TNBC patients after NACT. The optimal cutoff values were 4.85 for LMR and 67% for TVR. Logistic regression analysis for detecting the factors related to pCR after NACT Univariate logistic regression analysis was conducted to identify factors potentially influencing pCR in TNBC patients after NACT ( Table 3 ) . The results indicated that pCR was significantly associated with Ki67 expression, LYM, ALB, LMR, reduction in the largest tumor diameter, TVR, and decreased tumor blood supply. Further multivariate analysis demonstrated that Ki67 (OR: 4.228, 95% CI: 2.600–7.073, P < 0.0001), TVR (OR: 3.052, 95% CI: 1.752–5.318, P < 0.0001), LMR (OR: 1.762, 95% CI: 1.076–2.884, P = 0.024), and decreased tumor blood supply (OR: 0.199, 95% CI: 0.122–0.324, P < 0.0001) were independent predictors of pCR in TNBC patients after NACT. Among them, Ki67, TVR, and LMR showed positive correlations with pCR, while decreased tumor blood supply was associated with higher pCR. Establishment and evaluation of the nomogram model A nomogram was developed based on multivariate logistic regression analysis results to predict pCR after NACT in TNBC patients ( Fig. 5 ) . The receiver operating characteristic (ROC) curve was generated based on this model, yielding an AUC of 0.817 (95% CI: 0.764–0.870) ( Fig. 2 ) . The C-index of the predictive model was 0.817, indicating high discriminative ability. Bootstrap calibration curves demonstrated that the predicted results were consistent with the actual observed outcomes (average absolute error: 0.016) ( Fig. 3 ) . Additionally, the DCA showed that the nomogram could accurately predict pCR in NACT patients ( Fig. 4 ). Table 2 Baseline data of BC patients receiving NACT Factors Non-pCR (N = 217) pCR (N = 162) Total X2 P Age (Years) < 48.5 117 81 198 0.570 0.450 ≥ 48.5 100 81 181 BMI(kg/m2) < 21.23 51 32 83 0.762 0.383 ≥ 21.23 166 130 296 Menstrual states Pre-menopause 81 63 144 0.096 0.757 Menopause 136 99 235 Number of births < 3 184 132 316 0.734 0.392 ≥ 3 33 30 63 cT 1/2 199 153 352 1.052 0.305 3/4 18 9 27 cN 0/1 175 121 296 1.376 0.241 2/3 42 39 81 Lymph node metastasis Yes 172 119 291 1.754 0.185 No 45 43 88 Histological grade I/II 175 124 299 0.937 0.333 III 42 38 80 Ki67 < 20% 133 47 180 38.753 < 0.0001 ≥ 20% 84 115 199 *HER2 0 84 54 138 1.158 0.282 Low 132 108 241 CEA (ng/mL) < 1.52 147 97 244 2.502 0.114 ≥ 1.52 70 65 135 CA153 (U/mL) < 19.7 193 142 335 0.149 0.699 ≥ 19.7 24 20 44 WBC (×10^9/L) < 7.29 178 121 299 2.998 0.083 ≥ 7.29 39 41 80 NEU (×10^9/L) < 4.99 177 122 299 2.181 0.140 ≥ 4.99 40 40 80 LYM (×10^9/L) < 1.79 154 98 252 4.567 0.033 ≥ 1.79 63 64 127 Monocyte (×10^9/L) < 0.321 108 69 177 1.919 0.166 ≥ 0.321 109 93 202 PLT (×10^9/L) < 225.5 59 35 94 1.551 0.213 ≥ 225.5 158 127 285 ALB(g/L) < 41.25 45 21 66 3.898 0.048 ≥ 41.25 174 139 313 ALP < 73.5 110 69 179 2.441 0.118 ≥ 73.5 107 93 200 NLR < 1.51 33 20 53 0.631 0.427 ≥ 1.51 184 142 326 PLR < 156.50 88 60 148 0.482 0.488 ≥ 156.50 129 102 231 LMR < 4.85 103 56 159 6.336 0.012 ≥ 4.85 114 106 220 AAPR < 0.54 84 57 141 0.374 0.541 ≥ 0.54 133 103 236 The largest diameter decrease rate < 19% 94 54 148 3.885 0.049 ≥ 19% 123 108 231 TVR < 67% 182 100 282 23.881 < 0.0001 ≥ 67% 35 62 97 * HER-2: Human epidermal growth factor receptor-2、HER2-low tumors were defined by HER2 IHC score of 1 + or 2 + with negative FISH, and HER2-0 by IHC score of 0;BC: Breast cancer; NACT: Neoadjuvant chemotherapy; pCR: Pathological complete response; CEA: Carcinoembryonic antigen; CA153: Carbohydrate antigen 153: WBC: White blood cell; NEU: Neutrophil; LYM: Lymphocyte; PLT: Platelet; ALB: Albumin; ALP: Alkaline phosphatase; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; AAPR: albumin-to-alkaline phosphatase ratio ;TVR: tumor volume reduction; Significant values are in bold. Table 3 Comparative analysis of differences in US between the pCR group and Non-pCR group. Factors Before NACT After 2 cycles of NACT Non-pCR pCR P Non-pCR pCR P Margin Circumscribed 64 44 0.619 13 10 0.941 Non-circumscribed 153 118 204 152 Shape Regular 8 5 0.751 28 22 0.847 Irregular 209 157 189 140 Posterior acoustic Shadowing 22 21 0.521 72 57 0.550 No change 174 122 128 88 Enhancement 21 19 17 17 Internal echogenicity Very low 19 17 0.799 12 7 Low 184 135 181 141 0.648 Mix 6 6 12 9 Wait for an echo 8 4 12 5 *Adler grade Level 0 7 2 0.091 19 20 0.134 Level I 54 26 86 70 Level II 93 78 68 53 Level 3 63 56 44 19 Calcification None 101 74 0.867 91 64 0.634 Have 116 88 126 98 *Adler grade: Adler grading observes the distribution and richness of blood flow, finds the section with the most abundant blood flow, calculates the number of blood vessels, and defines the blood flow characteristics according to the semiquantitative grading of Adler. The definition of Alder grading change is whether grading decreases after NACT. Level 0: no blood flow in the lesion; Level I:a small amount of blood flow, with 1 or 2 punctured or thin rod blood flow; Level II: moderate blood flow, one major blood vessel can be seen, its length is close to or beyond the radius of the lesion or 3∼4 punctured or fine rod-shaped blood vessels; Level III: abundant blood flow, visible more than 4 blood vessels or interconnected, intertwined into a network. Table 4 Univariate and Multivariate Logistic analysis for pCR of BC cancer after NACT Factors Univariate analysis OR (95% CI) P Multivariate analysis OR (95%CI) P Age (Years) ≥ 46.5vs < 46.5 1.198(0.785, 1.829) 0.403 BMI(kg/m2) ≥ 21.23 vs < 21.23 1.248(0.758, 2.054) 0.383 Menstrual states Premenopause vs Menopause 0.936(0.616,1.423) 0.757 Number of births ≥ 3vs < 3 1.267(0.737, 2.180) 0.392 cT 1/2vs3/4 0.650(0.284,1.488) 0.308 cN 0/1vs2/3 Lymph node metastasis Yes vs No 0.724(0.449,1.169) 0.186 Histological grade I/II vs III 1.277(0.778,2.096) 0.334 Ki67 ≥ 20%vs < 20% 3.874(2.506,5.989) < 0.0001 4.228(2.600,7.073) < 0.0001 HER2 * 0 vs low 1.263(0.825,1.934) 0.282 CEA (ng/mL) ≥ 1.52vs < 1.52 1.407(0.921,2.150) 0.114 CA153 (U/mL) ≥ 19.7 vs < 19.7 1.133(0.602,2.130) 0.699 WBC (×10^9/L) ≥ 7.29 vs < 7.29 1.547(0.942,2.538) 0.085 NEU (×10^9/L) ≥ 4.99 vs < 4.99 1.451(0.884,2.381) 0.141 LYM (×10^9/L) ≥ 1.79 vs < 1.79 1.596(1.038,2.455) 0.033 Monocyte (×10^9/L) ≥ 0.321 vs < 0.321 1.335(0.887,2.012) 0.166 PLT (×10^9/L) ≥ 225.5 vs < 225.5 1.355(0.839,2.188) 0.214 ALB(g/L) ≥ 41.25vs < 41.25 1.757(1.000,3.087) 0.05 ALP ≥ 73.5vs < 73.5 1.386(0.920,2.087) 0.119 NLR ≥ 1.51 vs < 1.51 1.273(0.701,2.313) 0.428 PLR ≥ 156.50 vs < 156.50 2.352(0.912,6.065) 0.077 LMR ≥ 4.85 vs < 4.85 1.710(1.124,2.601) 0.012 1.762(1.076,2.884) 0.024 AAPR ≥ 0.54 vs < 0.54 1.141(0.747,1.743) 0.541 The largest diameter decrease rate ≥ 19% vs < 19% 1.528(1.001,2.333) 0.049 TVR ≥ 67% vs < 67% 3.224(1.993,5.215) < 0.0001 3.052(1.752.5.318) < 0.0001 Margin Change vs No change 0.833(0.508, 1.365) 0.468 Shape Chang to regular vs No change 1.079(0.541,2.155) 0.828 Posterior acoustic Change to shadowing vs No change 0.948(0.588,1.529) 0.827 Internal echogenicity Change to high vs No change 0.957(0.536,1.707) 0.881 Adler grade Decreased blood flow # vs No change 0.177(0.114,0.277) < 0.0001 0.199(0.122,0.324) < 0.0001 Calcification Increase vs No change 0.861(0.464,1.598) 0.636 *HER-2: Human epidermal growth factor receptor-2、HER2-low tumors were defined by HER2 IHC score of 1 + or 2 + with negative FISH, and HER2-0 by IHC score of 0;BC: Breast cancer; NACT: Neoadjuvant chemotherapy; pCR: Pathological complete response; CEA: Carcinoembryonic antigen; CA153: Carbohydrate antigen 153: WBC: White blood cell; NEU: Neutrophil; LYM: Lymphocyte; PLT: Platelet; ALB: Albumin; ALP: Alkaline phosphatase; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; AAPR: albumin-to-alkaline phosphatase ratio; TVR: tumor volume reduction; # The definition of decreased blood flow is Adler’s grading decreases after two therapy cycles of NACT. Significant values are in bold. Discussion TNBC remains the subtype with the poorest prognosis among all breast cancer subtypes. During NACT, accurate and early prediction of treatment response enables clinicians to adjust therapeutic regimens promptly. This approach can significantly improve pCR rates and overall survival ( 12 ). Therefore, we investigated the roles of the LMR, tumor blood supply and volume changes, and other potential factors associated with achieving pCR after NACT in TNBC patients. Based on logistic regression analysis, we developed and internally validated a nomogram prediction model incorporating LMR, Ki67, tumor blood supply and volume changes. Our results demonstrated that LMR is an independent predictor of pCR after NACT in TNBC patients. Previous studies have shown that LMR serves as a predictor of therapeutic efficacy and prognosis in breast cancer, with low LMR significantly associated with poorer DFS( 18 ). Furthermore, LMR was also identified as an independent predictor of pCR after NACT in breast cancer patients. For instance, patients with high LMR values were more likely to achieve pCR after NACT( 21 – 23 ). However, another study found no significant association between LMR and pCR( 25 ). In contrast, Zhang et al. reported that higher LMR values were associated with a lower likelihood of achieving pCR after NACT in TNBC patients( 24 ). In our analysis, logistic regression confirmed that LMR was an independent predictor of pCR. Moreover, TNBC patients with high LMR values (≥ 4.85) were significantly more likely to achieve pCR, consistent with most previous findings( 21 – 23 ). Compared with Zhang et al., our study included a substantially larger cohort (n = 379 vs. n = 80). Additionally, we carefully controlled for confounding factors that could affect peripheral blood inflammatory markers at the initial stage of our research. For example, patients with acute or chronic inflammation, hematologic disorders, or autoimmune diseases prior to NACT were excluded. Together with the findings from previous studies, our results are more compelling. Only a few studies have examined the predictive value of LMR for pCR following NACT in TNBC. Therefore, our findings not only reinforce the association between high LMR (≥ 4.85) and high pCR rates but also further clarify the predictive role of LMR in TNBC patients undergoing NACT. Analysis of US features showed that, after the initial two cycles of NACT, the tumor’s largest diameter reduction rate ≥ 19%, TVR ≥ 67%, and decreased tumor blood supply were each significantly associated with higher pCR rates.In multivariable logistic regression, TVR and decreased tumor blood supply were independent predictors of pCR in patients with TNBC treated with NACT. Compared to Magnetic Resonance Imaging (MRI), using US features to assess and predict pCR is less complex, more cost-effective, and more accessible, holding significant potential for both clinical and economic benefits( 27 ). US enables convenient, dynamic monitoring during NACT, and changes in US parameters can more accurately predict pCR( 28 ).Assessing the response to NACT via conventional imaging modalities, such as traditional US and MRI, primarily relies on changes in tumor size. We observed that the tumor’s largest diameter decrease rate ≥ 19% after the initial two cycles of NACT was associated with a higher pCR rate. However, this association did not persist in multivariable analyses. Therefore, a change in the largest tumor diameter alone did not independently predict pCR in TNBC. By contrast, tumor-volume change calculated from multidimensional measurements before NACT and after two cycles independently predicted pCR.This approach aligns with routine clinical practice. Relative to a single-diameter change, dynamic tumor-volume change more directly and accurately reflects response to chemotherapy( 27 ). External evidence supports this: studies by Adrada et al. and Tong et al. show that a reduction in tumor volume after two NACT cycles effectively predicts pCR in TNBC( 26 , 29 ). Moreover, colour doppler US readily characterises intratumoral blood flow. In the study by Chen et al., tumor blood supply was assessed before and after completion of 4–8 NACT cycles. Decreased blood supply independently predicted pCR and was significantly associated with higher pCR rates, consistent with our findings( 28 ). Our study differs in evaluating blood-supply change after only two cycles, enabling earlier pCR prediction. This earlier prediction may facilitate timely treatment modification and improve outcomes. Stevens et al. also attempted early prediction using contrast-enhanced MRI to assess blood-flow change after the first NACT cycle. However, despite the higher cost of MRI, they found no association between tumor blood flow change and pCR( 30 ). In summary, breast colour doppler US quantifies tumor volume change and evaluates intratumoral perfusion during NACT. It enables early prediction of pCR without additional financial burden. This study is the first to integrate peripheral blood inflammatory markers and tumor US characteristics (including TVR and decreased tumor blood supply) to predict treatment response early in a relatively large cohort of TNBC patients. Furthermore, our study identified Ki67 as an independent predictor of pCR after NACT. These results align with previous studies, which found that patients with higher Ki67 expression (≥ 20%) were more likely to achieve pCR [31, 32]. Using the accessible indicators outlined above, we developed a nomogram to predict pCR in TNBC patients undergoing NACT. To our knowledge, this is the first nomogram predictive model developed on a large cohort to early predict pCR in TNBC patients undergoing NACT, based on LMR, tumor volume and blood supply changes. The nomogram prediction model, developed through logistic regression analysis, is a simple and effective tool for prognostic prediction, demonstrating strong predictive capability for tumor pathological response following NACT( 31 – 33 ). Our multivariate regression analysis revealed that LMR, Ki67, TVR, and decreased tumor blood supply were independent predictors of pCR.Based on these positive indicators, we developed a nomogram predictive model for pCR in TNBC patients receiving NACT. The model’s C-index was 0.817 (95% CI: 0.764–0.870), and the AUC value on the ROC curve was also 0.817, indicating that the nomogram demonstrates strong accuracy in predicting pCR. We further validated the nomogram internally using calibration curves and decision curve analysis (DCA), confirming the model’s strong predictive capability. However, our study has several limitations. First, we excluded factors influencing peripheral blood inflammatory indicators (e.g., acute or chronic inflammation, hematological disorders, or autoimmune diseases before NACT) and factors affecting US measurements (e.g., bilateral breast cancer, multifocal cancer, and metastatic breast cancer) to enhance model accuracy. However, this limitation restricts the broader applicability of the model to the wider TNBC population. Second, although our dataset was relatively large, the study was limited to a single research center. Third, obtaining more comprehensive clinical data proved challenging due to the retrospective nature of the analysis. As a result, the study focused only on statistical analyses of common clinical pathological factors, laboratory indicators, and US parameters. Furthermore, TNBC is a heterogeneous disease, encompassing multiple tumor subtypes (e.g., luminal androgen receptor, mesenchymal, basal-like, and others). Prognosis varies among the different TNBC subtypes. Therefore, larger prospective multicenter studies are needed to analyze additional indicators and external validation cohorts to refine predictive models. Conclusions In summary, we established a nomogram prediction model based on easily accessible peripheral blood inflammatory indicators and US parameters in clinical practice. This model enables early prediction of pCR after NACT in TNBC patients, assisting clinicians in optimizing chemotherapy regimens to enhance patient outcomes. Declarations Acknowledgements We would like to express our heartfelt thanks to the ultrasound specialists, pathologists, and statisticians for their invaluable support. We thank all the investigators who participated in this study Author contributions TZ and YL3 contributed to the conception and design.TZ, YL3, LH, QZ, YL2 and LD contributed to data collection and analysis. TZ and YL3 wrote the manuscript. XZ and YL1 provided the administrative support and revised the manuscript. XZ provided the funding support. All authors have reviewed and approved the submission of this article. Funding This work was supported by the Wujieping Medical Foundation , under Grant number [320.6750.2022-19-24]. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Leshan People’s Hospital. 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1","display":"","copyAsset":false,"role":"figure","size":254789,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for selecting the study population.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7883050/v1/5ce255e85c1081f43161bbe4.png"},{"id":97255174,"identity":"ac8acc6f-ac06-4d47-bad3-26d411ef2b46","added_by":"auto","created_at":"2025-12-02 13:26:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97761,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of the nomogram for predicting pCR in TNBC patients treated with NACT.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7883050/v1/07f39ddb975bb9e688ea3f24.png"},{"id":97255288,"identity":"cb51fe11-fbcc-4a76-9981-096b2413cfb9","added_by":"auto","created_at":"2025-12-02 13:27:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75362,"visible":true,"origin":"","legend":"\u003cp\u003eBootstrap calibration curve of the nomogram for predicting pCR in TNBC received NACT.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7883050/v1/71d5faf94d2b5c4a039dc082.png"},{"id":97255257,"identity":"33dfad27-c402-4f87-900d-d738e5c675f3","added_by":"auto","created_at":"2025-12-02 13:27:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":124482,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the nomogram for predicting pCR in TNBC after NACT.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7883050/v1/b0e66d64a139b2b0616f0097.png"},{"id":97255336,"identity":"ffbafca6-29a7-4f21-8299-6f329377094e","added_by":"auto","created_at":"2025-12-02 13:27:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":119953,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram for predicting pCR in TNBC after NACT\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7883050/v1/6fe5e47b26a3e48b29ff43a0.png"},{"id":97664497,"identity":"0bb972a6-af1e-4046-86b4-fbe132912436","added_by":"auto","created_at":"2025-12-08 09:06:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1925785,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7883050/v1/2b4f21fa-e02d-408e-a445-0e924f70f0d0.pdf"},{"id":97255225,"identity":"97d3eb43-39cf-4e6e-96cd-26f8ec481479","added_by":"auto","created_at":"2025-12-02 13:27:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14940509,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialstestgroup.docx","url":"https://assets-eu.researchsquare.com/files/rs-7883050/v1/badf1e4fe5e77169cd5d410f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A nomogram based on the lymphocyte-to-monocyte ratio and changes in tumor blood supply and volume for predicting pathological complete response of triple negative breast cancer after neoadjuvant chemotherapy","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer is the most common malignant tumor among women and poses a significant threat to their health and survival (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although survival outcomes for breast cancer patients have significantly improved in recent years owing to further understanding of the disease and advances in treatment methods, the prognosis for TNBC patients remains poor(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Triple-negative breast cancer accounts for approximately 15\u0026ndash;20% of all breast cancer cases. The lack of expression of targetable proteins, such as the estrogen receptor, and the absence of HER2 amplification have made cytotoxic chemotherapy necessary for decades(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In recent years, the application of immune checkpoint inhibitors has significantly improved the prognosis of TNBC. The KEYNOTE-522 trial by Peter et al. has transformed the treatment landscape for TNBC. Adding pembrolizumab to NACT improved the pCR rate to 64.8% compared to 51.2% in the chemotherapy-only group, thereby improving patients\u0026rsquo; DFS(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Despite this, triple-negative breast cancer remains the subtype with the highest recurrence and mortality rates among all breast cancer types and shows considerable heterogeneity in treatment response(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNACT is the standard treatment regimen for patients with locally advanced breast cancer. NACT downstages tumors, increases the likelihood of resectability and breast-conservation surgery, enables early eradication of micrometastatic disease, and permits in vivo assessment of chemosensitivity(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Accordingly, NACT is the preferred approach for triple-negative breast cancer (TNBC). Multiple studies show that the pCR after NACT predicts favorable long-term outcomes(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, not all patients can benefit from the initial treatment regimen. TNBC is biologically and clinically heterogeneous; reported pCR rates after neoadjuvant therapy range from 18% to 60%(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Therefore, early prediction of pCR in TNBC is essential. Timely treatment adaptation\u0026mdash;escalating therapy for non-responders to improve pCR rates and de-escalating for likely responders to reduce toxicity\u0026mdash;may improve survival outcomes(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious studies have shown that the immune system is closely related to tumor initiation, progression, and metastasis(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Peripheral blood inflammatory markers\u0026mdash;such as the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR)\u0026mdash;are valuable predictors of treatment efficacy and prognosis(\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). For example, high NLR and PLR are associated with shorter overall survival (OS) and disease-free survival (DFS), whereas a low LMR predicts poorer DFS(\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Furthermore, LMR serves as an independent predictor of pCR in breast cancer patients undergoing NACT, with multiple studies reporting higher pCR rates in patients with high LMR(\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, another study focusing on TNBC reported that patients with high LMR had lower pCR rates(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In contrast, some other studies have found no significant association between LMR and pCR rates(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Therefore, the value of LMR for predicting pCR in breast cancer after NACT remains controversial. Although the LMR is readily available in clinical practice and does not incur additional patient costs, reports on its application in NACT for TNBC remain scarce. Furthermore, LMR-based nomogram models for predicting pCR after NACT in TNBC patients are currently lacking. US imaging is another readily available source of clinical data. Owing to its low cost, noninvasiveness, and high accuracy, breast color doppler US has become the preferred method for assessing tumor changes in breast cancer patients. The Chinese Anti-Cancer Association Breast Cancer Guidelines strongly recommend performing breast color doppler US before NACT and after every two treatment cycles during therapy. However, few studies have reported using conventional color doppler US findings for the early prediction of pCR in TNBC(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study aimed to develop an accurate, noninvasive model for early prediction of pCR after NACT in TNBC patients using readily available clinical data. We constructed a nomogram-based predictive model using pre-NACT peripheral blood inflammatory markers (LMR)and changes in tumor volume and blood supply after the initial two cycles of NACT. This model aimed to enable early prediction of pCR in TNBC patients, assisting clinicians in promptly adjusting treatment regimens and developing more personalized therapeutic strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePopulation\u003c/h2\u003e\u003cp\u003eAccording to the inclusion and exclusion criteria, 379 patients with TNBC who received NACT at Leshan People\u0026rsquo;s Hospital were enrolled between January 2018 and March 2025. Inclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) TNBC confirmed by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) before NACT; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Completion of the full planned NACT regimen; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Underwent surgery after NACT; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Had a single US-measurable lesion; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) No acute or chronic inflammatory conditions, hematologic disorders, or autoimmune diseases before NACT; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Complete clinical-pathological and US data. Exclusion criteria:(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Receipt of any antitumor therapy before NACT;(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Inability to tolerate NACT or failure to complete the planned regimen;(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Bilateral breast lesions or multiple lesions in the ipsilateral breast. ༈4)No surgery performed after NACT;(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Initial diagnosis of advanced breast cancer or development of distant metastases during NACT; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Incomplete clinicopathologic or US data. The study complied with the Declaration of Helsinki and was approved and overseen by the Ethics Committee of Leshan People\u0026rsquo;s Hospital (No.LYLL-2024-KY150). This retrospective analysis did not involve identifiable personal information. Accordingly, the Ethics Committee granted a waiver of informed consent. No identifying information was collected at any stage of data acquisition or analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection and processing\u003c/h3\u003e\n\u003cp\u003eWe collected baseline, clinicopathologic, US, treatment-related, and pre-NACT laboratory data. Data were processed using Microsoft Excel and SPSS (version 26.0). Optimal cut-off values for continuous variables were determined from receiver operating characteristic (ROC) curves. Continuous variables were dichotomized according to these cut-offs.\u003c/p\u003e\n\u003ch3\u003eHistopathology review\u003c/h3\u003e\n\u003cp\u003eBefore NACT, the primary tumor underwent US-guided core-needle biopsy, followed by immunohistochemical (IHC) staining and histopathological evaluation. Immunoreactivity for oestrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67 was quantified as the percentage of tumor cells with positive nuclear staining. ER and PR positivity was defined as \u0026ge;\u0026thinsp;1% of invasive tumor cells showing nuclear staining. HER2 immunohistochemical scores were recorded as 0, 1+, 2+, or 3+. IHC 3\u0026thinsp;+\u0026thinsp;was considered positive, whereas 0\u0026ndash;and 1\u0026thinsp;+\u0026thinsp;were considered negative. Equivocal (2+) cases underwent fluorescence in situ hybridization (FISH); an amplification ratio\u0026thinsp;\u0026gt;\u0026thinsp;2.0 was considered positive, whereas a ratio\u0026thinsp;\u0026lt;\u0026thinsp;2.0 was negative. TNBC was defined as ER and PR nuclear staining\u0026thinsp;\u0026lt;\u0026thinsp;1% in invasive tumor cells and HER2-negative status. The Ki-67 index was defined as the proportion of tumor cells with nuclear staining, calculated by counting\u0026thinsp;\u0026ge;\u0026thinsp;1,000 cells across 10 high-power fields (\u0026times;40). A high proliferative index was defined as Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;20%. Post-NACT surgical specimens were evaluated by breast-specialist pathologists. PCR was defined as no residual invasive carcinoma in the breast and no axillary lymph-node metastasis, with or without residual ductal carcinoma in situ (ypT0/Tis ypN0).\u003c/p\u003e\n\u003ch3\u003eUS examination\u003c/h3\u003e\n\u003cp\u003eAll patients underwent breast US before NACT. Follow-up US examinations were performed after every two NACT cycles. Three breast US specialists, each with more than 10 years of experience, independently reviewed images obtained at baseline (pre-NACT) and after two NACT cycles. Recorded US features included tumor size, shape, margins, calcifications, internal echo pattern, posterior acoustic features, Adler blood-flow grade, and changes in these features after two NACT cycles. Any disagreements were resolved by a fourth senior breast US specialist after re-assessment, and all four reviewers reached a final consensus.\u003c/p\u003e\u003cp\u003eA standardized protocol was used for volume assessment: lesion outer margins were measured with calipers in three orthogonal planes, and the same sonographer preferentially performed follow-up measurements. All measurements were verified by a second radiologist. Tumor volume reduction (TVR) was calculated by comparing baseline with post-cycle two measurements. Three-dimensional measurements of the target lesion were recorded. Tumor volume was approximated as an ellipsoid: Volume\u0026thinsp;=\u0026thinsp;0.523 \u0026times; Length \u0026times; Width \u0026times; Height. TVR (%) = ((V1 - V2) / V1) \u0026times;100, where V1 is the baseline volume and V2 is the volume after two NACT cycles.\u003c/p\u003e\u003cp\u003eLargest-diameter reduction rate: ∆Tx(%) = ((T1-T2)/T1) \u0026times;100, where T1 is the maximum tumor diameter at baseline and T2 is the maximum tumor diameter after two NACT cycles.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was conducted using SPSS 26.0 and RStudio software. The optimal cutoff was determined using the maximum Youden index, and continuous variables were converted into binary variables according to this threshold. Differences between the two groups were assessed using the chi-square test or Fisher\u0026rsquo;s exact test. Univariable and multivariable logistic regression were used to identify factors associated with pCR after NACT. Based on the multivariate logistic regression results, we constructed a nomogram to predict pCR after NACT. Discrimination was quantified by Harrell\u0026rsquo;s concordance index (C-index). We also calculated the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was assessed using internal validation with bootstrap resampling and visualized with a calibration curve. Decision-curve analysis (DCA) was performed to evaluate net clinical benefit across threshold probabilities. Two-sided \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eClinicopathological and US characteristics of BC patients received NACT\u003c/h2\u003e\u003cp\u003eThis study enrolled 379 patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of the patients was 48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4 years, and the mean body mass index (BMI) was 24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0. All patients completed 6 to 8 cycles of NACT according to the standard treatment protocol. Color Doppler ultrasonography was used to evaluate the primary lesions at baseline and after every two cycles of therapy. All patients underwent surgery after completion of NACT. Postoperative pathology confirmed that 42.74% (162/379) of patients achieved pCR. Tables\u0026nbsp;1 and \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarize the clinicopathological characteristics and ultrasonographic features of the tumors. The chi-square test indicated that Ki-67 expression, lymphocyte count (LYM), albumin level (ALB), lymphocyte-to-monocyte ratio (LMR), reduction in the largest tumor diameter, tumor volume reduction (TVR), and decreased tumor blood supply were significantly associated with pCR in TNBC patients after NACT. The optimal cutoff values were 4.85 for LMR and 67% for TVR.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLogistic regression analysis for detecting the factors related to pCR after NACT\u003c/h3\u003e\n\u003cp\u003eUnivariate logistic regression analysis was conducted to identify factors potentially influencing pCR in TNBC patients after NACT \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The results indicated that pCR was significantly associated with Ki67 expression, LYM, ALB, LMR, reduction in the largest tumor diameter, TVR, and decreased tumor blood supply. Further multivariate analysis demonstrated that Ki67 (OR: 4.228, 95% CI: 2.600\u0026ndash;7.073, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), TVR (OR: 3.052, 95% CI: 1.752\u0026ndash;5.318, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), LMR (OR: 1.762, 95% CI: 1.076\u0026ndash;2.884, P\u0026thinsp;=\u0026thinsp;0.024), and decreased tumor blood supply (OR: 0.199, 95% CI: 0.122\u0026ndash;0.324, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were independent predictors of pCR in TNBC patients after NACT. Among them, Ki67, TVR, and LMR showed positive correlations with pCR, while decreased tumor blood supply was associated with higher pCR.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eEstablishment and evaluation of the nomogram model\u003c/h2\u003e\u003cp\u003eA nomogram was developed based on multivariate logistic regression analysis results to predict pCR after NACT in TNBC patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The receiver operating characteristic (ROC) curve was generated based on this model, yielding an AUC of 0.817 (95% CI: 0.764\u0026ndash;0.870) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The C-index of the predictive model was 0.817, indicating high discriminative ability. Bootstrap calibration curves demonstrated that the predicted results were consistent with the actual observed outcomes (average absolute error: 0.016) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Additionally, the DCA showed that the nomogram could accurately predict pCR in NACT patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline data of BC patients receiving NACT\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-pCR (N\u0026thinsp;=\u0026thinsp;217)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epCR\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;162)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eX2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (Years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;48.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.450\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\u0026ge;\u0026thinsp;48.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;21.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.383\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\u0026ge;\u0026thinsp;21.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenstrual states\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.757\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\u003eMenopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of births\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.392\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\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003e1/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.305\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\u003e3/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003e0/1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.241\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\u003e2/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph node metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.185\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=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistological grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI/II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.333\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\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u0026lt;\u0026thinsp;20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e38.753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\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\u0026ge;\u0026thinsp;20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e*HER2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.282\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\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.114\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\u0026ge;\u0026thinsp;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA153 (U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.699\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\u0026ge;\u0026thinsp;19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (\u0026times;10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;7.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.083\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\u0026ge;\u0026thinsp;7.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEU (\u0026times;10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.140\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\u0026ge;\u0026thinsp;4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLYM (\u0026times;10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\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\u0026ge;\u0026thinsp;1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte (\u0026times;10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.166\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\u0026ge;\u0026thinsp;0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT (\u0026times;10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;225.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.213\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\u0026ge;\u0026thinsp;225.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;41.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\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\u0026ge;\u0026thinsp;41.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;73.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.118\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\u0026ge;\u0026thinsp;73.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u0026lt;\u0026thinsp;1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.427\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\u0026ge;\u0026thinsp;1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u0026lt;\u0026thinsp;156.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.488\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\u0026ge;\u0026thinsp;156.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u0026lt;\u0026thinsp;4.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\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\u0026ge;\u0026thinsp;4.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAAPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.541\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\u0026ge;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe largest diameter decrease rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\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\u0026ge;\u0026thinsp;19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTVR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\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\u0026ge;\u0026thinsp;67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e* HER-2: Human epidermal growth factor receptor-2、HER2-low tumors were defined by HER2 IHC score of 1\u0026thinsp;+\u0026thinsp;or 2\u0026thinsp;+\u0026thinsp;with negative FISH, and HER2-0 by IHC score of 0;BC: Breast cancer; NACT: Neoadjuvant chemotherapy; pCR: Pathological complete response; CEA: Carcinoembryonic antigen; CA153: Carbohydrate antigen 153: WBC: White blood cell; NEU: Neutrophil; LYM: Lymphocyte; PLT: Platelet; ALB: Albumin; ALP: Alkaline phosphatase; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; AAPR: albumin-to-alkaline phosphatase ratio ;TVR: tumor volume reduction; Significant values are in bold.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative analysis of differences in US between the pCR group and Non-pCR group.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eBefore NACT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eAfter 2 cycles of NACT\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-pCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-pCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003epCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMargin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCircumscribed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.941\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\u003eNon-circumscribed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShape\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.847\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\u003eIrregular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePosterior acoustic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShadowing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.550\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 change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\u003eEnhancement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal echogenicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.648\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\u003eMix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\u003eWait for an echo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e*Adler grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.134\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\u003eLevel I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\u003eLevel II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\u003eLevel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.634\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\u003eHave\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*Adler grade: Adler grading observes the distribution and richness of blood flow, finds the section with the most abundant blood flow, calculates the number of blood vessels, and defines the blood flow characteristics according to the semiquantitative grading of Adler. The definition of Alder grading change is whether grading decreases after NACT. Level 0: no blood flow in the lesion; Level I:a small amount of blood flow, with 1 or 2 punctured or thin rod blood flow; Level II: moderate blood flow, one major blood vessel can be seen, its length is close to or beyond the radius of the lesion or 3\u0026sim;4 punctured or fine rod-shaped blood vessels; Level III: abundant blood flow, visible more than 4 blood vessels or interconnected, intertwined into a network.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and Multivariate Logistic analysis for pCR of BC cancer after NACT\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnivariate analysis OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMultivariate analysis OR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (Years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;46.5vs\u0026thinsp;\u0026lt;\u0026thinsp;46.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.198(0.785, 1.829)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;21.23 vs\u0026thinsp;\u0026lt;\u0026thinsp;21.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.248(0.758, 2.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenstrual states\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePremenopause vs Menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.936(0.616,1.423)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of births\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3vs\u0026thinsp;\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.267(0.737, 2.180)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003e1/2vs3/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.650(0.284,1.488)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003e0/1vs2/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph node metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes vs No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.724(0.449,1.169)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistological grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI/II vs III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.277(0.778,2.096)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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;20%vs\u0026thinsp;\u0026lt;\u0026thinsp;20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.874(2.506,5.989)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.228(2.600,7.073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHER2\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 vs low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.263(0.825,1.934)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.52vs\u0026thinsp;\u0026lt;\u0026thinsp;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.407(0.921,2.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA153 (U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;19.7 vs\u0026thinsp;\u0026lt;\u0026thinsp;19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.133(0.602,2.130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (\u0026times;10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;7.29 vs\u0026thinsp;\u0026lt;\u0026thinsp;7.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.547(0.942,2.538)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEU (\u0026times;10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4.99 vs\u0026thinsp;\u0026lt;\u0026thinsp;4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.451(0.884,2.381)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLYM (\u0026times;10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.79 vs\u0026thinsp;\u0026lt;\u0026thinsp;1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.596(1.038,2.455)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte (\u0026times;10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.321 vs\u0026thinsp;\u0026lt;\u0026thinsp;0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.335(0.887,2.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT (\u0026times;10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;225.5 vs\u0026thinsp;\u0026lt;\u0026thinsp;225.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.355(0.839,2.188)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;41.25vs\u0026thinsp;\u0026lt;\u0026thinsp;41.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.757(1.000,3.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;73.5vs\u0026thinsp;\u0026lt;\u0026thinsp;73.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.386(0.920,2.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u0026ge;\u0026thinsp;1.51 vs\u0026thinsp;\u0026lt;\u0026thinsp;1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.273(0.701,2.313)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u0026ge;\u0026thinsp;156.50 vs\u0026thinsp;\u0026lt;\u0026thinsp;156.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.352(0.912,6.065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u0026ge;\u0026thinsp;4.85 vs\u0026thinsp;\u0026lt;\u0026thinsp;4.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.710(1.124,2.601)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.762(1.076,2.884)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAAPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.54 vs\u0026thinsp;\u0026lt;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.141(0.747,1.743)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe largest diameter decrease rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;19% vs\u0026thinsp;\u0026lt;\u0026thinsp;19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.528(1.001,2.333)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTVR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;67% vs\u0026thinsp;\u0026lt;\u0026thinsp;67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.224(1.993,5.215)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.052(1.752.5.318)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMargin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChange vs\u003c/p\u003e\u003cp\u003eNo change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.833(0.508, 1.365)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShape\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChang to regular vs No change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.079(0.541,2.155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePosterior acoustic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChange to shadowing vs No change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.948(0.588,1.529)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal echogenicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChange to high vs No change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.957(0.536,1.707)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdler grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDecreased blood flow\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e vs\u003c/p\u003e\u003cp\u003eNo change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.177(0.114,0.277)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.199(0.122,0.324)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncrease vs No change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.861(0.464,1.598)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*HER-2: Human epidermal growth factor receptor-2、HER2-low tumors were defined by HER2 IHC score of 1\u0026thinsp;+\u0026thinsp;or 2\u0026thinsp;+\u0026thinsp;with negative FISH, and HER2-0 by IHC score of 0;BC: Breast cancer; NACT: Neoadjuvant chemotherapy; pCR: Pathological complete response; CEA: Carcinoembryonic antigen; CA153: Carbohydrate antigen 153: WBC: White blood cell; NEU: Neutrophil; LYM: Lymphocyte; PLT: Platelet; ALB: Albumin; ALP: Alkaline phosphatase; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; AAPR: albumin-to-alkaline phosphatase ratio; TVR: tumor volume reduction; \u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003eThe definition of decreased blood flow is Adler\u0026rsquo;s grading decreases after two therapy cycles of NACT. Significant values are in bold.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTNBC remains the subtype with the poorest prognosis among all breast cancer subtypes. During NACT, accurate and early prediction of treatment response enables clinicians to adjust therapeutic regimens promptly. This approach can significantly improve pCR rates and overall survival (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, we investigated the roles of the LMR, tumor blood supply and volume changes, and other potential factors associated with achieving pCR after NACT in TNBC patients. Based on logistic regression analysis, we developed and internally validated a nomogram prediction model incorporating LMR, Ki67, tumor blood supply and volume changes.\u003c/p\u003e\u003cp\u003eOur results demonstrated that LMR is an independent predictor of pCR after NACT in TNBC patients. Previous studies have shown that LMR serves as a predictor of therapeutic efficacy and prognosis in breast cancer, with low LMR significantly associated with poorer DFS(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Furthermore, LMR was also identified as an independent predictor of pCR after NACT in breast cancer patients.\u003c/p\u003e\u003cp\u003eFor instance, patients with high LMR values were more likely to achieve pCR after NACT(\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, another study found no significant association between LMR and pCR(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In contrast, Zhang et al. reported that higher LMR values were associated with a lower likelihood of achieving pCR after NACT in TNBC patients(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In our analysis, logistic regression confirmed that LMR was an independent predictor of pCR. Moreover, TNBC patients with high LMR values (\u0026ge;\u0026thinsp;4.85) were significantly more likely to achieve pCR, consistent with most previous findings(\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Compared with Zhang et al., our study included a substantially larger cohort (n\u0026thinsp;=\u0026thinsp;379 vs. n\u0026thinsp;=\u0026thinsp;80). Additionally, we carefully controlled for confounding factors that could affect peripheral blood inflammatory markers at the initial stage of our research. For example, patients with acute or chronic inflammation, hematologic disorders, or autoimmune diseases prior to NACT were excluded. Together with the findings from previous studies, our results are more compelling. Only a few studies have examined the predictive value of LMR for pCR following NACT in TNBC. Therefore, our findings not only reinforce the association between high LMR (\u0026ge;\u0026thinsp;4.85) and high pCR rates but also further clarify the predictive role of LMR in TNBC patients undergoing NACT.\u003c/p\u003e\u003cp\u003eAnalysis of US features showed that, after the initial two cycles of NACT, the tumor\u0026rsquo;s largest diameter reduction rate\u0026thinsp;\u0026ge;\u0026thinsp;19%, TVR\u0026thinsp;\u0026ge;\u0026thinsp;67%, and decreased tumor blood supply were each significantly associated with higher pCR rates.In multivariable logistic regression, TVR and decreased tumor blood supply were independent predictors of pCR in patients with TNBC treated with NACT. Compared to Magnetic Resonance Imaging (MRI), using US features to assess and predict pCR is less complex, more cost-effective, and more accessible, holding significant potential for both clinical and economic benefits(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). US enables convenient, dynamic monitoring during NACT, and changes in US parameters can more accurately predict pCR(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).Assessing the response to NACT via conventional imaging modalities, such as traditional US and MRI, primarily relies on changes in tumor size. We observed that the tumor\u0026rsquo;s largest diameter decrease rate\u0026thinsp;\u0026ge;\u0026thinsp;19% after the initial two cycles of NACT was associated with a higher pCR rate. However, this association did not persist in multivariable analyses. Therefore, a change in the largest tumor diameter alone did not independently predict pCR in TNBC. By contrast, tumor-volume change calculated from multidimensional measurements before NACT and after two cycles independently predicted pCR.This approach aligns with routine clinical practice. Relative to a single-diameter change, dynamic tumor-volume change more directly and accurately reflects response to chemotherapy(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). External evidence supports this: studies by Adrada et al. and Tong et al. show that a reduction in tumor volume after two NACT cycles effectively predicts pCR in TNBC(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Moreover, colour doppler US readily characterises intratumoral blood flow. In the study by Chen et al., tumor blood supply was assessed before and after completion of 4\u0026ndash;8 NACT cycles. Decreased blood supply independently predicted pCR and was significantly associated with higher pCR rates, consistent with our findings(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Our study differs in evaluating blood-supply change after only two cycles, enabling earlier pCR prediction. This earlier prediction may facilitate timely treatment modification and improve outcomes. Stevens et al. also attempted early prediction using contrast-enhanced MRI to assess blood-flow change after the first NACT cycle. However, despite the higher cost of MRI, they found no association between tumor blood flow change and pCR(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In summary, breast colour doppler US quantifies tumor volume change and evaluates intratumoral perfusion during NACT. It enables early prediction of pCR without additional financial burden.\u003c/p\u003e\u003cp\u003eThis study is the first to integrate peripheral blood inflammatory markers and tumor US characteristics (including TVR and decreased tumor blood supply) to predict treatment response early in a relatively large cohort of TNBC patients. Furthermore, our study identified Ki67 as an independent predictor of pCR after NACT. These results align with previous studies, which found that patients with higher Ki67 expression (\u0026ge;\u0026thinsp;20%) were more likely to achieve pCR [31, 32]. Using the accessible indicators outlined above, we developed a nomogram to predict pCR in TNBC patients undergoing NACT. To our knowledge, this is the first nomogram predictive model developed on a large cohort to early predict pCR in TNBC patients undergoing NACT, based on LMR, tumor volume and blood supply changes.\u003c/p\u003e\u003cp\u003eThe nomogram prediction model, developed through logistic regression analysis, is a simple and effective tool for prognostic prediction, demonstrating strong predictive capability for tumor pathological response following NACT(\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Our multivariate regression analysis revealed that LMR, Ki67, TVR, and decreased tumor blood supply were independent predictors of pCR.Based on these positive indicators, we developed a nomogram predictive model for pCR in TNBC patients receiving NACT. The model\u0026rsquo;s C-index was 0.817 (95% CI: 0.764\u0026ndash;0.870), and the AUC value on the ROC curve was also 0.817, indicating that the nomogram demonstrates strong accuracy in predicting pCR. We further validated the nomogram internally using calibration curves and decision curve analysis (DCA), confirming the model\u0026rsquo;s strong predictive capability.\u003c/p\u003e\u003cp\u003eHowever, our study has several limitations. First, we excluded factors influencing peripheral blood inflammatory indicators (e.g., acute or chronic inflammation, hematological disorders, or autoimmune diseases before NACT) and factors affecting US measurements (e.g., bilateral breast cancer, multifocal cancer, and metastatic breast cancer) to enhance model accuracy. However, this limitation restricts the broader applicability of the model to the wider TNBC population. Second, although our dataset was relatively large, the study was limited to a single research center. Third, obtaining more comprehensive clinical data proved challenging due to the retrospective nature of the analysis. As a result, the study focused only on statistical analyses of common clinical pathological factors, laboratory indicators, and US parameters. Furthermore, TNBC is a heterogeneous disease, encompassing multiple tumor subtypes (e.g., luminal androgen receptor, mesenchymal, basal-like, and others). Prognosis varies among the different TNBC subtypes. Therefore, larger prospective multicenter studies are needed to analyze additional indicators and external validation cohorts to refine predictive models.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, we established a nomogram prediction model based on easily accessible peripheral blood inflammatory indicators and US parameters in clinical practice. This model enables early prediction of pCR after NACT in TNBC patients, assisting clinicians in optimizing chemotherapy regimens to enhance patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our heartfelt thanks to the ultrasound specialists, pathologists, and statisticians for their invaluable support. We thank all the investigators who participated in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTZ and YL3 contributed to the conception and design.TZ, YL3, LH, QZ, YL2 and LD contributed to data collection and analysis. TZ and YL3 wrote the manuscript. XZ and YL1 provided the administrative support and revised the manuscript. XZ provided the funding support. All authors have reviewed and approved the submission of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the \u003cstrong\u003eWujieping Medical Foundation\u003c/strong\u003e, under Grant number [320.6750.2022-19-24].\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 analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and\u003c/p\u003e\n\u003cp\u003ewas approved by the Institutional Review Board of Leshan People\u0026rsquo;s Hospital. For retrospective data analysis, the requirement for informed consent was waived by the ethics committee of Leshan People\u0026rsquo;s Hospital due to the anonymized nature of the data.\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 "},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Zhang H, Merkher Y, Chen L, Liu N, Leonov S, et al. Recent advances in therapeutic strategies for triple-negative breast cancer. J Hematol Oncol. 2022;15(1):121.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWagle NS, Nogueira L, Devasia TP, Mariotto AB, Yabroff KR, Islami F, et al. Cancer treatment and survivorship statistics, 2025. CA Cancer J Clin. 2025;75(4):308\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeon-Ferre RA, Goetz MP. Advances in systemic therapies for triple negative breast cancer. BMJ. 2023;381:e071674.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchmid P, Cortes J, Pusztai L, McArthur H, K\u0026uuml;mmel S, Bergh J, et al. Pembrolizumab for Early Triple-Negative Breast Cancer. N Engl J Med. 2020;382(9):810\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGradishar WJ, Anderson BO, Abraham J, Aft R, Agnese D, Allison KH, et al. Breast Cancer, Version 3.2020, NCCN Clinical Practice Guidelines in Oncology. 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Front Oncol. 2022;12:894476.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Triple negative breast cancer, Pathological complete response, Neoadjuvant chemotherapy, Ultrasound, Lymphocyte-to-monocyte ratio, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7883050/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7883050/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly and accurate prediction of the response to neoadjuvant chemotherapy (NACT) in triple negative breast cancer (TNBC) patients holds significant clinical value. This study investigated the role of the lymphocyte-to-monocyte ratio (LMR), changes in tumor blood supply and volume after two cycles of NACT, and their association with pathological complete response (pCR) in TNBC patients, aiming to establish and validate a nomogram for predicting pCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom January 2018 to May 2025, 379 TNBC patients were enrolled. The correlation between pCR and peripheral blood inflammatory markers, clinicopathological factors, and tumor ultrasound (US) features was analyzed using the chi-square test. Logistic regression analysis was performed to identify factors potentially influencing pCR. Based on the logistic regression analysis results, a nomogram was developed and validated to predict pCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e42.74% (162/379) of TNBC patients achieved pCR after NACT. Logistic regression analysis identified Ki67 (OR: 4.228, 95% CI: 2.600–7.073, P \u0026lt; 0.0001), tumor volume reduction after two NACT cycles (OR: 3.052, 95% CI: 1.752–5.318, P \u0026lt; 0.0001), lymphocyte-to-monocyte ratio (LMR) (OR: 1.762, 95% CI: 1.076–2.884, P = 0.024), and decreased tumor blood supply after two NACT cycles (OR: 0.199, 95% CI: 0.122–0.324, P \u0026lt; 0.0001) as independent predictors of pCR after NACT. A nomogram prediction model was developed based on these positive indicators, demonstrating good predictive ability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis predictive model will assist in early prediction of pCR after NACT in TNBC patients, helping clinicians optimize treatment regimens.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"A nomogram based on the lymphocyte-to-monocyte ratio and changes in tumor blood supply and volume for predicting pathological complete response of triple negative breast cancer after neoadjuvant chemotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 13:24:24","doi":"10.21203/rs.3.rs-7883050/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"201928001052824150861016100292078261375","date":"2025-11-14T21:20:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-14T13:38:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-20T04:33:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-19T22:57:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-19T22:56:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-10-17T05:42:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4433c914-5924-411f-90f2-2d90fecd5e65","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-02T13:24:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 13:24:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7883050","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7883050","identity":"rs-7883050","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}