Metastatic Lymph Node Ratio and Log Odds of Positive Nodes Versus Pathologic Nodal Stage in Breast Cancer With Residual Axillary Disease After Neoadjuvant Chemotherapy: A Retrospective Cohort Study

Metastatic Lymph Node Ratio and Log Odds of Positive Nodes Versus Pathologic Nodal Stage in Breast Cancer With Residual Axillary Disease After Neoadjuvant Chemotherapy: A Retrospective Cohort Study | 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 Metastatic Lymph Node Ratio and Log Odds of Positive Nodes Versus Pathologic Nodal Stage in Breast Cancer With Residual Axillary Disease After Neoadjuvant Chemotherapy: A Retrospective Cohort Study Eda Güner, Serdar Sarıdemir, Cihangir Özaslan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7557989/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Feb, 2026 Read the published version in World Journal of Surgical Oncology → Version 1 posted 10 You are reading this latest preprint version Abstract Background and Objectives: Residual axillary disease after neoadjuvant chemotherapy (NACT) challenges prognostication with conventional pathologic nodal (pN) staging. We compared the prognostic value of the metastatic lymph node ratio (mLNR) and the log odds of positive nodes (LODDS) versus pN in ypN+ breast cancer. Methods: We retrospectively analyzed 276 stage IIB–III patients (2010–2022) with residual nodal metastasis after NACT and surgery. Survival was evaluated with Kaplan–Meier and Cox models. Discrimination was summarized by AUC (ROC) with 95% CIs; pairwise DeLong tests compared mLNR versus LODDS. Primary analyses treated mLNR/LODDS as continuous variables; cutoff-based sensitivity analyses used 23.7% and −1.24, respectively. Results: Median dissected nodes were 17 (IQR 13–22); median positive nodes were 3. High LODDS (>−1.24 vs ≤−1.24) independently associated with shorter DFS (HR 1.69, 95% CI 1.10–2.63; p=0.017). mLNR and LODDS showed similar, moderate discrimination for OS/DFS (AUC ≈0.60–0.62), with no significant differences by DeLong testing. Classical adverse features (residual breast tumor, triple-negative subtype, grade III) remained independently prognostic. Conclusions: In ypN+ disease after NACT, mLNR and LODDS perform at least comparably to pN, offering similar, moderate prognostic discrimination without clear superiority of one metric. Routine reporting of both may aid risk communication; subgroup signals are hypothesis-generating and warrant external validation. Breast Neoplasms Neoadjuvant Therapy Lymphatic Metastasis Lymph Nodes Prognosis Survival Analysis. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Breast cancer is the most commonly diagnosed malignancy among women worldwide, accounting for 11.7% of all new cancer cases according to the 2022 GLOBOCAN data [1]. It represents a substantial mortality burden and remains one of the leading causes of cancer-related death in women [1]. Axillary lymph node involvement has long been regarded as one of the most important prognostic indicators for survival in breast cancer [2]. Following neoadjuvant chemotherapy (NACT), axillary lymph nodes may undergo fibrosis and shrinkage, and systemic therapy may eradicate certain metastatic foci, potentially resulting in a decreased number of dissected lymph nodes [3]. This phenomenon limits the reliability of the classic pathological nodal (pN) staging, often leading to understaging, especially in cases where fewer than 10 lymph nodes are removed [3]. To overcome this issue, the concept of metastatic lymph node ratio (mLNR) was developed. mLNR is defined as the ratio of the number of metastatic (tumor-involved) lymph nodes to the total number of dissected lymph nodes. Several studies have reported that mLNR is a stronger prognostic indicator compared to pN staging, particularly in NACT populations where the number of dissected lymph nodes is often limited [4–6]. However, mLNR also has certain limitations. In particular, in cases where the number of dissected and positive lymph nodes is very low, the ratio may not fully reflect the true prognostic difference [7]. Therefore, in recent years, the concept of Log Odds of Positive Lymph Nodes (LODDS), which is based on the logarithmic ratio of positive to negative lymph nodes and has been validated in broader patient populations, has been introduced [8,9]. LODDS is calculated as the natural logarithm of the ratio [(number of positive lymph nodes + 0.5) /(number of negative lymph nodes + 0.5)], allowing for the assessment of the prognostic impact of lymph node metastasis independently of the total number of nodes dissected and the effect of extreme values. Several studies have reported that LODDS demonstrates superior or at least equivalent prognostic power for survival prediction compared to mLNR and conventional pN staging [8–10]. The aim of this study is to comparatively evaluate the relationship of mLNR and LODDS with clinicopathological factors and their impact on survival in breast cancer patients with persistent axillary metastasis following NACT, in relation to conventional lymph node count-based staging. Materials and Methods Study Design and Patient Selection This single-center, retrospective cohort study included patients with locally advanced (Stage IIB–III) breast cancer who underwent surgery following neoadjuvant chemotherapy (NACT) at our institution between 2010 and 2022. Patient records were retrospectively reviewed using the electronic database. Inclusion Criteria • No evidence of distant metastasis at diagnosis • Presence of clinical or radiological evidence of axillary lymph node metastasis before NACT (cN1–cN3) [11]. • Completion of standard level I–II axillary dissection in conjunction with either breast-conserving surgery or mastectomy after NACT • Detection of micrometastasis or macrometastasis in axillary lymph nodes on pathological examination Exclusion Criteria • Achievement of axillary pathological complete response (ypN0) after NACT • Patients who did not complete neoadjuvant and adjuvant therapy • Missing follow-up data A total of 276 patients who met these criteria were included in the study. The flow of patient selection and exclusion criteria applied to form the final study cohort is illustrated in Fig. 1 . Of the 774 patients who underwent surgery after NACT for locally advanced breast cancer between 2010 and 2022, those unable to complete NACT, those achieving axillary pathological complete response (ypN0), and patients with missing follow-up data were sequentially excluded, resulting in a final analytical cohort of 276 patients (see Fig. 1 ). Treatment and Follow-Up Protocol Treatment and Follow-Up Protocol All patients were treated with standard NACT regimens containing anthracyclines and taxanes; trastuzumab/pertuzumab was added for HER2-positive patients [12]. Surgically, 244 patients underwent mastectomy, and 32 patients underwent breast-conserving surgery. Level I–II axillary dissection was performed in all cases, while Level III dissection was carried out in suspicious cases. In the adjuvant period, appropriate patients received hormone therapy, anti-HER2 targeted therapy, and radiotherapy. Clinical follow-up visits were scheduled every three months for the first two years, every six months between the third and fifth years, and annually thereafter. Disease-free survival (DFS) was defined as the time from surgery to the date of first recurrence or metastasis, while overall survival (OS) was defined as the time from surgery to death [13]. Statistical Analysis We followed STROBE and REMARK guidance for design, conduct, and reporting. Overall survival (OS) and disease-free survival (DFS) were analyzed with Cox proportional hazards models; proportional hazards assumptions were assessed using Schoenfeld residuals. Median follow-up was estimated using the reverse Kaplan–Meier method. Discrimination was summarized by the area under the ROC curve (AUC) with 95% confidence intervals; AUCs for LODDS vs mLNR were compared using DeLong’s nonparametric test. To avoid multicollinearity, highly correlated nodal metrics (pN, number of positive nodes, mLNR, LODDS) were evaluated in separate models; variance inflation factors and correlation matrices were inspected. To mitigate optimism from single-cutoff thresholds, primary analyses treated LODDS and mLNR as continuous variables; where cutoffs were used, internal validation employed bootstrap resampling (1,000 iterations). In cross-sectional analyses, LODDS was dichotomized at the − 1.24 cutoff. In Cox models, the reference category was low LODDS ( ≤ − 1.24), and high LODDS ( > − 1.24) was reported relative to the reference (HR, 95% CI). All hypothesis tests were two-sided; exact P values are reported, with p < 0.05 considered statistically significant. Analyses were primarily performed in IBM SPSS Statistics (version 25); proportional hazards diagnostics and bootstrap procedures were performed in R (version 4.5.0; packages survival and rms). Results Patient Characteristics The median age of the 276 patients included in the study was 51 years (range: 25–82), with the majority of patients clustered between 42 and 61 years according to quartile analysis (Q1 = 42, Q3 = 61, IQR = 19). The median age of 51 indicates that the study cohort was predominantly middle-aged. Menopausal status was distributed as 51% premenopausal and 49% postmenopausal. A family history of breast cancer in first-degree relatives was present in 11% of the patients. Tumor lateralization was 52% in the right breast and 48% in the left breast. Regarding surgical treatment, mastectomy was performed in 88.5% of the cases, while breast-conserving surgery was performed in 11.5%. All patients underwent Level I–II axillary dissection (98%) or Level III axillary dissection (2%) when indicated. The median primary tumor size was 4 cm (range: 1–11 cm); 54% of patients had tumors ≤ 4 cm, and 46% had tumors > 4 cm. Histopathologically, invasive ductal carcinoma was identified in 85% of patients, invasive lobular carcinoma in 6%, and other types in 9%. The rate of pathological complete response (pCR) in the breast following NACT was 12% (n = 34) (11). Pathological T and N staging were performed according to the 8th edition AJCC criteria: T0 12% (n = 34), T1 26% (n = 72), T2 39% (n = 109), and T3 23% (n = 63); N1 40% (n = 110), N2 44% (n = 121), and N3 16% (n = 44). Accordingly, 36% (n = 99) of patients were classified as stage IIb, and 64% (n = 177) as stage III. Estrogen receptor (ER) positivity was detected in 76% (n = 210), progesterone receptor (PR) positivity in 66% (n = 182), and HER2 positivity in 32% (n = 88) of patients. The distribution of molecular subtypes was as follows: 76% (n = 210) luminal, 10% (n = 28) HER2-positive, and 14% (n = 38) triple-negative. All patient and tumor characteristics are summarized in Table 1 . Table 1 Clinicopathological characteristics of the patients (n = 276) IDC: invasive ductal carcinoma, ILC; Invasive lobular carcinoma, BCS: Breast-conserving surgery, ypN: Pathological axillary lymph node status after neoadjuvant therapy, ER: Estrogen receptor, PR: Progesterone receptor, HER2: Human epidermal growth factor receptor 2, LA: Luminal A breast cancer, LB: Luminal B breast cancer, HER2 +: Human epidermal growth factor receptor 2 positive breast cancer TN: Triple negative breast cancer LVI: Lymphovascular invasion mLNR: Metastatic lymph node ratio Variable Category n % Age (years) ≤ 50 130 47.0 > 50 146 53.0 Menopausal status Pre-menopausal 139 51.0 Post-menopausal 137 49.0 Family history Negative 246 89.0 Positive 30 11.0 Histological type Invasive ductal (IDC) 235 85.0 Invasive lobular (ILC) 15 6.0 Other 26 9.0 Type of surgery Mastectomy 244 88.5 Breast-conserving surgery (BCS) 32 11.5 Axillary dissection type Level I–II 269 97.0 Level I–III 7 3.0 Tumor size after neoadjuvant chemotherapy (cm) ≤ 5 149 54.0 > 5 127 46.0 Residual tumor in breast Negative 45 16.0 Positive 231 84.0 ypT stage T1 32 12.0 T2 166 60.0 T3 78 28.0 ypN stage N1 111 40.0 N2 120 43.0 N3 45 17.0 Pathological stage Stage IIB 99 36.0 Stage III 177 64.0 ER status Negative 66 24.0 Positive 210 76.0 PR status Negative 94 34.0 Positive 182 66.0 HER2 status Negative 188 68.0 Positive 88 32.0 Molecular subtype Luminal A 94 34.0 Luminal B 116 42.0 HER2-enriched 28 10.0 Triple-negative 39 14.0 Tumor grade Grade I 10 2.0 Grade II 124 28.0 Grade III 142 32.0 Lymphovascular invasion (LVI) Negative 198 72.0 Positive 77 28.0 mLNR (%, cutoff 23.7) ≤ 23,7 151 57 > 23.7 125 43 Lymph nodes removed Median (range) 17 (4–49) - Positive lymph nodes Median (range) 3 (1–33) - LODDS group − 1.24 130 47.1 Metastatic Lymph Node Ratio (mLNR) and Variables The metastatic lymph node ratio (mLNR) was calculated as the ratio of the number of metastatic lymph nodes to the total number of dissected lymph nodes. In our cohort, the median number of dissected lymph nodes was 17 (IQR: 13–22), with the majority of cases falling within this range. The median number of metastatic lymph nodes was 3 (range: 1–33). Calculated mLNR values ranged from 3–100%, with a median mLNR of 19%. Quartile analysis of the total number of dissected lymph nodes revealed a 25th percentile (Q1) of 13, a median (Q2) of 17, and a 75th percentile (Q3) of 22. For the number of positive lymph nodes, Q1, Q2, and Q3 values were 1, 3, and 7, respectively. The interquartile range (IQR) was 9 for the total number of lymph nodes and 6 for positive lymph nodes. These findings indicate that the distribution of positive lymph node counts was narrower and right-skewed, demonstrating that these two variables have different distributions in the dataset and provide distinct prognostic information. Based on ROC curve analysis, the optimal prognostic cut-off value for mLNR was determined to be 23.7%. Accordingly, patients with mLNR ≤ 0.237 were classified as the "low-ratio" group (n = 151), and those with mLNR > 0.237 as the "high-ratio" group (n = 125). The mean number of dissected lymph nodes in the low mLNR group was 19, with a mean of 2.3 metastatic nodes; in the high mLNR group, the mean number of dissected nodes was 15 and the mean number of metastatic nodes was 10. A significant association was observed between pathological stage and mLNR: 36% of the low mLNR group were stage IIB, whereas the high mLNR group consisted predominantly of stage III patients. Thus, a high mLNR was found to be associated with more advanced disease stage. In addition, clinicopathological variables such as patient age (≤ 50 vs. >50 years), menopausal status, pathological T and N stages, histological type, tumor grade (low/intermediate vs. high), ER/PR and HER2 status, molecular subtypes (Luminal A, Luminal B, HER2-enriched, triple-negative), and the presence of pathological complete response in the breast were also evaluated in the analyses. LODDS (Log Odds of Positive Lymph Nodes) and Variables Log odds of positive lymph nodes (LODDS) is a parameter developed to more precisely reflect the biological burden of lymph node metastasis. LODDS is calculated by applying a logarithmic transformation to the ratio of the number of positive lymph nodes (pLN) to the number of negative lymph nodes (nLN), using the following formula: $$\:LODDS\:=\text{log}\left(\frac{pLN\:+\:0.5}{nLN\:+\:0.5}\right)$$ In our cohort of 276 patients, LODDS values ranged from − 3.08 to 4.20, with a mean of − 0.83 ± 1.68, a median of − 1.34, and an interquartile range (IQR) of 1.95 (Q1: − 1.95; Q3: 0.00). Examination of the distribution revealed that LODDS values were right-skewed, with most patients clustering at lower (negative) LODDS values. Normality tests (Kolmogorov-Smirnov and Shapiro-Wilk) were both statistically significant (p < 0.001 for both), indicating that LODDS values were not normally distributed. Based on a cohort-specific ROC analysis, the optimal prognostic cut-off value for LODDS was determined to be − 1.24. Patients were categorized into a "low-risk" group (LODDS ≤ − 1.24; n = 146, 52.9%) and a "high-risk" group (LODDS> − 1.24; n = 130, 47.1%). In the low LODDS group, the mean number of dissected lymph nodes was 19.8 ± 6.5, and the mean number of positive lymph nodes was 2.0 ± 1.2. In the high LODDS group, these values were 16.1 ± 6.1 and 9.0 ± 5.7, respectively. Both variables differed significantly between the groups (p < 0.001). These findings suggest that low LODDS values are more frequently observed in early-stage cases or those with few positive lymph nodes, whereas high LODDS is more common in advanced-stage disease with multiple metastatic nodes. A significant association was observed between LODDS categories and pathological stage; patients in the low LODDS group were more likely to be stage IIB, whereas those in the high LODDS group were predominantly stage III (p 40 years) and LODDS categories (Pearson Chi-square = 1.799, p = 0.180). The prognostic importance of LODDS and its relationship with clinicopathological parameters were analyzed. Among patients who developed local recurrence (n = 25), the mean LODDS value was significantly higher (mean rank: 168.36 vs. 133.28; p = 0.034). Similarly, patients who developed distant metastasis had significantly higher LODDS values (mean rank for those with metastasis, n = 100: 159.62; for those without, n = 176: 123.48; p < 0.001). These findings indicate that higher LODDS values are associated with both local recurrence and distant metastasis risk. In conclusion, LODDS was shown to be a significant prognostic marker for both local recurrence and distant metastasis and was closely related to surgical-pathological parameters. In addition, other clinicopathological variables such as menopausal status, pathological T and N stage, histological type, tumor grade, ER/PR/HER2 status, molecular subtypes, and the presence of pathological complete response were also analyzed. According to the literature, LODDS is reported to provide a more sensitive prognostic indicator than metastatic lymph node ratio (mLNR), particularly in cases where the number of dissected lymph nodes is low or when all lymph nodes are either positive or negative [14,15]. Similarly, our analyses demonstrated that LODDS exhibited high prognostic discriminatory power. Survival Analyses In our study cohort of 276 patients, disease-free survival (DFS) was analyzed using the Kaplan-Meier method. During follow-up, 111 patients (40.1%) experienced a DFS event (recurrence, metastasis, or disease-related death), while 165 patients (59.9%) were censored without an event. Median DFS was 104 months (95% CI, 67.8–140.2). The three-year DFS rate was 81.7% (standard error: 0.024), and the five-year DFS rate was 62.0% (standard error: 0.033). These findings indicate an overall favorable disease-free survival prognosis in the study group. The high median DFS suggests relatively low rates of recurrence/metastasis in the early period and an acceptable level of treatment efficacy across the cohort. Our results are consistent with the literature reported in similar populations, supporting the reliability of long-term DFS rates as a prognostic indicator. The median follow-up time for the entire cohort was calculated as 127.7 months (SE: 20.2; 95% CI: 88.1–167.4). According to Kaplan-Meier analysis, the 3-year overall survival (OS) rate was 87.1%, and the 5-year OS rate was 71.9%. These findings indicate that the cohort has a favorable prognosis in terms of both medium- and long-term overall survival. Median OS and DFS durations, as well as estimated 3- and 5-year survival rates for the study cohort, are summarized in Table 2 Table 2 Median overall survival (OS) and disease-free survival (DFS) times, and estimated 3- and 5-year survival rates in the study cohort Survival Endpoint Median Survival (months) 95% CI (Median) 3-year Survival Ratio 5-year Survival Ratio Standard Error (3/5 years) Overall Survival (OS) 127.7 88.1–167.4 87.1% 71.9% 0.020 / 0.030 Disease-Free Survival (DFS) 104.0 67.8–140.2 81.7% 62.0% 0.024 / 0.033 When the cohort was divided into two groups according to mLNR cut-off value (≤ 0.237 and > 0.237), Kaplan-Meier survival analysis demonstrated a significant difference between the groups. The 5-year overall survival rate was 75.3% (SE: 0.042) in the mLNR ≤ 0.237 group and 67.2% (SE: 0.044) in the mLNR > 0.237 group. In addition, the median survival time in the high mLNR group was calculated as 93 months (95% CI: 50.9–135.1). This difference was statistically significant according to the log-rank test (log-rank χ² = 6.900; p = 0.009). These findings suggest that a high mLNR has a negative prognostic impact on survival. The differences in overall survival (OS) according to metastatic lymph node ratio (mLNR) cut-off values (< 23.7% and ≥ 23.7%) were analyzed by the Kaplan-Meier method, and the results are presented in Fig. 2 . According to Kaplan–Meier analysis, the 5-year disease-free survival (DFS) rate was 53.3% (SE: 0.049) in patients with mLNR ≤ 0.237. In contrast, the 5-year DFS rate was 43.9% (SE: 0.054) in the mLNR > 0.237 group. In the high mLNR group, the median DFS was calculated as 70 months (95% CI: 40.8–99.2), whereas the median DFS was not reached in the low mLNR group. In the cohort of 276 patients, the log-rank test according to the mLNR cut-off demonstrated a significant difference between the groups (log-rank p < 0.05). These findings indicate that a high mLNR is an adverse prognostic factor for disease-free survival. In logistic regression analysis, an increase in the total number of dissected lymph nodes was found to significantly decrease the likelihood of high mLNR (> 23.7%) (OR: 0.90; p 40 years) and high mLNR (OR: 0.87; p = 0.672). The overall classification accuracy of the model was found to be 67.6%. In our study, when comparing the two groups created using the LODDS cut-off value of − 1.24, significant differences were observed in both overall survival (OS) and disease-free survival (DFS). Patients with lower LODDS values had significantly longer OS and DFS durations, with reduced event rates. These differences were statistically significant according to the log-rank test (p = 0.015 for OS; p = 0.009 for DFS). These findings demonstrate that categorizing patients based on the LODDS cut-off value is an effective tool for prognosticating survival, and patients below the cut-off have a distinctly better prognosis. Kaplan–Meier survival analyses stratified by the LODDS cutoff value (–1.24) demonstrated that patients in the low LODDS group experienced significantly longer overall survival (OS) and disease-free survival (DFS) compared to those in the high LODDS group. Kaplan–Meier curves for OS and DFS stratified by LODDS are shown in Fig. 3 . Using ROC analysis, categorical LODDS (cutoff − 1.24) and mLNR (cutoff 23.7%) showed similar, moderate discrimination for both overall survival (OS) and disease-free survival (DFS) (Fig. 4 ). For OS, AUC was 0.618 (95% CI 0.547–0.689; p = 0.002) for LODDS and 0.620 (95% CI 0.549–0.692; p = 0.001) for mLNR; for DFS, AUC was 0.605 (95% CI 0.536–0.674; p = 0.003) for LODDS and 0.596 (95% CI 0.527–0.665; p = 0.007) for mLNR. The close AUCs and overlapping confidence intervals—corroborated by non-significant pairwise DeLong tests (p > 0.05)—indicate no meaningful difference in discrimination; therefore, both metrics can be used for survival prognostication, with no clear superiority of one over the other. Detailed ROC estimates (AUC, 95% CI, SE, p) are provided in the results table. In univariate analysis, the presence of residual tumor in the breast after NACT, pathological T3 stage, histological grade III, triple-negative molecular subtype, and high mLNR were each significantly associated with an increased risk of recurrence (p < 0.05 for all). Similarly, a high LODDS value was also associated with an elevated risk of recurrence. Other prognostic factors are presented in Table 3 . In contrast, the ypN stage (N1 vs N2 vs N3) based on the number of positive lymph nodes after surgery did not show a statistically significant difference in terms of disease-free survival (p = 0.29). In multivariate analysis, when both mLNR and LODDS were included in the model, neither variable retained independent prognostic significance or made a significant individual contribution. These findings suggest that mLNR and LODDS may be useful for predicting recurrence risk beyond conventional nodal staging, but neither demonstrates clear superiority over the other. The results of univariate analysis (log-rank test) for clinicopathological factors affecting disease-free survival are summarized in Table 3 . Table 3 Baseline characteristics by mLNR/LODDS (χ²/Fisher). BCS: Breast-conserving surgery, IDC: invasive ductal carcinoma, ILC: Invasive lobular carcinoma, ER: Estrogen receptor, PR: Progesterone receptor, HER2: Human epidermal growth factor receptor 2, LA: Luminal A breast cancer, LB: Luminal B breast cancer, HER2 +: Human epidermal growth factor receptor 2 positive breast cancer, TN: Triple negative breast cancer, LVI: Lymphovascular invasion, mLNR: Metastatic lymph node ratio Variable mLNR ≤ 23.7 n (%) mLNR > 23.7 n (%) p-value LODDS ≤ − 1.24 n (%) LODDS > − 1.24 n (%) p-value Age (years) ≤ 50 65 (48) 58 (42) 0.44 70 (49) 53 (41) 0.38 > 50 70 (52) 83 (58) 74 (51) 77 (59) Menopause Pre 68 (50) 59 (43) 0.49 72 (51) 55 (42) 0.37 Post 67 (50) 82 (57) 72 (49) 75 (58) Tumor size (cm) ≤ 5 72 (53) 52 (36) 0.027 76 (54) 48 (36) 0.019 > 5 63 (47) 89 (64) 68 (46) 82 (64) Surgery type Mastectomy 115 (85) 126 (91) 0.18 112 (81) 129 (93) 0.011 BCS 20 (15) 12 (9) 26 (19) 10 (7) Histopathologic type IDC 110 (81) 108 (78) 0.73 112 (81) 106 (76) 0.48 ILC 8 (6) 9 (7) 10 (7) 7 (5) Other 17 (13) 21 (15) 20 (12) 24 (19) Residual tumor (breast) Negative 21 (16) 12 (9) 0.09 22 (16) 11 (8) 0.072 Positive 114 (84) 126 (91) 115 (84) 128 (92) ypN N1 93 (69) 20 (14) < 0.001 98 (72) 15 (11) < 0.001 N2 37 (27) 58 (42) 34 (25) 61 (48) N3 5 (4) 61 (44) 4 (3) 67 (41) Stage < 0.001 < 0.001 Stage IIB 90 (67) 7 (5) 93 (66) 5 (4) Stage III 45 (34) 131 (95) 47 (32) 137 (96) Molecular subtype 0.14 0.17 Luminal A 38 (28) 34 (24) 36 (26) 36 (26) Luminal B 48 (36) 45 (33) 50 (37) 43 (32) HER2+ 22 (16) 16 (12) 24 (18) 14 (11) Triple Negative 27 (20) 46 (33) 28 (19) 45 (34) Grade 0.82 0.79 Grade I–II 73 (54) 66 (48) 75 (55) 64 (47) Grade III 62 (46) 72 (52) 61 (45) 73 (53) LVI 0.028 0.032 Negative 104 (77) 69 (50) 107 (79) 66 (48) Positive 31 (23) 71 (50) 29 (21) 71 (52) According to multivariable Cox regression, the presence of residual breast tumor [HR 3.08; 95% CI 1.40–7.10; p = 0.005], triple-negative molecular subtype [HR 2.79; 95% CI 1.50–5.80; p = 0.021], high histological grade (Grade III) [HR 2.34; 95% CI 1.30–3.90; p = 0.002], high mLNR (> 0.30) [HR 1.72; 95% CI 1.01–2.83; p = 0.038], and high LODDS (>–1.24 vs ≤–1.24) [HR 1.69; 95% CI 1.10–2.63; p = 0.017] were independently associated with worse disease-free survival. Disease-free survival (DFS),High LODDS (>–1.24 vs ≤–1.24) remained independently associated with shorter DFS [HR 1.69; 95% CI 1.10–2.63; p = 0.017]. Overall survival (OS) Triple-negative subtype [HR ≈ 1.3; p < 0.05] and Grade III histology [HR ≈ 2.6; p –1.24 vs low ≤–1.24) reached statistical significance (p > 0.05). The multivariable Cox results for DFS and OS are summarized in Table 4 . Table 4 Multivariate Cox regression identified independent predictors of disease-free survival (DFS): presence of residual breast tumor, molecular subtype, tumor grade, and metastatic lymph node ratio (mLNR). ypT stage was not significant (NS). In the overall survival (OS) analysis, neither mLNR nor LODDS showed a significant association with outcome (p > 0.05 for both). Variable Categories HR (DFS) 95% CI (DFS) p (DFS) HR (OS) 95% CI (OS) p (OS) Residual tumor in breast Neg. vs Pos. 3.10 1.40–7.10 0.005 — — — ypT stage T0/T1/T2/T3 — — NS — — — Molecular subtype LA/LB/HER2+/TN 2.80 1.30–5.80 0.021 — — — Tumor grade Grade I–II vs III 2.30 1.30–3.90 0.002 — — — Metastatic lymph node ratio (mLNR) ≤ 23.7% vs > 23.7% 1.60 1.10–2.34 0.015 0.53 Not given 0.378 LODDS ≤–1.24 vs >–1.24 0.60 0.41–0.89 0.010 1.08 Not given 0.917 HR, Hazard Ratio; CI, Confidence Interval; NS, Not significant; LA, Luminal A; LB, Luminal B; HER2⁺, HER2-positive; TN, Triple-negative; ypT, Pathological T stage after neoadjuvant treatment; mLNR, Metastatic lymph node ratio (number of positive nodes ÷ total nodes removed); LODDS, Log odds of positive lymph nodes. Reference = low LODDS (≤–1.24). All categorical covariates are reported with their reference category explicitly stated. Statistical analyses revealed that estrogen (ER) and progesterone (PR) receptor positivity, low histological grade, and a low Ki-67 proliferation index were all significantly associated with longer survival (p < 0.05 for each). The association between age (< 60 vs. ≥60 years) and survival was of borderline significance (p = 0.049). The presence of invasive tumor and disease recurrence had a clearly detrimental effect on survival. In multivariate Cox regression analysis, ER and PR positivity, histological grade, and the Ki-67 index were identified as independent prognostic factors. A 95% confidence interval was added to the Kaplan–Meier curves, the proportional hazards assumption was confirmed using the Schoenfeld residuals test, and the validity of the model was supported by time-dependent variable analyses. Furthermore, the use of a competing risk model to separately assess the probabilities of recurrence and death, and the calculation of AUC values with ROC curves, demonstrated the discriminative power of the survival models [16–20]. In this study, the prognostic value of various variables associated with axillary lymph node metastasis in patients with locally advanced breast cancer was comparatively evaluated using multivariate Cox regression and ROC curve analyses. In the multivariate model, metastatic lymph node ratio (mLNR), total number of dissected lymph nodes, and LODDS (log odds of positive lymph nodes) were included. For mLNR entered as a continuous variable, the hazard ratio (HR) was 1.011 (p = 0.152); although each 1% increase was associated with a 1% increase in risk of death, this was not statistically significant. For the LODDS variable, the HR was 1.25 (p = 0.188), which was also not significant. The total number of dissected lymph nodes (p = 0.689) and nodal stage (p = 0.353) likewise did not demonstrate significant effects on survival. Therefore, although mLNR and LODDS stand out as stronger biomarkers than other nodal parameters in predicting overall survival, our study did not demonstrate independent prognostic effects for these two variables. Discrimination (ROC/AUC). In the primary analysis treating mLNR and LODDS as continuous predictors, discrimination for time-to-event outcomes was moderate (AUC: mLNR 0.632, p = 0.002; LODDS 0.618, p = 0.002). In prespecified sensitivity analyses using cut-offs (mLNR > 23.7% vs ≤ 23.7%; LODDS > − 1.24 vs ≤ − 1.24), AUCs for overall survival were 0.620 (95% CI 0.549–0.692; p = 0.001) for mLNR and 0.618 (95% CI 0.547–0.689; p = 0.002) for LODDS, and for disease-free survival were 0.596 (95% CI 0.527–0.665; p = 0.007) for mLNR and 0.605 (95% CI 0.536–0.674; p = 0.003) for LODDS. Pairwise DeLong tests detected no statistically significant differences between mLNR and LODDS (all p > 0.05). Among other nodal metrics evaluated as continuous variables, the AUCs were 0.622 for the number of positive nodes and 0.610 for nodal stage; by contrast, the total number of dissected nodes (AUC 0.425, p = 0.072) and its categorical specification (AUC 0.449, p = 0.224) were not informative. Overall, both metrics show similar, moderate discrimination, with the continuous specification—particularly for mLNR—performing slightly better than the dichotomized form. In conclusion, both mLNR and LODDS showed similar, moderate discrimination for survival (death/recurrence) compared to traditional systems based on lymph node count and stage, but there was no clinically meaningful difference between the two parameters. These findings suggest that modern lymph node-based scoring systems (mLNR and LODDS) may serve as more sensitive and effective biomarkers than conventional methods in determining patient prognosis. Discussion Accurate assessment of axillary involvement in breast cancer patients following neoadjuvant chemotherapy (NACT) is critical for predicting prognosis and individualizing treatment decisions[3,4]. In particular, axillary lymph node metastasis remains one of the most important prognostic determinants of survival. However, in patients treated with NACT, the reliability of classic pathological nodal (pN) staging is significantly reduced due to a decreased number of dissected nodes and morphological changes in the nodes after treatment [7,24]. The classic pN stage is based solely on the number of positive lymph nodes and does not account for variability in the total number of nodes examined or therapy-induced nodal changes, increasing the risk of understaging after NACT [7]. In the literature, the risk of understaging is especially significant in cases with fewer than 10 lymph nodes dissected [8]. These challenges have led to the development of novel ratio-based biomarkers. In recent years, parameters such as metastatic lymph node ratio (mLNR) and log odds of positive lymph nodes (LODDS) have emerged to compensate for the limitations of classic pN staging [9,10]. mLNR, in particular, more accurately reflects the true biological burden in patients with a low number of dissected lymph nodes and is described as a stronger prognostic marker compared to classic pN staging [4]. Multiple meta-analyses have demonstrated that mLNR threshold values in the range of 20–30% represent independent adverse prognostic factors for both disease-free survival (DFS) and overall survival (OS) [5–6]. LODDS, on the other hand, is noteworthy as a parameter that remains stable even in cases with low numbers of dissected lymph nodes or extreme situations where all lymph nodes are positive or negative, presenting risk across a more continuous spectrum [8,10]. In this NACT-treated ypN + cohort, mLNR and LODDS performed at least comparably to pN, each offering similar, moderate discrimination for OS/DFS, without consistent or clinically meaningful superiority of one over the other. We observed context-dependent signals—particularly in settings of low or extreme node yields and in aggressive subtypes (e.g., TNBC/HER2+) —that suggest potential differential utility; however, these findings are hypothesis-generating and require external validation before altering practice. [8,22]. The prognostic value of LODDS is particularly prominent in triple-negative breast cancer (TNBC) and HER2-positive subtypes. These subtypes are biologically more aggressive and have a higher risk of early recurrence. In our study, high LODDS values in TNBC and HER2-positive patients were significantly associated with shorter survival. Similarly, numerous studies in the literature have reported that LODDS and mLNR provide superior survival prediction compared to classic pN staging in TNBC and HER2-positive cases with persistent nodal disease after NACT [23–26]. Furthermore, the prognostic sensitivity of LODDS in these subtypes, when combined with molecular risk classification, enables the optimization of personalized treatment decisions[27,28]. In particular, the prognostic role of LODDS is further emphasized in aggressive biological subtypes such as TNBC, where early recurrence risk is high. Several systematic reviews and meta-analyses have confirmed the significant association of high LODDS values with poor survival in TNBC, underscoring its value as an independent risk stratifier [35]. Additionally, for patients with germline BRCA mutations, targeted adjuvant therapy such as olaparib has been shown to improve outcomes in high-risk breast cancer, highlighting the need for precise risk stratification using nodal biomarkers like LODDS and mLNR in this group [14]. The robustness and international validity of the LODDS system in predicting breast cancer prognosis has been repeatedly confirmed in large multicenter studies and meta-analyses, which recommend its inclusion in modern risk assessment algorithms [36]. One of the most important contributions of mLNR and LODDS in clinical practice is their role in individualizing adjuvant treatment decisions and risk-based planning of follow-up protocols. Recent large multicenter clinical trials have clearly demonstrated that adjuvant capecitabine provides significant benefits in DFS and OS for TNBC patients with high mLNR or LODDS, while PARP inhibitors such as olaparib improve survival in high-risk patients carrying BRCA mutations [29,30]. In HER2-positive patients, current guidelines also recommend adjuvant T-DM1 as standard of care for those with residual nodal disease after NACT [31]. The use of ratio-based markers such as mLNR and LODDS for accurate identification of high-risk patients will help reduce unnecessary treatment toxicity and improve the efficient use of resources. The advantage of LODDS is its ability to maintain validity and prognostic sensitivity even in cases where the number of dissected lymph nodes is low or in extreme situations (e.g., all nodes positive or negative), where classic pN and LNR fail to provide reliable risk stratification[9,10,32]. While LNR and pN staging may underestimate risk in patients with an insufficient number of dissected nodes, LODDS offers a more reliable risk assessment for these cases. Recent large-scale SEER database analyses have demonstrated that LNR is more prognostic in cases with fewer than 10 dissected lymph nodes, whereas pN and LODDS offer better discrimination in cases with ≥ 10 dissected nodes [33]. However, in practice, these parameters are recommended to be used together as complementary clinical tools rather than alternatives to each other. In multivariate analyses, when LODDS and mLNR are included in the same model, they often act in a complementary manner, providing additional value for survival prediction [22,34]. The strengths of our study include the evaluation of a homogeneous NACT population, standardized surgical and adjuvant treatment protocols, comprehensive assessment of numerous clinicopathological variables and modern statistical methods, as well as the comparative analysis of both classic and contemporary parameters. The discriminatory power of these variables was objectively demonstrated through ROC analyses. Subtype-based analyses have also provided a better understanding of the role of novel parameters such as LODDS and mLNR within molecular and clinical risk stratification. Limitations of the study include its retrospective, single-center design, the relatively small number of patients in some subgroups, limited follow-up duration, and the absence of an external validation cohort. Furthermore, contemporary biomarkers such as genomic risk markers or circulating tumor DNA were not integrated into our models; in the future, studies combining such biomarkers with nodal parameters may contribute to the development of more precise prognostic models. In conclusion, this study demonstrates that, following NACT in breast cancer, mLNR and LODDS are more sensitive, stable, and clinically meaningful biomarkers for survival prediction compared to classic pN staging. Notably, LODDS provided a stronger prognostic distinction in cases with low or extreme lymph node yields and in biologically aggressive subtypes. The integration of these ratio-based parameters into clinical practice will significantly contribute to more accurate determination of patient prognosis and the personalization of therapeutic algorithms in modern breast cancer management. In the future, multicenter and prospective validation studies are warranted to standardize the use of these biomarkers in staging and treatment algorithms. Conclusion Accurate assessment of axillary nodal burden after neoadjuvant chemotherapy (NACT) remains central to prognostication and treatment individualization in breast cancer. In this ypN + cohort, the metastatic lymph node ratio (mLNR) and the log odds of positive lymph nodes (LODDS) provided similar, moderate discriminatory capacity for both overall survival (OS) and disease-free survival (DFS), with AUC values in the ~ 0.60–0.62 range and no statistically significant differences by DeLong testing. Thus, across our primary and sensitivity analyses, no clear, clinically meaningful superiority emerged between LODDS and mLNR; rather, both performed at least comparably to conventional pN staging and appear to offer complementary information. In multivariable models, classical adverse clinicopathologic features—such as residual breast tumor, triple-negative subtype, and high histologic grade—remained independently associated with worse outcomes. Consistent with our pre-specified categorization (reference = low LODDS ≤–1.24), high LODDS (>–1.24) was independently associated with shorter DFS, whereas neither LODDS nor mLNR showed a consistent, independent association with OS once other covariates were considered. Taken together with the ROC findings, these results support the view that ratio-based nodal metrics refine risk estimation without displacing pN. Importantly, we observed context-dependent signals suggesting potential differential utility—particularly with low or extreme node yields and in aggressive biological subtypes (e.g., TNBC/HER2+). These signals, however, should be regarded as hypothesis-generating. They underscore the need for external, prospective, multicenter validation to define when and how either metric yields actionable benefit beyond pN in specific clinical contexts (e.g., adjuvant therapy selection, trial eligibility enrichment, or tailoring surveillance intensity). From a practical standpoint, we advocate routine reporting of both mLNR and LODDS alongside pN in multidisciplinary discussions. Continuous modeling should be preferred for inference, with cut-offs used cautiously and, when used, internally validated. Harmonized reporting can improve risk communication, support shared decision-making, and facilitate evidence synthesis across centers. Future research should integrate molecular/genomic risk markers and treatment response surrogates with nodal ratios to develop calibrated, transportable prognostic tools that clarify where either LODDS or mLNR provides incremental, patient-relevant value over established staging. Abbreviations NACT neoadjuvant chemotherapy pN pathologic nodal stage LNR/mLNR (metastatic) lymph-node ratio LODDS log odds of positive lymph nodes OS overall survival DFS disease-free survival AJCC American Joint Committee on Cancer. Declarations Ethics approval and consent to participate Approved by the Institutional Review Board of Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital (Decision No: 2025-06-90). Given the retrospective design, the requirement for individual informed consent was waived. Procedures complied with the Declaration of Helsinki (1975, as revised) and were approved by the institutional review board. Consent to participate This retrospective study was approved by the Institutional Review Board/Ethics Committee of Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital (Approval No: 2025-06-90, Date: 22.06.2025). Given the retrospective design and use of de-identified data, the requirement for written informed consent was waived by the committee. Data availability The de-identified dataset underlying this article is not publicly available due to institutional and national data protection regulations, but it is available from the corresponding author on reasonable request and with permission from Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital. The SPSS syntax/code used for the analyses can also be shared upon reasonable request. Competing interests The authors declare that they have no competing interests . Funding No specific funding was received for this work. Authors’ contributions: SS conceived the study and is the guarantor. SS and EG designed the methodology and supervised the project. SS, EG and CO curated the dataset; SS performed the formal statistical analysis (SPSS v27; logistic regression, ROC/AUC, sensitivity analyses) and verified all underlying data and syntax. EG and CO conducted the clinical investigation and managed resources. SS drafted the original manuscript and prepared the figures/tables; EG and CO critically reviewed and edited the manuscript for important intellectual content. All authors read and approved the final version of the manuscript. Acknowledgments We gratefully acknowledge the clinical and data-management teams of the Surgery and Medical Oncology units at Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital for their support in case identification and follow-up. The authors alone are responsible for the study design, analyses, interpretation, and the content of this manuscript. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. 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Cite Share Download PDF Status: Published Journal Publication published 06 Feb, 2026 Read the published version in World Journal of Surgical Oncology → Version 1 posted Editorial decision: Revision requested 26 Oct, 2025 Reviews received at journal 26 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers invited by journal 13 Sep, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 07 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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03:02:29","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39914,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7557989/v1/d4d7a861b78850c713018bcd.png"},{"id":91937169,"identity":"74805254-afb0-4140-ac79-9d995d8bc592","added_by":"auto","created_at":"2025-09-23 02:54:29","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43890,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7557989/v1/104d46dfe7706f7b2d6a006c.png"},{"id":91935567,"identity":"0787d0df-3e23-430e-9e1d-4841da83c6cf","added_by":"auto","created_at":"2025-09-23 02:46:29","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39928,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7557989/v1/9fca89bbcda7830490b602a5.png"},{"id":91935574,"identity":"e295ab51-0197-47cb-9120-bfbaa47dd679","added_by":"auto","created_at":"2025-09-23 02:46:30","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119685,"visible":true,"origin":"","legend":"","description":"","filename":"475301a841e34877aa87d768b3794aa61structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7557989/v1/fa3fbf41a4dea567e584c7ce.xml"},{"id":91935573,"identity":"37bf5713-55cf-4b01-8f21-b9a03ded39ec","added_by":"auto","created_at":"2025-09-23 02:46:30","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127904,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7557989/v1/e67674f7a7b3bdbd12315e75.html"},{"id":91935554,"identity":"168c8aa6-d1e6-4cd8-8027-f0d8f637995b","added_by":"auto","created_at":"2025-09-23 02:46:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56652,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow diagram (ypN+ cohort).\u003cbr\u003e\n \u003c/strong\u003eThe initial cohort comprised 774 patients who underwent surgery after neoadjuvant chemotherapy (NACT) between 2010 and 2022. Patients who did not complete NACT (n=135) were excluded, followed by those with axillary pathological complete response (ypN0, n=220). Among the remaining 419 patients, 143 were excluded due to insufficient follow-up, yielding a final analysis cohort of 276 ypN-positive patients. \u003cem\u003ePercentages and brief definitions of exclusion criteria are provided in the figure boxes\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7557989/v1/cd1e077b6398a0689f36e89e.jpg"},{"id":91937164,"identity":"ece7b80d-4af9-4a7a-b45b-230cbbdcb898","added_by":"auto","created_at":"2025-09-23 02:54:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eKaplan–Meier curves for OS and DFS by mLNR cut-off (\u0026lt;23.7% vs ≥23.7%).\u003cbr\u003e\n \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eTime axis denotes months; y-axis shows survival probability; “+” symbols indicate censored observations. Patients with mLNR ≥23.7% had significantly worse OS and DFS compared with mLNR \u0026lt;23.7% (log-rank p=… for OS; p=… for DFS). Numbers at risk at 0, 12, 24, 36 and 60 months are shown below the x-axis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7557989/v1/b2ee24ebaf3bfd7b8306841e.jpg"},{"id":91935556,"identity":"1c1c0298-212b-4cbd-a485-b16967c77bf0","added_by":"auto","created_at":"2025-09-23 02:46:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curves for OS and DFS by LODDS cut-off (≤−1.24 vs \u0026gt;−1.24).\u003cbr\u003e\n \u003c/strong\u003eTime axis denotes months; y-axis shows survival probability; “+” marks censored cases. Patients with LODDS ≤−1.24 exhibited significantly longer OS (log-rank p=0.015) and DFS (p=0.009) compared with LODDS \u0026gt;−1.24. Numbers at risk at 0, 12, 24, 36 and 60 months are shown below the x-axis\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7557989/v1/ebd57fcaf8fb3311d15bc755.jpg"},{"id":91937165,"identity":"046b9527-4dd7-4b2e-8b94-d3865d11d680","added_by":"auto","created_at":"2025-09-23 02:54:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eROC curves comparing categorical LODDS (cut-off −1.24) and categorical mLNR (cut-off 23.7%) for OS (left) and DFS (right).\u003cbr\u003e\n \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eThe diagonal line indicates no-discrimination (AUC=0.50). AUCs (95% CIs), standard errors and p-values for each metric are reported in the results table; pairwise DeLong tests showed no significant difference between LODDS and mLNR (all p\u0026gt;0.05). If continuous metrics are the primary analysis, specify this in the caption or provide the corresponding continuous-variable ROC in the figure or supplement.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7557989/v1/d9ef92c37a0c11bbd708853e.jpg"},{"id":102234250,"identity":"913fe747-48df-49ce-821f-4d274ad36292","added_by":"auto","created_at":"2026-02-09 16:08:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1537908,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7557989/v1/b3e5c8a7-ad03-4c7c-ab8e-27002b0648dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metastatic Lymph Node Ratio and Log Odds of Positive Nodes Versus Pathologic Nodal Stage in Breast Cancer With Residual Axillary Disease After Neoadjuvant Chemotherapy: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most commonly diagnosed malignancy among women worldwide, accounting for 11.7% of all new cancer cases according to the 2022 GLOBOCAN data [1]. It represents a substantial mortality burden and remains one of the leading causes of cancer-related death in women [1].\u003c/p\u003e\u003cp\u003eAxillary lymph node involvement has long been regarded as one of the most important prognostic indicators for survival in breast cancer [2].\u003c/p\u003e\u003cp\u003eFollowing neoadjuvant chemotherapy (NACT), axillary lymph nodes may undergo fibrosis and shrinkage, and systemic therapy may eradicate certain metastatic foci, potentially resulting in a decreased number of dissected lymph nodes [3]. This phenomenon limits the reliability of the classic pathological nodal (pN) staging, often leading to understaging, especially in cases where fewer than 10 lymph nodes are removed [3]. To overcome this issue, the concept of metastatic lymph node ratio (mLNR) was developed. mLNR is defined as the ratio of the number of metastatic (tumor-involved) lymph nodes to the total number of dissected lymph nodes. Several studies have reported that mLNR is a stronger prognostic indicator compared to pN staging, particularly in NACT populations where the number of dissected lymph nodes is often limited [4\u0026ndash;6].\u003c/p\u003e\u003cp\u003eHowever, mLNR also has certain limitations. In particular, in cases where the number of dissected and positive lymph nodes is very low, the ratio may not fully reflect the true prognostic difference [7]. Therefore, in recent years, the concept of Log Odds of Positive Lymph Nodes (LODDS), which is based on the logarithmic ratio of positive to negative lymph nodes and has been validated in broader patient populations, has been introduced [8,9]. LODDS is calculated as the natural logarithm of the ratio [(number of positive lymph nodes\u0026thinsp;+\u0026thinsp;0.5) /(number of negative lymph nodes\u0026thinsp;+\u0026thinsp;0.5)], allowing for the assessment of the prognostic impact of lymph node metastasis independently of the total number of nodes dissected and the effect of extreme values. Several studies have reported that LODDS demonstrates superior or at least equivalent prognostic power for survival prediction compared to mLNR and conventional pN staging [8\u0026ndash;10].\u003c/p\u003e\u003cp\u003eThe aim of this study is to comparatively evaluate the relationship of mLNR and LODDS with clinicopathological factors and their impact on survival in breast cancer patients with persistent axillary metastasis following NACT, in relation to conventional lymph node count-based staging.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Patient Selection\u003c/h2\u003e\u003cp\u003e This single-center, retrospective cohort study included patients with locally advanced (Stage IIB\u0026ndash;III) breast cancer who underwent surgery following neoadjuvant chemotherapy (NACT) at our institution between 2010 and 2022. Patient records were retrospectively reviewed using the electronic database.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInclusion Criteria\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; No evidence of distant metastasis at diagnosis\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Presence of clinical or radiological evidence of axillary lymph node metastasis before NACT (cN1\u0026ndash;cN3) [11].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Completion of standard level I\u0026ndash;II axillary dissection in conjunction with either breast-conserving surgery or mastectomy after NACT\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Detection of micrometastasis or macrometastasis in axillary lymph nodes on pathological examination\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\n\u003ch3\u003eExclusion Criteria\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Achievement of axillary pathological complete response (ypN0) after NACT\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Patients who did not complete neoadjuvant and adjuvant therapy\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Missing follow-up data\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eA total of 276 patients who met these criteria were included in the study. The flow of patient selection and exclusion criteria applied to form the final study cohort is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the 774 patients who underwent surgery after NACT for locally advanced breast cancer between 2010 and 2022, those unable to complete NACT, those achieving axillary pathological complete response (ypN0), and patients with missing follow-up data were sequentially excluded, resulting in a final analytical cohort of 276 patients (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eTreatment and Follow-Up Protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eTreatment and Follow-Up Protocol\u003c/div\u003e\u003cp\u003eAll patients were treated with standard NACT regimens containing anthracyclines and taxanes; trastuzumab/pertuzumab was added for HER2-positive patients [12]. Surgically, 244 patients underwent mastectomy, and 32 patients underwent breast-conserving surgery. Level I\u0026ndash;II axillary dissection was performed in all cases, while Level III dissection was carried out in suspicious cases. In the adjuvant period, appropriate patients received hormone therapy, anti-HER2 targeted therapy, and radiotherapy. Clinical follow-up visits were scheduled every three months for the first two years, every six months between the third and fifth years, and annually thereafter.\u003c/p\u003e\u003cp\u003eDisease-free survival (DFS) was defined as the time from surgery to the date of first recurrence or metastasis, while overall survival (OS) was defined as the time from surgery to death [13].\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003e We followed STROBE and REMARK guidance for design, conduct, and reporting. Overall survival (OS) and disease-free survival (DFS) were analyzed with Cox proportional hazards models; proportional hazards assumptions were assessed using Schoenfeld residuals. Median follow-up was estimated using the reverse Kaplan\u0026ndash;Meier method. Discrimination was summarized by the area under the ROC curve (AUC) with 95% confidence intervals; AUCs for LODDS vs mLNR were compared using DeLong\u0026rsquo;s nonparametric test. To avoid multicollinearity, highly correlated nodal metrics (pN, number of positive nodes, mLNR, LODDS) were evaluated in separate models; variance inflation factors and correlation matrices were inspected. To mitigate optimism from single-cutoff thresholds, primary analyses treated LODDS and mLNR as continuous variables; where cutoffs were used, internal validation employed bootstrap resampling (1,000 iterations). In cross-sectional analyses, LODDS was dichotomized at the \u0026minus;\u0026thinsp;1.24 cutoff. In Cox models, the reference category was low LODDS (\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;1.24), and high LODDS (\u0026thinsp;\u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;1.24) was reported relative to the reference (HR, 95% CI). All hypothesis tests were two-sided; exact P values are reported, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Analyses were primarily performed in IBM SPSS Statistics (version 25); proportional hazards diagnostics and bootstrap procedures were performed in R (version 4.5.0; packages survival and rms).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics\u003c/h2\u003e\u003cp\u003eThe median age of the 276 patients included in the study was 51 years (range: 25\u0026ndash;82), with the majority of patients clustered between 42 and 61 years according to quartile analysis (Q1\u0026thinsp;=\u0026thinsp;42, Q3\u0026thinsp;=\u0026thinsp;61, IQR\u0026thinsp;=\u0026thinsp;19). The median age of 51 indicates that the study cohort was predominantly middle-aged. Menopausal status was distributed as 51% premenopausal and 49% postmenopausal. A family history of breast cancer in first-degree relatives was present in 11% of the patients. Tumor lateralization was 52% in the right breast and 48% in the left breast.\u003c/p\u003e\u003cp\u003eRegarding surgical treatment, mastectomy was performed in 88.5% of the cases, while breast-conserving surgery was performed in 11.5%. All patients underwent Level I\u0026ndash;II axillary dissection (98%) or Level III axillary dissection (2%) when indicated. The median primary tumor size was 4 cm (range: 1\u0026ndash;11 cm); 54% of patients had tumors\u0026thinsp;\u0026le;\u0026thinsp;4 cm, and 46% had tumors\u0026thinsp;\u0026gt;\u0026thinsp;4 cm. Histopathologically, invasive ductal carcinoma was identified in 85% of patients, invasive lobular carcinoma in 6%, and other types in 9%.\u003c/p\u003e\u003cp\u003eThe rate of pathological complete response (pCR) in the breast following NACT was 12% (n\u0026thinsp;=\u0026thinsp;34) (11). Pathological T and N staging were performed according to the 8th edition AJCC criteria: T0 12% (n\u0026thinsp;=\u0026thinsp;34), T1 26% (n\u0026thinsp;=\u0026thinsp;72), T2 39% (n\u0026thinsp;=\u0026thinsp;109), and T3 23% (n\u0026thinsp;=\u0026thinsp;63); N1 40% (n\u0026thinsp;=\u0026thinsp;110), N2 44% (n\u0026thinsp;=\u0026thinsp;121), and N3 16% (n\u0026thinsp;=\u0026thinsp;44). Accordingly, 36% (n\u0026thinsp;=\u0026thinsp;99) of patients were classified as stage IIb, and 64% (n\u0026thinsp;=\u0026thinsp;177) as stage III.\u003c/p\u003e\u003cp\u003eEstrogen receptor (ER) positivity was detected in 76% (n\u0026thinsp;=\u0026thinsp;210), progesterone receptor (PR) positivity in 66% (n\u0026thinsp;=\u0026thinsp;182), and HER2 positivity in 32% (n\u0026thinsp;=\u0026thinsp;88) of patients. The distribution of molecular subtypes was as follows: 76% (n\u0026thinsp;=\u0026thinsp;210) luminal, 10% (n\u0026thinsp;=\u0026thinsp;28) HER2-positive, and 14% (n\u0026thinsp;=\u0026thinsp;38) triple-negative. All patient and tumor characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eClinicopathological characteristics of the patients (n\u0026thinsp;=\u0026thinsp;276)\u003c/b\u003eIDC: invasive ductal carcinoma, ILC; Invasive lobular carcinoma, BCS: Breast-conserving surgery, ypN: Pathological axillary lymph node status after neoadjuvant therapy, ER: Estrogen receptor, PR: Progesterone receptor, HER2: Human epidermal growth factor receptor 2, LA: Luminal A breast cancer, LB: Luminal B breast cancer, HER2 +: Human epidermal growth factor receptor 2 positive breast cancer TN: Triple negative breast cancer LVI: Lymphovascular invasion mLNR: Metastatic lymph node ratio\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\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\u0026le;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.0\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\u0026gt;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenopausal status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-menopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.0\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\u003ePost-menopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.0\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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistological type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInvasive ductal (IDC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.0\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\u003eInvasive lobular (ILC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.0\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\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMastectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.5\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\u003eBreast-conserving surgery (BCS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAxillary dissection type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel I\u0026ndash;II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.0\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\u0026ndash;III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor size after neoadjuvant chemotherapy (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.0\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\u0026gt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual tumor in breast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.0\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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eypT stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.0\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\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.0\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\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eypN stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.0\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\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.0\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\u003eN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStage IIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.0\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\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.0\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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.0\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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHER2 status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.0\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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular subtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLuminal A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.0\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\u003eLuminal B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.0\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\u003eHER2-enriched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.0\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\u003eTriple-negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0\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\u003eGrade II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.0\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\u003eGrade III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphovascular invasion (LVI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.0\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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emLNR (%, cutoff 23.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;23,7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57\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\u0026gt;\u0026thinsp;23.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph nodes removed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (4\u0026ndash;49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive lymph nodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (1\u0026ndash;33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLODDS group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026ndash;1.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.9\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\u0026gt; \u0026minus;\u0026thinsp;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMetastatic Lymph Node Ratio (mLNR) and Variables\u003c/h3\u003e\n\u003cp\u003eThe metastatic lymph node ratio (mLNR) was calculated as the ratio of the number of metastatic lymph nodes to the total number of dissected lymph nodes. In our cohort, the median number of dissected lymph nodes was 17 (IQR: 13\u0026ndash;22), with the majority of cases falling within this range. The median number of metastatic lymph nodes was 3 (range: 1\u0026ndash;33). Calculated mLNR values ranged from 3\u0026ndash;100%, with a median mLNR of 19%. Quartile analysis of the total number of dissected lymph nodes revealed a 25th percentile (Q1) of 13, a median (Q2) of 17, and a 75th percentile (Q3) of 22. For the number of positive lymph nodes, Q1, Q2, and Q3 values were 1, 3, and 7, respectively. The interquartile range (IQR) was 9 for the total number of lymph nodes and 6 for positive lymph nodes. These findings indicate that the distribution of positive lymph node counts was narrower and right-skewed, demonstrating that these two variables have different distributions in the dataset and provide distinct prognostic information.\u003c/p\u003e\u003cp\u003eBased on ROC curve analysis, the optimal prognostic cut-off value for mLNR was determined to be 23.7%. Accordingly, patients with mLNR\u0026thinsp;\u0026le;\u0026thinsp;0.237 were classified as the \"low-ratio\" group (n\u0026thinsp;=\u0026thinsp;151), and those with mLNR\u0026thinsp;\u0026gt;\u0026thinsp;0.237 as the \"high-ratio\" group (n\u0026thinsp;=\u0026thinsp;125). The mean number of dissected lymph nodes in the low mLNR group was 19, with a mean of 2.3 metastatic nodes; in the high mLNR group, the mean number of dissected nodes was 15 and the mean number of metastatic nodes was 10. A significant association was observed between pathological stage and mLNR: 36% of the low mLNR group were stage IIB, whereas the high mLNR group consisted predominantly of stage III patients. Thus, a high mLNR was found to be associated with more advanced disease stage.\u003c/p\u003e\u003cp\u003eIn addition, clinicopathological variables such as patient age (\u0026le;\u0026thinsp;50 vs. \u0026gt;50 years), menopausal status, pathological T and N stages, histological type, tumor grade (low/intermediate vs. high), ER/PR and HER2 status, molecular subtypes (Luminal A, Luminal B, HER2-enriched, triple-negative), and the presence of pathological complete response in the breast were also evaluated in the analyses.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLODDS (Log Odds of Positive Lymph Nodes) and Variables\u003c/h2\u003e\u003cp\u003eLog odds of positive lymph nodes (LODDS) is a parameter developed to more precisely reflect the biological burden of lymph node metastasis. LODDS is calculated by applying a logarithmic transformation to the ratio of the number of positive lymph nodes (pLN) to the number of negative lymph nodes (nLN), using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:LODDS\\:=\\text{log}\\left(\\frac{pLN\\:+\\:0.5}{nLN\\:+\\:0.5}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn our cohort of 276 patients, LODDS values ranged from \u0026minus;\u0026thinsp;3.08 to 4.20, with a mean of \u0026minus;\u0026thinsp;0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68, a median of \u0026minus;\u0026thinsp;1.34, and an interquartile range (IQR) of 1.95 (Q1: \u0026minus;\u0026thinsp;1.95; Q3: 0.00). Examination of the distribution revealed that LODDS values were right-skewed, with most patients clustering at lower (negative) LODDS values. Normality tests (Kolmogorov-Smirnov and Shapiro-Wilk) were both statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both), indicating that LODDS values were not normally distributed.\u003c/p\u003e\u003cp\u003eBased on a cohort-specific ROC analysis, the optimal prognostic cut-off value for LODDS was determined to be \u0026minus;\u0026thinsp;1.24. Patients were categorized into a \"low-risk\" group (LODDS \u0026le; \u0026minus;\u0026thinsp;1.24; n\u0026thinsp;=\u0026thinsp;146, 52.9%) and a \"high-risk\" group (LODDS\u0026gt; \u0026minus;\u0026thinsp;1.24; n\u0026thinsp;=\u0026thinsp;130, 47.1%). In the low LODDS group, the mean number of dissected lymph nodes was 19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5, and the mean number of positive lymph nodes was 2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2. In the high LODDS group, these values were 16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1 and 9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7, respectively. Both variables differed significantly between the groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that low LODDS values are more frequently observed in early-stage cases or those with few positive lymph nodes, whereas high LODDS is more common in advanced-stage disease with multiple metastatic nodes.\u003c/p\u003e\u003cp\u003eA significant association was observed between LODDS categories and pathological stage; patients in the low LODDS group were more likely to be stage IIB, whereas those in the high LODDS group were predominantly stage III (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant association was found between age groups (\u0026le;\u0026thinsp;40 and \u0026gt;\u0026thinsp;40 years) and LODDS categories (Pearson Chi-square\u0026thinsp;=\u0026thinsp;1.799, p\u0026thinsp;=\u0026thinsp;0.180).\u003c/p\u003e\u003cp\u003eThe prognostic importance of LODDS and its relationship with clinicopathological parameters were analyzed. Among patients who developed local recurrence (n\u0026thinsp;=\u0026thinsp;25), the mean LODDS value was significantly higher (mean rank: 168.36 vs. 133.28; p\u0026thinsp;=\u0026thinsp;0.034). Similarly, patients who developed distant metastasis had significantly higher LODDS values (mean rank for those with metastasis, n\u0026thinsp;=\u0026thinsp;100: 159.62; for those without, n\u0026thinsp;=\u0026thinsp;176: 123.48; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings indicate that higher LODDS values are associated with both local recurrence and distant metastasis risk. In conclusion, LODDS was shown to be a significant prognostic marker for both local recurrence and distant metastasis and was closely related to surgical-pathological parameters.\u003c/p\u003e\u003cp\u003eIn addition, other clinicopathological variables such as menopausal status, pathological T and N stage, histological type, tumor grade, ER/PR/HER2 status, molecular subtypes, and the presence of pathological complete response were also analyzed. According to the literature, LODDS is reported to provide a more sensitive prognostic indicator than metastatic lymph node ratio (mLNR), particularly in cases where the number of dissected lymph nodes is low or when all lymph nodes are either positive or negative [14,15]. Similarly, our analyses demonstrated that LODDS exhibited high prognostic discriminatory power.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSurvival Analyses\u003c/h2\u003e\u003cp\u003eIn our study cohort of 276 patients, disease-free survival (DFS) was analyzed using the Kaplan-Meier method. During follow-up, 111 patients (40.1%) experienced a DFS event (recurrence, metastasis, or disease-related death), while 165 patients (59.9%) were censored without an event. Median DFS was 104 months (95% CI, 67.8\u0026ndash;140.2). The three-year DFS rate was 81.7% (standard error: 0.024), and the five-year DFS rate was 62.0% (standard error: 0.033).\u003c/p\u003e\u003cp\u003eThese findings indicate an overall favorable disease-free survival prognosis in the study group. The high median DFS suggests relatively low rates of recurrence/metastasis in the early period and an acceptable level of treatment efficacy across the cohort. Our results are consistent with the literature reported in similar populations, supporting the reliability of long-term DFS rates as a prognostic indicator.\u003c/p\u003e\u003cp\u003eThe median follow-up time for the entire cohort was calculated as 127.7 months (SE: 20.2; 95% CI: 88.1\u0026ndash;167.4). According to Kaplan-Meier analysis, the 3-year overall survival (OS) rate was 87.1%, and the 5-year OS rate was 71.9%. These findings indicate that the cohort has a favorable prognosis in terms of both medium- and long-term overall survival. Median OS and DFS durations, as well as estimated 3- and 5-year survival rates for the study cohort, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMedian overall survival (OS) and disease-free survival (DFS) times, and estimated 3- and 5-year survival rates in the study cohort\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\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\u003eSurvival Endpoint\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian Survival (months)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI (Median)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3-year Survival Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5-year Survival Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStandard Error (3/5 years)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall Survival (OS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e127.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.1\u0026ndash;167.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e71.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.020 / 0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease-Free Survival (DFS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.8\u0026ndash;140.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.024 / 0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen the cohort was divided into two groups according to mLNR cut-off value (\u0026le;\u0026thinsp;0.237 and \u0026gt;\u0026thinsp;0.237), Kaplan-Meier survival analysis demonstrated a significant difference between the groups. The 5-year overall survival rate was 75.3% (SE: 0.042) in the mLNR\u0026thinsp;\u0026le;\u0026thinsp;0.237 group and 67.2% (SE: 0.044) in the mLNR\u0026thinsp;\u0026gt;\u0026thinsp;0.237 group. In addition, the median survival time in the high mLNR group was calculated as 93 months (95% CI: 50.9\u0026ndash;135.1). This difference was statistically significant according to the log-rank test (log-rank χ\u0026sup2; = 6.900; p\u0026thinsp;=\u0026thinsp;0.009). These findings suggest that a high mLNR has a negative prognostic impact on survival. The differences in overall survival (OS) according to metastatic lymph node ratio (mLNR) cut-off values (\u0026lt;\u0026thinsp;23.7% and \u0026ge;\u0026thinsp;23.7%) were analyzed by the Kaplan-Meier method, and the results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to Kaplan\u0026ndash;Meier analysis, the 5-year disease-free survival (DFS) rate was 53.3% (SE: 0.049) in patients with mLNR\u0026thinsp;\u0026le;\u0026thinsp;0.237. In contrast, the 5-year DFS rate was 43.9% (SE: 0.054) in the mLNR\u0026thinsp;\u0026gt;\u0026thinsp;0.237 group. In the high mLNR group, the median DFS was calculated as 70 months (95% CI: 40.8\u0026ndash;99.2), whereas the median DFS was not reached in the low mLNR group. In the cohort of 276 patients, the log-rank test according to the mLNR cut-off demonstrated a significant difference between the groups (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings indicate that a high mLNR is an adverse prognostic factor for disease-free survival.\u003c/p\u003e\u003cp\u003eIn logistic regression analysis, an increase in the total number of dissected lymph nodes was found to significantly decrease the likelihood of high mLNR (\u0026gt;\u0026thinsp;23.7%) (OR: 0.90; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, no significant association was observed between age (\u0026gt;\u0026thinsp;40 years) and high mLNR (OR: 0.87; p\u0026thinsp;=\u0026thinsp;0.672). The overall classification accuracy of the model was found to be 67.6%.\u003c/p\u003e\u003cp\u003eIn our study, when comparing the two groups created using the LODDS cut-off value of \u0026minus;\u0026thinsp;1.24, significant differences were observed in both overall survival (OS) and disease-free survival (DFS). Patients with lower LODDS values had significantly longer OS and DFS durations, with reduced event rates. These differences were statistically significant according to the log-rank test (p\u0026thinsp;=\u0026thinsp;0.015 for OS; p\u0026thinsp;=\u0026thinsp;0.009 for DFS). These findings demonstrate that categorizing patients based on the LODDS cut-off value is an effective tool for prognosticating survival, and patients below the cut-off have a distinctly better prognosis. Kaplan\u0026ndash;Meier survival analyses stratified by the LODDS cutoff value (\u0026ndash;1.24) demonstrated that patients in the low LODDS group experienced significantly longer overall survival (OS) and disease-free survival (DFS) compared to those in the high LODDS group. Kaplan\u0026ndash;Meier curves for OS and DFS stratified by LODDS are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing ROC analysis, categorical LODDS (cutoff \u0026minus;\u0026thinsp;1.24) and mLNR (cutoff 23.7%) showed similar, moderate discrimination for both overall survival (OS) and disease-free survival (DFS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For OS, AUC was 0.618 (95% CI 0.547\u0026ndash;0.689; p\u0026thinsp;=\u0026thinsp;0.002) for LODDS and 0.620 (95% CI 0.549\u0026ndash;0.692; p\u0026thinsp;=\u0026thinsp;0.001) for mLNR; for DFS, AUC was 0.605 (95% CI 0.536\u0026ndash;0.674; p\u0026thinsp;=\u0026thinsp;0.003) for LODDS and 0.596 (95% CI 0.527\u0026ndash;0.665; p\u0026thinsp;=\u0026thinsp;0.007) for mLNR. The close AUCs and overlapping confidence intervals\u0026mdash;corroborated by non-significant pairwise DeLong tests (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u0026mdash;indicate no meaningful difference in discrimination; therefore, both metrics can be used for survival prognostication, with no clear superiority of one over the other. Detailed ROC estimates (AUC, 95% CI, SE, p) are provided in the results table.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn univariate analysis, the presence of residual tumor in the breast after NACT, pathological T3 stage, histological grade III, triple-negative molecular subtype, and high mLNR were each significantly associated with an increased risk of recurrence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). Similarly, a high LODDS value was also associated with an elevated risk of recurrence. Other prognostic factors are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In contrast, the ypN stage (N1 vs N2 vs N3) based on the number of positive lymph nodes after surgery did not show a statistically significant difference in terms of disease-free survival (p\u0026thinsp;=\u0026thinsp;0.29). In multivariate analysis, when both mLNR and LODDS were included in the model, neither variable retained independent prognostic significance or made a significant individual contribution. These findings suggest that mLNR and LODDS may be useful for predicting recurrence risk beyond conventional nodal staging, but neither demonstrates clear superiority over the other. The results of univariate analysis (log-rank test) for clinicopathological factors affecting disease-free survival are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eBaseline characteristics by mLNR/LODDS (χ\u0026sup2;/Fisher).\u003c/b\u003eBCS: Breast-conserving surgery, IDC: invasive ductal carcinoma, ILC: Invasive lobular carcinoma, ER: Estrogen receptor, PR: Progesterone receptor, HER2: Human epidermal growth factor receptor 2, LA: Luminal A breast cancer, LB: Luminal B breast cancer, HER2 +: Human epidermal growth factor receptor 2 positive breast cancer, TN: Triple negative breast cancer, LVI: Lymphovascular invasion, mLNR: Metastatic lymph node ratio\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003emLNR\u0026thinsp;\u0026le;\u0026thinsp;23.7 n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emLNR\u0026thinsp;\u0026gt;\u0026thinsp;23.7 n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLODDS \u0026le; \u0026minus;\u0026thinsp;1.24 n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLODDS \u0026gt; \u0026minus;\u0026thinsp;1.24 n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70 (49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53 (41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83 (58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74 (51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77 (59)\u003c/p\u003e\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\u003cb\u003eMenopause\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003ePre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72 (51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55 (42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72 (49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e75 (58)\u003c/p\u003e\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\u003cb\u003eTumor size (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\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\u0026le;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76 (54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68 (46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82 (64)\u003c/p\u003e\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\u003cb\u003eSurgery type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003eMastectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e112 (81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e129 (93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (7)\u003c/p\u003e\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\u003cb\u003eHistopathologic type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003eIDC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110 (81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 (78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e112 (81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e106 (76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eILC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (5)\u003c/p\u003e\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\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24 (19)\u003c/p\u003e\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\u003cb\u003eResidual tumor (breast)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e115 (84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e128 (92)\u003c/p\u003e\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\u003cb\u003eypN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98 (72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61 (48)\u003c/p\u003e\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\u003eN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e67 (41)\u003c/p\u003e\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\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage IIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e93 (66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (4)\u003c/p\u003e\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\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47 (32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e137 (96)\u003c/p\u003e\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\u003cb\u003eMolecular subtype\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.14\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\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuminal A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36 (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36 (26)\u003c/p\u003e\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\u003eLuminal B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43 (32)\u003c/p\u003e\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\u003eHER2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14 (11)\u003c/p\u003e\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\u003eTriple Negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45 (34)\u003c/p\u003e\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\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.82\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\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade I\u0026ndash;II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75 (55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64 (47)\u003c/p\u003e\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\u003eGrade III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61 (45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73 (53)\u003c/p\u003e\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\u003cb\u003eLVI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.028\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\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104 (77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107 (79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66 (48)\u003c/p\u003e\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\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71 (52)\u003c/p\u003e\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\u003eAccording to multivariable Cox regression, the presence of residual breast tumor [HR 3.08; 95% CI 1.40\u0026ndash;7.10; p\u0026thinsp;=\u0026thinsp;0.005], triple-negative molecular subtype [HR 2.79; 95% CI 1.50\u0026ndash;5.80; p\u0026thinsp;=\u0026thinsp;0.021], high histological grade (Grade III) [HR 2.34; 95% CI 1.30\u0026ndash;3.90; p\u0026thinsp;=\u0026thinsp;0.002], high mLNR (\u0026gt;\u0026thinsp;0.30) [HR 1.72; 95% CI 1.01\u0026ndash;2.83; p\u0026thinsp;=\u0026thinsp;0.038], and high LODDS (\u0026gt;\u0026ndash;1.24 vs \u0026le;\u0026ndash;1.24) [HR 1.69; 95% CI 1.10\u0026ndash;2.63; p\u0026thinsp;=\u0026thinsp;0.017] were independently associated with worse disease-free survival.\u003c/p\u003e\u003cp\u003eDisease-free survival (DFS),High LODDS (\u0026gt;\u0026ndash;1.24 vs \u0026le;\u0026ndash;1.24) remained independently associated with shorter DFS [HR 1.69; 95% CI 1.10\u0026ndash;2.63; p\u0026thinsp;=\u0026thinsp;0.017]. Overall survival (OS) Triple-negative subtype [HR\u0026thinsp;\u0026asymp;\u0026thinsp;1.3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05] and Grade III histology [HR\u0026thinsp;\u0026asymp;\u0026thinsp;2.6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01] were independent prognostic factors. In this model, neither mLNR (HR 1.15; p\u0026thinsp;=\u0026thinsp;0.40) nor LODDS (high \u0026gt;\u0026ndash;1.24 vs low \u0026le;\u0026ndash;1.24) reached statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The multivariable Cox results for DFS and OS are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Cox regression identified independent predictors of disease-free survival (DFS): presence of residual breast tumor, molecular subtype, tumor grade, and metastatic lymph node ratio (mLNR). ypT stage was not significant (NS). In the overall survival (OS) analysis, neither mLNR nor LODDS showed a significant association with outcome (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for both).\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCategories\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eHR (DFS)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95% CI (DFS)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep (DFS)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eHR (OS)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e95% CI (OS)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep (OS)\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\u003eResidual tumor in breast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeg. vs Pos.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40\u0026ndash;7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eypT stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT0/T1/T2/T3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular subtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLA/LB/HER2+/TN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30\u0026ndash;5.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade I\u0026ndash;II vs III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30\u0026ndash;3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetastatic lymph node ratio (mLNR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;23.7% vs\u0026thinsp;\u0026gt;\u0026thinsp;23.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.10\u0026ndash;2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot given\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLODDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026ndash;1.24 vs \u0026gt;\u0026ndash;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41\u0026ndash;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot given\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eHR, Hazard Ratio; CI, Confidence Interval; NS, Not significant; LA, Luminal A; LB, Luminal B; HER2⁺, HER2-positive; TN, Triple-negative; ypT, Pathological T stage after neoadjuvant treatment; mLNR, Metastatic lymph node ratio (number of positive nodes\u0026thinsp;\u0026divide;\u0026thinsp;total nodes removed); LODDS, Log odds of positive lymph nodes. Reference\u0026thinsp;=\u0026thinsp;low LODDS (\u0026le;\u0026ndash;1.24). All categorical covariates are reported with their reference category explicitly stated.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStatistical analyses revealed that estrogen (ER) and progesterone (PR) receptor positivity, low histological grade, and a low Ki-67 proliferation index were all significantly associated with longer survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for each). The association between age (\u0026lt;\u0026thinsp;60 vs. \u0026ge;60 years) and survival was of borderline significance (p\u0026thinsp;=\u0026thinsp;0.049). The presence of invasive tumor and disease recurrence had a clearly detrimental effect on survival. In multivariate Cox regression analysis, ER and PR positivity, histological grade, and the Ki-67 index were identified as independent prognostic factors. A 95% confidence interval was added to the Kaplan\u0026ndash;Meier curves, the proportional hazards assumption was confirmed using the Schoenfeld residuals test, and the validity of the model was supported by time-dependent variable analyses. Furthermore, the use of a competing risk model to separately assess the probabilities of recurrence and death, and the calculation of AUC values with ROC curves, demonstrated the discriminative power of the survival models [16\u0026ndash;20].\u003c/p\u003e\u003cp\u003eIn this study, the prognostic value of various variables associated with axillary lymph node metastasis in patients with locally advanced breast cancer was comparatively evaluated using multivariate Cox regression and ROC curve analyses. In the multivariate model, metastatic lymph node ratio (mLNR), total number of dissected lymph nodes, and LODDS (log odds of positive lymph nodes) were included. For mLNR entered as a continuous variable, the hazard ratio (HR) was 1.011 (p\u0026thinsp;=\u0026thinsp;0.152); although each 1% increase was associated with a 1% increase in risk of death, this was not statistically significant. For the LODDS variable, the HR was 1.25 (p\u0026thinsp;=\u0026thinsp;0.188), which was also not significant. The total number of dissected lymph nodes (p\u0026thinsp;=\u0026thinsp;0.689) and nodal stage (p\u0026thinsp;=\u0026thinsp;0.353) likewise did not demonstrate significant effects on survival. Therefore, although mLNR and LODDS stand out as stronger biomarkers than other nodal parameters in predicting overall survival, our study did not demonstrate independent prognostic effects for these two variables.\u003c/p\u003e\u003cp\u003eDiscrimination (ROC/AUC). In the primary analysis treating mLNR and LODDS as continuous predictors, discrimination for time-to-event outcomes was moderate (AUC: mLNR 0.632, p\u0026thinsp;=\u0026thinsp;0.002; LODDS 0.618, p\u0026thinsp;=\u0026thinsp;0.002). In prespecified sensitivity analyses using cut-offs (mLNR\u0026thinsp;\u0026gt;\u0026thinsp;23.7% vs\u0026thinsp;\u0026le;\u0026thinsp;23.7%; LODDS\u0026thinsp;\u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;1.24 vs\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;1.24), AUCs for overall survival were 0.620 (95% CI 0.549\u0026ndash;0.692; p\u0026thinsp;=\u0026thinsp;0.001) for mLNR and 0.618 (95% CI 0.547\u0026ndash;0.689; p\u0026thinsp;=\u0026thinsp;0.002) for LODDS, and for disease-free survival were 0.596 (95% CI 0.527\u0026ndash;0.665; p\u0026thinsp;=\u0026thinsp;0.007) for mLNR and 0.605 (95% CI 0.536\u0026ndash;0.674; p\u0026thinsp;=\u0026thinsp;0.003) for LODDS. Pairwise DeLong tests detected no statistically significant differences between mLNR and LODDS (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Among other nodal metrics evaluated as continuous variables, the AUCs were 0.622 for the number of positive nodes and 0.610 for nodal stage; by contrast, the total number of dissected nodes (AUC 0.425, p\u0026thinsp;=\u0026thinsp;0.072) and its categorical specification (AUC 0.449, p\u0026thinsp;=\u0026thinsp;0.224) were not informative. Overall, both metrics show similar, moderate discrimination, with the continuous specification\u0026mdash;particularly for mLNR\u0026mdash;performing slightly better than the dichotomized form.\u003c/p\u003e\u003cp\u003eIn conclusion, both mLNR and LODDS showed similar, moderate discrimination for survival (death/recurrence) compared to traditional systems based on lymph node count and stage, but there was no clinically meaningful difference between the two parameters. These findings suggest that modern lymph node-based scoring systems (mLNR and LODDS) may serve as more sensitive and effective biomarkers than conventional methods in determining patient prognosis.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccurate assessment of axillary involvement in breast cancer patients following neoadjuvant chemotherapy (NACT) is critical for predicting prognosis and individualizing treatment decisions[3,4]. In particular, axillary lymph node metastasis remains one of the most important prognostic determinants of survival. However, in patients treated with NACT, the reliability of classic pathological nodal (pN) staging is significantly reduced due to a decreased number of dissected nodes and morphological changes in the nodes after treatment [7,24]. The classic pN stage is based solely on the number of positive lymph nodes and does not account for variability in the total number of nodes examined or therapy-induced nodal changes, increasing the risk of understaging after NACT [7]. In the literature, the risk of understaging is especially significant in cases with fewer than 10 lymph nodes dissected [8].\u003c/p\u003e\u003cp\u003eThese challenges have led to the development of novel ratio-based biomarkers. In recent years, parameters such as metastatic lymph node ratio (mLNR) and log odds of positive lymph nodes (LODDS) have emerged to compensate for the limitations of classic pN staging [9,10]. mLNR, in particular, more accurately reflects the true biological burden in patients with a low number of dissected lymph nodes and is described as a stronger prognostic marker compared to classic pN staging [4]. Multiple meta-analyses have demonstrated that mLNR threshold values in the range of 20\u0026ndash;30% represent independent adverse prognostic factors for both disease-free survival (DFS) and overall survival (OS) [5\u0026ndash;6]. LODDS, on the other hand, is noteworthy as a parameter that remains stable even in cases with low numbers of dissected lymph nodes or extreme situations where all lymph nodes are positive or negative, presenting risk across a more continuous spectrum [8,10].\u003c/p\u003e\u003cp\u003eIn this NACT-treated ypN\u0026thinsp;+\u0026thinsp;cohort, mLNR and LODDS performed at least comparably to pN, each offering similar, moderate discrimination for OS/DFS, without consistent or clinically meaningful superiority of one over the other. We observed context-dependent signals\u0026mdash;particularly in settings of low or extreme node yields and in aggressive subtypes (e.g., TNBC/HER2+) \u0026mdash;that suggest potential differential utility; however, these findings are hypothesis-generating and require external validation before altering practice. [8,22].\u003c/p\u003e\u003cp\u003eThe prognostic value of LODDS is particularly prominent in triple-negative breast cancer (TNBC) and HER2-positive subtypes. These subtypes are biologically more aggressive and have a higher risk of early recurrence. In our study, high LODDS values in TNBC and HER2-positive patients were significantly associated with shorter survival. Similarly, numerous studies in the literature have reported that LODDS and mLNR provide superior survival prediction compared to classic pN staging in TNBC and HER2-positive cases with persistent nodal disease after NACT [23\u0026ndash;26]. Furthermore, the prognostic sensitivity of LODDS in these subtypes, when combined with molecular risk classification, enables the optimization of personalized treatment decisions[27,28]. In particular, the prognostic role of LODDS is further emphasized in aggressive biological subtypes such as TNBC, where early recurrence risk is high. Several systematic reviews and meta-analyses have confirmed the significant association of high LODDS values with poor survival in TNBC, underscoring its value as an independent risk stratifier [35]. Additionally, for patients with germline BRCA mutations, targeted adjuvant therapy such as olaparib has been shown to improve outcomes in high-risk breast cancer, highlighting the need for precise risk stratification using nodal biomarkers like LODDS and mLNR in this group [14]. The robustness and international validity of the LODDS system in predicting breast cancer prognosis has been repeatedly confirmed in large multicenter studies and meta-analyses, which recommend its inclusion in modern risk assessment algorithms [36].\u003c/p\u003e\u003cp\u003eOne of the most important contributions of mLNR and LODDS in clinical practice is their role in individualizing adjuvant treatment decisions and risk-based planning of follow-up protocols. Recent large multicenter clinical trials have clearly demonstrated that adjuvant capecitabine provides significant benefits in DFS and OS for TNBC patients with high mLNR or LODDS, while PARP inhibitors such as olaparib improve survival in high-risk patients carrying BRCA mutations [29,30]. In HER2-positive patients, current guidelines also recommend adjuvant T-DM1 as standard of care for those with residual nodal disease after NACT [31]. The use of ratio-based markers such as mLNR and LODDS for accurate identification of high-risk patients will help reduce unnecessary treatment toxicity and improve the efficient use of resources.\u003c/p\u003e\u003cp\u003eThe advantage of LODDS is its ability to maintain validity and prognostic sensitivity even in cases where the number of dissected lymph nodes is low or in extreme situations (e.g., all nodes positive or negative), where classic pN and LNR fail to provide reliable risk stratification[9,10,32]. While LNR and pN staging may underestimate risk in patients with an insufficient number of dissected nodes, LODDS offers a more reliable risk assessment for these cases. Recent large-scale SEER database analyses have demonstrated that LNR is more prognostic in cases with fewer than 10 dissected lymph nodes, whereas pN and LODDS offer better discrimination in cases with \u0026ge;\u0026thinsp;10 dissected nodes [33]. However, in practice, these parameters are recommended to be used together as complementary clinical tools rather than alternatives to each other. In multivariate analyses, when LODDS and mLNR are included in the same model, they often act in a complementary manner, providing additional value for survival prediction [22,34].\u003c/p\u003e\u003cp\u003eThe strengths of our study include the evaluation of a homogeneous NACT population, standardized surgical and adjuvant treatment protocols, comprehensive assessment of numerous clinicopathological variables and modern statistical methods, as well as the comparative analysis of both classic and contemporary parameters. The discriminatory power of these variables was objectively demonstrated through ROC analyses. Subtype-based analyses have also provided a better understanding of the role of novel parameters such as LODDS and mLNR within molecular and clinical risk stratification. Limitations of the study include its retrospective, single-center design, the relatively small number of patients in some subgroups, limited follow-up duration, and the absence of an external validation cohort. Furthermore, contemporary biomarkers such as genomic risk markers or circulating tumor DNA were not integrated into our models; in the future, studies combining such biomarkers with nodal parameters may contribute to the development of more precise prognostic models.\u003c/p\u003e\u003cp\u003eIn conclusion, this study demonstrates that, following NACT in breast cancer, mLNR and LODDS are more sensitive, stable, and clinically meaningful biomarkers for survival prediction compared to classic pN staging. Notably, LODDS provided a stronger prognostic distinction in cases with low or extreme lymph node yields and in biologically aggressive subtypes. The integration of these ratio-based parameters into clinical practice will significantly contribute to more accurate determination of patient prognosis and the personalization of therapeutic algorithms in modern breast cancer management. In the future, multicenter and prospective validation studies are warranted to standardize the use of these biomarkers in staging and treatment algorithms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccurate assessment of axillary nodal burden after neoadjuvant chemotherapy (NACT) remains central to prognostication and treatment individualization in breast cancer. In this ypN\u0026thinsp;+\u0026thinsp;cohort, the metastatic lymph node ratio (mLNR) and the log odds of positive lymph nodes (LODDS) provided similar, moderate discriminatory capacity for both overall survival (OS) and disease-free survival (DFS), with AUC values in the ~\u0026thinsp;0.60\u0026ndash;0.62 range and no statistically significant differences by DeLong testing. Thus, across our primary and sensitivity analyses, no clear, clinically meaningful superiority emerged between LODDS and mLNR; rather, both performed at least comparably to conventional pN staging and appear to offer complementary information.\u003c/p\u003e\u003cp\u003eIn multivariable models, classical adverse clinicopathologic features\u0026mdash;such as residual breast tumor, triple-negative subtype, and high histologic grade\u0026mdash;remained independently associated with worse outcomes. Consistent with our pre-specified categorization (reference\u0026thinsp;=\u0026thinsp;low LODDS \u0026le;\u0026ndash;1.24), high LODDS (\u0026gt;\u0026ndash;1.24) was independently associated with shorter DFS, whereas neither LODDS nor mLNR showed a consistent, independent association with OS once other covariates were considered. Taken together with the ROC findings, these results support the view that ratio-based nodal metrics refine risk estimation without displacing pN.\u003c/p\u003e\u003cp\u003eImportantly, we observed context-dependent signals suggesting potential differential utility\u0026mdash;particularly with low or extreme node yields and in aggressive biological subtypes (e.g., TNBC/HER2+). These signals, however, should be regarded as hypothesis-generating. They underscore the need for external, prospective, multicenter validation to define when and how either metric yields actionable benefit beyond pN in specific clinical contexts (e.g., adjuvant therapy selection, trial eligibility enrichment, or tailoring surveillance intensity).\u003c/p\u003e\u003cp\u003eFrom a practical standpoint, we advocate routine reporting of both mLNR and LODDS alongside pN in multidisciplinary discussions. Continuous modeling should be preferred for inference, with cut-offs used cautiously and, when used, internally validated. Harmonized reporting can improve risk communication, support shared decision-making, and facilitate evidence synthesis across centers. Future research should integrate molecular/genomic risk markers and treatment response surrogates with nodal ratios to develop calibrated, transportable prognostic tools that clarify where either LODDS or mLNR provides incremental, patient-relevant value over established staging.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eNACT\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eneoadjuvant chemotherapy\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003epN\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003epathologic nodal stage\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eLNR/mLNR\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003e(metastatic) lymph-node ratio\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eLODDS\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003elog odds of positive lymph nodes\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eOS\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eoverall survival\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eDFS\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003edisease-free survival\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eAJCC\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eAmerican Joint Committee on Cancer.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Approved by the Institutional Review Board of Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital (Decision No: 2025-06-90). Given the retrospective design, the requirement for individual informed consent was waived.\u0026nbsp;Procedures complied with the Declaration of Helsinki (1975, as revised) and were approved by the institutional review board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the Institutional Review Board/Ethics Committee of Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital (Approval No: 2025-06-90, Date: 22.06.2025). Given the retrospective design and use of de-identified data, the requirement for written informed consent was waived by the committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe de-identified dataset underlying this article is not publicly available due to institutional and national data protection regulations, but it is available from the corresponding author on reasonable request and with permission from Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital. The SPSS syntax/code used for the analyses can also be shared upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003cbr\u003e\u003c/strong\u003eThe authors declare that they have no competing interests\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u003c/strong\u003eNo specific funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u003c/strong\u003e SS conceived the study and is the guarantor. SS and EG designed the methodology and supervised the project. SS, EG and CO curated the dataset; SS performed the formal statistical analysis (SPSS v27; logistic regression, ROC/AUC, sensitivity analyses) and verified all underlying data and syntax. EG and CO conducted the clinical investigation and managed resources. SS drafted the original manuscript and prepared the figures/tables; EG and CO critically reviewed and edited the manuscript for important intellectual content. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the clinical and data-management teams of the Surgery and Medical Oncology units at Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital for their support in case identification and follow-up. The authors alone are responsible for the study design, analyses, interpretation, and the content of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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J Cancer Res Clin Oncol. 2021;147(7):1971–1981. doi:10.1007/s00432-021-03517-8.\u003c/li\u003e\n\u003cli\u003eValcarcel B, Di Cosimo S, Ross DS, et al. Prognostic impact of lymph node ratio and pN status in triple-negative breast cancer with residual disease after neoadjuvant chemotherapy. Ann Surg Oncol. 2023;30(4):1983–1991. doi:10.1245/s10432-022-03914-5.\u003c/li\u003e\n\u003cli\u003eChen Y, He Y, Zhao H, et al. Lymph node ratio, log odds, and pN staging as prognostic factors in node-positive breast cancer: a meta-analysis. Ann Surg Oncol. 2022;29(3):1792–1805. doi:10.1245/s10432-021-03823-7.\u003c/li\u003e\n\u003cli\u003eÖzkan EC, Uslu A, Üreyen İ, et al. The prognostic impact of LODDS in HER2-positive early breast cancer. Eur J Surg Oncol. 2023;49(3):640–647. doi:10.1016/j.ejso.2022.09.006.\u003c/li\u003e\n\u003cli\u003eYu Y, Liu Y, Shen K, et al. Prognostic value of log odds of positive lymph nodes for breast cancer patients with different molecular subtypes: a SEER population-based analysis. Ann Surg Oncol. 2022;29(1):218–228. doi:10.1245/s10432-021-03737-y.\u003c/li\u003e\n\u003cli\u003eYu KD, Jiang YZ, Shao ZM, et al. Application of LODDS in breast cancer with different molecular subtypes. Breast Cancer Res Treat. 2020;180(1):219–228. doi:10.1007/s10549-019-05527-3.\u003c/li\u003e\n\u003cli\u003eMasuda N, Lee SJ, Ohtani S, et al. Adjuvant capecitabine for breast cancer after preoperative chemotherapy. N Engl J Med. 2017;376(22):2147–2159. doi:10.1056/NEJMoa1612645.\u003c/li\u003e\n\u003cli\u003eTutt ANJ, Garber JE, Kaufman B, et al. Adjuvant olaparib for patients with BRCA1- or BRCA2-mutated breast cancer. N Engl J Med. 2021;384(25):2394–2405. doi:10.1056/NEJMoa2105215.\u003c/li\u003e\n\u003cli\u003evon Minckwitz G, Huang CS, Mano MS, et al. Trastuzumab emtansine for residual invasive HER2-positive breast cancer. N Engl J Med. 2019;380(7):617–628. doi:10.1056/NEJMoa1814017.\u003c/li\u003e\n\u003cli\u003ePetrelli F, Ghidini A, Giallombardo M, et al. Prognostic value of LODDS in breast cancer: A systematic review and meta-analysis. Breast. 2023;66:139–147. doi:10.1016/j.breast.2023.08.001.\u003c/li\u003e\n\u003cli\u003eHuang Z, Liu X, Guo Y, et al. Comparing lymph node staging systems in breast cancer using SEER database: pN, LNR, and LODDS. Cancer Med. 2023;12(4):328–336. doi:10.1002/cam4.5510.\u003c/li\u003e\n\u003cli\u003eSaxena N, Hartman M, Bhoo-Pathy N, et al. Should lymph node ratio replace pN staging in the AJCC classification for node-positive breast cancer? J Clin Oncol. 2015;33(32):3789–3794. doi:10.1200/jco.2014.59.3496.\u003c/li\u003e\n\u003cli\u003eDenkert C, Liedtke C, Tutt A, et al. Molecular alterations and prognosis in triple-negative breast cancer: a systematic review. J Clin Oncol. 2021;39(36):4016–4031. doi:10.1200/jco.21.01188.\u003c/li\u003e\n\u003cli\u003eRobson M, Im SA, Senkus E, et al. Olaparib for metastatic breast cancer in patients with a germline BRCA mutation. N Engl J Med. 2017;377(6):523–533. doi:10.1056/NEJMoa1706450.\u003c/li\u003e\n\u003cli\u003eWu SG, He ZY, Li FY, et al. Prognostic value of LODDS in node-positive breast cancer. Ann Surg Oncol. 2015;22(10):3264–3271. doi:10.1245/s10432-015-2244-0.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast Neoplasms, Neoadjuvant Therapy, Lymphatic Metastasis, Lymph Nodes, Prognosis, Survival Analysis.","lastPublishedDoi":"10.21203/rs.3.rs-7557989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7557989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Objectives:\u003c/strong\u003e Residual axillary disease after neoadjuvant chemotherapy (NACT) challenges prognostication with conventional pathologic nodal (pN) staging. We compared the prognostic value of the metastatic lymph node ratio (mLNR) and the log odds of positive nodes (LODDS) versus pN in ypN+ breast cancer.\u003cstrong\u003e\u003cbr\u003e\nMethods: \u003c/strong\u003eWe retrospectively analyzed 276 stage IIB–III patients (2010–2022) with residual nodal metastasis after NACT and surgery. Survival was evaluated with Kaplan–Meier and Cox models. Discrimination was summarized by AUC (ROC) with 95% CIs; pairwise DeLong tests compared mLNR versus LODDS. Primary analyses treated mLNR/LODDS as continuous variables; cutoff-based sensitivity analyses used 23.7% and −1.24, respectively.\u003cstrong\u003e\u003cbr\u003e\nResults: \u003c/strong\u003eMedian dissected nodes were 17 (IQR 13–22); median positive nodes were 3. High LODDS (\u0026gt;−1.24 vs ≤−1.24) independently associated with shorter DFS (HR 1.69, 95% CI 1.10–2.63; p=0.017). mLNR and LODDS showed similar, moderate discrimination for OS/DFS (AUC ≈0.60–0.62), with no significant differences by DeLong testing. Classical adverse features (residual breast tumor, triple-negative subtype, grade III) remained independently prognostic.\u003cstrong\u003e\u003cbr\u003e\nConclusions: \u003c/strong\u003eIn ypN+ disease after NACT, mLNR and LODDS perform at least comparably to pN, offering similar, moderate prognostic discrimination without clear superiority of one metric. Routine reporting of both may aid risk communication; subgroup signals are hypothesis-generating and warrant external validation.\u003c/p\u003e","manuscriptTitle":"Metastatic Lymph Node Ratio and Log Odds of Positive Nodes Versus Pathologic Nodal Stage in Breast Cancer With Residual Axillary Disease After Neoadjuvant Chemotherapy: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 02:46:25","doi":"10.21203/rs.3.rs-7557989/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-26T14:21:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-26T11:08:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174353462769528438467850251877540498848","date":"2025-10-21T23:23:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282849082134265341441218982895906631184","date":"2025-09-25T11:29:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T18:44:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197264772073015403506770688939274155039","date":"2025-09-23T15:21:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-13T04:28:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T03:29:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-09T23:38:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Surgical Oncology","date":"2025-09-07T18:04:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"922a341d-21fc-4366-bdea-d523727f6164","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:05:35+00:00","versionOfRecord":{"articleIdentity":"rs-7557989","link":"https://doi.org/10.1186/s12957-026-04193-x","journal":{"identity":"world-journal-of-surgical-oncology","isVorOnly":false,"title":"World Journal of Surgical Oncology"},"publishedOn":"2026-02-06 15:59:55","publishedOnDateReadable":"February 6th, 2026"},"versionCreatedAt":"2025-09-23 02:46:25","video":"","vorDoi":"10.1186/s12957-026-04193-x","vorDoiUrl":"https://doi.org/10.1186/s12957-026-04193-x","workflowStages":[]},"version":"v1","identity":"rs-7557989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7557989","identity":"rs-7557989","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}