Prognostic Factors and Survival in Elderly Breast Cancer Patients: Roles of Age, Stage and Inflammatory Markers

Prognostic Factors and Survival in Elderly Breast Cancer Patients: Roles of Age, Stage and Inflammatory Markers | 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 Prognostic Factors and Survival in Elderly Breast Cancer Patients: Roles of Age, Stage and Inflammatory Markers Dogancan Akpalamut, Halil İbrahim Ellez, Elif ATAĞ, Oktay Halit Aktepe, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8909658/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Introduction: In elderly individuals, breast cancer is a major clinical problem, and sufficient data regarding treatment in this population are lacking. This study aimed to investigate the clinical and pathological characteristics and overall survival of elderly patients with breast cancer, as well as the factors influencing survival outcomes . Methods: This retrospective study included 261 patients aged 65 years or older who were diagnosed with breast cancer and treated at the Medical Oncology Department of Dokuz Eylül University Faculty of Medicine between 2010 and 2023. The clinical and pathological characteristics and treatment modalities of the included patients were analyzed, as were their biochemical results, which included the NLR, LDH and NLI. The patients were stratified into three age groups (65–74, 75–84, and ≥85 years), and intergroup differences were assessed. Survival analyses were conducted via Kaplan‒Meier and log-rank tests, and multivariate analyses were performed according to the Cox regression model. The diagnostic features of the biochemical variables were verified via receiver operating characteristic (ROC) analyses. Results: A total of 261 patients were included in the study, with a median age at diagnosis of 73.5 years (range: 65–88). The median survival time in the 65–74 year age group (n=145) was 148 months (95% CI, 123.71–172.28), that in the 75–84 year age group was 91 months (95% CI, 71.10– 110.89), and that in the 85+ year age group was 58 months (95% CI, 25.73–90.26) (p<0.05). Patients aged 85 years and older were less likely to undergo surgery and receive adjuvant chemotherapy (p<0.05). No significant differences were observed among the age groups regarding pathological features or the rate of neoadjuvant therapy. However, older patients are more likely to be diagnosed at advanced stages than their younger counterparts are (p<0.05). Univariate survival analysis revealed that advanced disease stage at diagnosis and a high dNLR and estrogen receptor status have prognostic importance; however, tumor grade, progesterone receptor status and whether adjuvant or neoadjuvant therapy is administered do not significantly affect overall survival. ROC analysis revealed that the LDH value was insignificant; thus, the LDH and NLI (LDH combined with the dNLR) indices were excluded from the survival analysis. Conclusion: Although clinicopathological features were similar across age groups, elderly patients—particularly those over 85—received less aggressive curative treatment. Advanced stage, age, and elevated inflammatory markers (dNLRs) are independent predictors of mortality. These findings suggest that treatment decisions should be personalized through geriatric assessment to avoid undertreatment, as this population can achieve substantial survival. geriatric patients breast cancer prognosis Figures Figure 1 Figure 2 1. Introduction Breast cancer is one of the most common female cancers and the leading cause of cancer-related death worldwide [ 1 ]. Over one-third of invasive cancers are reported among those older than 70 years [ 2 ]. Moreover, the percentage of those who are diagnosed with breast cancer in the elderly age group is expected to increase. Limited data are available for those patients because clinical trial data that predominantly involve younger patients do not provide sufficient information to accurately assess the outcomes of therapy in older adults [ 3 ]. Early diagnosis of breast cancer and timely, effective implementation of treatment strategies are crucial for reducing breast cancer-related mortality. Several factors, including patient age, life expectancy, comorbidities, disease stage and molecular markers, play a significant role in determining the most appropriate treatment approach [ 4 ]. In addition to hormone status, stage and age, inflammatory markers such as the dNLR (derived neutrophil–to-lymphocyte ratio) have prognostic value in these patients. In one study, high dNLRs and LDH levels were related to worse outcomes in HER2-positive breast cancer patients.[ 5 ]. Moreover, the dNLR can reflect the immune state of a disease, and its prognostic importance has been shown in several studies [ 6 , 7 ]. Other inflammatory markers, such as lactate dehydrogenase (LDH), can have prognostic importance and can be used to evaluate treatment efficacy [ 8 , 9 ]. Despite these insights, factors influencing overall survival in elderly patients with breast cancer remain poorly defined, particularly regarding the role of serum biomarkers in guiding treatment decisions and surveillance strategies. This study aimed to investigate the clinical characteristics and determinants of overall survival in elderly patients with breast cancer. Given that comorbid conditions in this population may substantially impact survival, we also sought to examine nonmalignant factors that contribute to mortality. 2. Methods Patient Selection This retrospective study analyzed geriatric patients diagnosed with breast cancer between 2010 and 2023 at Dokuz Eylul University Medical Oncology Clinic. Those who were diagnosed before the age of 65 years and those who discontinued treatment or follow-up were excluded. Demographic, clinical, and pathological characteristics as well as treatment details were collected and recorded in accordance with the ethical approval obtained from the Dokuz Eylul University Ethics Committee. Clinical characteristics and data collection Patients’ ages and stages at the time of diagnosis, hormonal status, and grades were collected. Data on surgical procedures, neoadjuvant and adjuvant chemotherapies, hormonal therapies, and anti-HER2 treatments were documented. Recurrence, metastatic status, and first-, second-, and third-line treatment regimens were also recorded. The primary endpoint of the study was overall survival (OS), defined as the time from diagnosis to death or last follow-up. Patients were stratified into three age groups: Group 1 (65–74 years), Group 2 (75–84 years) and Group 3 (≥ 85 years). Blood parameters LDH levels and complete blood counts were recorded before treatment initiation. The derived neutrophil-to-lymphocyte ratio (dNLR) was calculated as neutrophil/(leukocytes – neutrophils). The NLI score was defined as an LDH level exceeding the upper normal limit combined with the dNLR score, and patients were classified into three risk groups (0: low risk, 1: intermediate risk, 2: high risk). Mortality analysis was conducted on the basis of these hematological parameters. The optimal cutoff values for LDH and the dNLR were determined via receiver operating characteristic (ROC) curve analysis. Statistical analysis Statistical analyses were performed via SPSS version 25.0. Descriptive statistics were used to calculate the medians and means of continuous variables. Comparisons between age groups were conducted via the log-rank test. Univariate survival analysis was performed via the Kaplan‒Meier method in R studio [ version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria], whereas multivariate analysis and hazard ratio (HR) estimations with 95% confidence intervals (CIs) were conducted via the Cox proportional hazards regression model in SPSS, in which statistically significant dependent variables were included. A p value < 0.05 was considered statistically significant for all analyses. 3. Results 3.1. Characteristics of the Age Groups A total of 261 patients were included in this study, and the median follow-up was 142 months (95% CI [130,3- 153,3]). There were 145 patients in the 65–74 years age group, 99 patients in the 75–84 years age group, and 17 patients in the 85 years and above age group. Among patients aged 65–74 years, 4 (2.75%) were male, whereas 2 (2%) of those aged 75–84 years were male. There were no male patients aged 85 years or older. The clinical and pathological characteristics of the age groups are summarized in Table 1 . Table 1 Pathological and Clinical Characteristics of the Age Groups. Variables 65–74 (n:145) Group 1 (n, %) 75–84 (n:99) Group 2 (n, %) > 85 (n:17) Group3 (n, %) p value Diabetes Mellitus Hypertension Coronary Artery Disease Respiratory Disease CKD Cognitive Dysfunction Musculoskeletal Diseases 52(35.86) 85(58.6) 48(33.1) 25(17.2) 20(13.7) 11(7.5) 56(38.6) 27(27.2) 71(71.7) 33(33.3) 18(18.1) 17(17.1) 14(14.1) 49(49.4) 5(29.4) 11(64.7) 7(41.1) 1(5.8) 2(11.7) 3(17.6) 9(52.9) > 0.05 Invazive Ductal Carcinoma Invazive Lobulary Carcinoma Other Type of Carcinoma 81 (55.8) 28(19.3) 36 (24.7) 61(61.6) 24(24.2) 14(14.1) 9(52.9) 4(23.5) 4(23.5) 0.901 Grade 1 Grade 2 Grade 3 Unknown 13 (8.9) 65 (44.8) 48 (33.1) 19 (13.1) 13 (13.1) 44 (44.4) 36 (36.3) 6 (6.0) 1 (5.8) 7 (41.1) 7 (41.1) 2 (11.7) 0.593 ER Pozitive ER Negative 118 (81.3) 27 (18.6) 81 (81.8) 18 (18.1) 16 (94.1) 1 (5.8) 0.272 PR Pozitive PR Negative 98 (67.5) 47 (32.4) 72 (72.7) 27 (27.2) 12 (70.5) 5 (29.4) 0.323 HER2 Pozitive HER2 Negative 23 (15.8) 122 (84.1) 8(8.0) 91 (91.9) 3(17.6) 14 (82.3) 0.175 Hormone Positive/HER2 Negative HER2 Positive Triple Negative Hormone/HER2 positive 106(73.1) 15(10.3) 16(11.0) 8(5.5) 79(79.7) 3(3.0) 12(12.1) 5(5.0) 12(70.5) 1(5.8) 2(11.7) 2(11.7) 0.422 Stage 1 Stage 2 Stage 3 Stage 4 Unknown 31 (21.3) 46 (31.7) 43(29.6) 18(12.4) 7(4.8) 16(16.1) 29(29.2) 29(29.2) 19(19.1) 6(6.0) 2(11.7) 1(5.8) 7(41.1) 3(17.6) 4(23.5) 0.049 dNLR > 1.74 90(62.0) 52(52.52) 10(58.8) 0.622 LDH 259 Unknown 122 (84.1) 17 (11.7) 6 (4.1) 84 (84.8) 12 (12.1) 3 (3) 16 (94.1) 1 (5.8) 0 (0) 0.823 NLI Good Intermadiate Poor Unknown 77 (53.1) 54 (37.2) 8 (5.5) 6 (4.1) 48 (48.4) 39 (39.3) 9 (9) 3 (3) 9 (52.9) 8 (47) 0 (0) 0 (0) 0.7 CKD: chronic kidney disease, dNLR: derived neutrophil–lymphocyte ratio, ER: estrogen receptor, HER: human epidermal receptor, LDH: lactate dehydrogenase, NLI: LDH combined with the dNLR index (**only those who have comorbidities are included in this table). We demonstrated that older patients were more likely to be diagnosed at advanced stages of the disease (p = 0.049). However, no statistically significant differences were observed among the age groups regarding other clinicopathological characteristics or comorbidities. Among patients with nonmetastatic disease, surgical intervention was significantly less common in those aged over 85 years when treatment modalities were compared across age groups (p < 0.01). Additionally, the use of adjuvant radiotherapy and chemotherapy declined progressively with increasing age (p < 0.01). These comparisons are summarized in Tables 2 and 3 . Table 2 Proportions of surgical interventions for patients with surgery indications. Surgery Group 1 n: 127 Group 2 n: 80 Group 3 n: 14 p value BCS (n, %) TM (n, %) Not Operated (n, %) Not Defined Surgery (n, %) 87 (60.68) 36 (26.2) 1 (11.03) 3 (2.06) 31 (36.36) 38 (39.39) 6 (19.19) 5 (5.05) 6 (35.29) 3 (17.64) 5 (47.05) 0 < 0,01 BCS: Breast Conserving Surgery; TM: Total Mastectomy Table 3 Comparison of adjuvant/neoadjuvant chemotherapy Parameter Group 1 n = 126 Group2 n = 74 Group3 n = 9 p value Neoadjuvant Therapy 22 (17.4) 11(14.8) 0(0) 0.21 Adjuvant Therapy 69 (54.7) 24(32.4) 2(22.2) < 0.01 Adjuvant Hormonotherapy Adjuvant RT 101(80.1) 117(92.8) 65(87.8) 56(75.6) 8(88.8) 6(66.66) 0.87 < 0.01 RT: Radiotherapy Table 4 ROC analysis of inflammatory markers for mortality analysis. 3.2 ROC analysis Variables AUC Cut-of Value Sensitivity Spesifity p LDH 0.545 259 0.181 0.974 0.212 dNLR 0.625 1.74 0.534 0.748 < 0.05 LDH: lactate dehydrogenase, dNLR: derived lymphocyte–neutrophil ratio For the inflammatory biomarkers, the dNLR and LDH values were analyzed. With a cutoff value of 1.74 for the dNLR, there was 53% sensitivity and 74% specificity (AUC = 0.625, p < 0.05). With the 259 mg/dl cutoff value of the LDH value, 18% sensitivity and 97% specificity were detected (AUC = 0.545, p = 0.212) (Fig. 1) (Table 4 ). Owing to a lack of significance, LDH values, as well as NLI values, were not used in survival analysis. 3.3. Overall survival The median survival time was 211 months (95% CI, 175–246) for stage 1 patients, 142 months (95% CI, 110–173) for stage 2 patients, 106 months (95% CI, 77–134) for stage 3 patients and 36 months (95% CI, 17–54) for stage 4 patients. A significant trend toward shorter survival durations was observed with increasing disease stage (p < 0.001) (Fig. 2 A). ER-positive patients demonstrated a significantly longer median OS of 126 months (95% CI: 102–149) than did ER-negative patients (95% CI: 34–111; p 0.05) (Fig. 2 B, 2 D). The median OS was 132 months (95% CI: 115–161) for hormone-positive/HER2-negative patients, 91 months (95% CI: 70–NR) for HER2-positive/HR-negative patients, 45 months (95% CI: 33–NR) for triple-negative patients, and 69 months (95% CI: 57–NR) for HR+/HER2 + patients (Figure F). Overall survival varied significantly across clinical subtypes (p < 0.001), with the luminal A/B subgroup exhibiting superior survival outcomes (p < 0.05). Owing to an insufficient number of events during the follow-up period, the upper limit of the 95% confidence interval could not be estimated for certain subgroups. In the survival analysis stratified by age group, the median overall survival was 148 months (95% CI, 123.7–172.3) in group 1, whereas it was 91 months (95% CI, 71.1–110.9) and 58 months (95% CI, 25.7–90.3) in groups 2 and 3, respectively. Overall survival differed significantly among the age groups (p < 0.05) (Fig. 2 A). Eighty-four patients had de novo metastatic disease or experienced recurrence/metastasis, 75 of whom (89.3%) received first-line treatment. Among those who received first-line treatment, 51 patients (68%) also received second- and third-line treatment. Patients who received first-line treatment had longer survival than did those who did not, with median survival times of 58 months (95% CI, 45.75–70.24) and 30 months (95% CI, 18.31–41.68), respectively (p < 0.05) (Fig. 2 G). The dNLR marker was also included in the survival analysis, and the median survival was 166 months (95% CI, 135.95– 196.04) for patients with a dNLR 1.74 (p < 0.05) (Fig. 2 C). In our study, ER negativity, advanced age at diagnosis, advanced stage, clinical subtype and higher dNLR values were associated with a worse prognosis, and in multivariate regression analysis, stage at diagnosis, age group and dNLR value were found to be independent risk factors for mortality (Table 5 ). Table 5: Univariate and multivariate analyses of survival Characteristics Univariate Multivariate OS m (95% CI) p HR (95% CI) p Age Groups 65- 74 75- 84 85 and above 148 (123-172) p< 0.05 91 (71- 110) 58 (25- 90) 1 p< 0.05 1.4 (1.03- 2.15) 3.9 (2.12- 7.33) Stage Stage 1 Stage 2 Stage 3 Stage 4 211 (175- 246) p< 0.05 142 (110- 173) 106 (77- 134) 36 (17- 54) 1 p< 0.05 3,8 (1.8- 8.3) 5.3 (2.5- 11.4) 13.9 (6.3- 30.9) Estrogen Receptor Status Pozitif Negatif 126 (102- 149) p< 0.05 73 (34- 111) Clinical Subtype Luminal A/B HER 2 positive Triple Negative Hormon/HER2 positive 132 (115- 161) p< 0.05 91 (70- NR) 45 (33- NR) 69 (57- NR) 1 p< 0.05 2.3 (1.6- 3.2) dNLR 1.74 166 (135- 196) p< 0.05 73 (58- 87) CI: confidence interval; dLR: derived neutrophil‒lymphocyte ratio; HR: hazard ratio; OS: overall survival 4. Discussion In our study, advanced disease stage, older age at diagnosis, and a higher dNLR were identified as independent risk factors for mortality. We also observed that with increasing age, fewer patients were offered curative treatment modalities, including chemotherapy and radiotherapy. In addition, elderly patients in our cohort were more frequently diagnosed at advanced stages of disease, and DNLR, an inflammatory marker, emerged as a prognostic factor in the geriatric population. Forty-nine (18%) patients were diagnosed with stage 1 disease, and 76 (29%) and 79 (30%) patients were diagnosed with stage 2 and 3 disease, respectively. Moreover, 15% of the patients had stage 4 disease. In the literature, Glas et al. [ 10 ] reported that metastatic disease at presentation was observed in 8% of patients aged over 85 years, whereas it was observed in 4% of those younger than 85 years, indicating a significantly greater prevalence of advanced-stage disease among the oldest patients (p < 0.05). Furthermore, this age group was notably less likely to receive intensive treatments such as chemotherapy and radiotherapy (p < 0.05). These observations are consistent with other studies; for example, patients aged 80 years or older have been shown to present with more advanced disease stages than those in the 70–79 years age bracket (8% vs. 5.9%, p < 0.05) [ 11 ]. Collectively, these findings suggest that barriers to healthcare access, the presence of multiple comorbidities, or perhaps more fatalistic attitudes toward illnesses in later life may collectively contribute to delayed diagnosis and undertreatment in the geriatric population. In terms of other disease features, there were no significant correlations among the age groups in terms of tumor type, receptor status or inflammatory markers. However, with respect to treatment modalities, patients aged 85 years and above were less likely to have primary tumor and axillary surgery (p < 0.005). In a study including 127,805 participants, Bastiannet et al. [ 12 ]. showed that elderly patients underwent less surgery and received less adjuvant systemic therapy; however, they received more adjuvant hormonal monotherapy. We found that patients aged 85 years and older were less likely to receive chemotherapy and radiotherapy than were those in groups 1 and 2. In addition, surgical intervention was performed in 52.9% of patients in group 3, whereas it was performed in 88.9% and 80.8% of patients in groups 1 and 2, respectively (p < 0.05). With respect to hormonotherapy, elderly patients in our cohort were more commonly treated with hormonotherapy than with chemotherapy, with 88% of patients in group 3 receiving hormonotherapy. This trend might be attributed to a clinical preference for hormonotherapy in older populations, owing to its more favorable tolerability profile. In our study, we observed a steady decline in the administration of adjuvant chemotherapy and radiotherapy as age increased (p < 0.05). While no significant correlation was found across age groups regarding neoadjuvant therapy, surgical intervention rates significantly decreased in the oldest group. These findings are consistent with broader literature; for example, studies by Bastiaannet et al. [ 12 ] and de Glas et al. [ 10 ] similarly reported that geriatric patients receive substantially less surgery and adjuvant systemic therapy than their younger counterparts do. Furthermore, Liu et al. [ 13 ] confirmed this trend of declining treatment intensity with age but also reported (consistent with our results) no significant age-related disparity in neoadjuvant chemotherapy administration (p > 0.05). Collectively, our results align with the well-documented global trend of reduced treatment aggressiveness in the elderly population. The lower rates of treatment administration among geriatric patients may be attributed to factors such as shorter life expectancy, higher prevalence of comorbidities, concerns about treatment-related toxicity, and patient or physician preferences. We performed ROC analysis to determine the significance of the dNLR and LDH cutoff values for survival analysis. In the literature, dNLR values can be used as a prognostic marker in localized breast cancer patients treated with adjuvant chemotherapy [ 14 ]. Geng et al. used a cutoff NLR of 1.878 to determine survival outcomes (95% CI, 0.663–0.744; p < 0.001), and high NLR values were associated with mortality in breast cancer patients who underwent curative resection [ 15 ]. Krenn- Pilko et al also reported that a derived neutrophil‒lymphocyte ratio and a dNLR ≥ 3 were associated with poor DFS (hazard ratio (HR) 1.87, 95% CI, 1.28–2.73, p = 0.001) and OS (HR 1.67, 95% CI, 1.07–2.63, p = 0.025) [ 16 ]. In addition, a review of the literature revealed that high NLR values are independent risk factors for mortality in breast cancer patients [ 17 ]. This result is consistent with existing evidence in this area. The prognostic value of these parameters has not been previously established in geriatric populations, and external validation is warranted. Nonetheless, inflammatory markers remain prognostically informative in elderly patients despite age-related immune alterations. In our study, high LDH levels did not provide prognostic clues, as the ROC analysis was insignificant (p > 0.05); thus, LDH values and the NLI ratio were excluded from further analysis. However, a high dNLR was associated with poor survival according to univariate and multivariate analyses. Li et al. (5) reported that the optimal cutoff value for LDH was 244 U/L (p = 0.003), and the corresponding AUC for LDH was 0.793 in breast cancer patients prior to trastuzumab emtansine therapy. Overall, despite existing evidence supporting LDH as a prognostic indicator, our data failed to demonstrate a similar association, suggesting that further prospective studies are needed to clarify its prognostic value. Our study included relatively few metastatic patients, which may have resulted in the insignificant prognostic importance of LDH. In the univariate analysis, age at diagnosis, disease stage, estrogen receptor negativity, clinical subtype, and higher dNLR values were identified as significant prognostic factors. We demonstrated similar prognostic features in clinical subtypes between geriatric and nongeriatric patients; thus, geriatric patients should receive the same therapy as their younger counterparts. While the relationship between increasing age and survival is well recognized, we suggest that older patients are less likely to receive treatment. This may explain the lower survival rates observed in this population. Multivariate analysis further confirmed that advanced disease stage, older age and elevated dNLR were independent predictors of mortality. Specifically, patients diagnosed with stage IV disease and those aged 85 years or older presented significantly greater mortality risks (HR = 13.9, 95% CI: 6.3–30.9, p < 0.05; HR = 3.9, 95% CI: 2.12–7.33, p < 0.05, respectively). Moreover, a dNLR greater than 1.74 was independently associated with shorter overall survival (HR = 2.3, 95% CI: 1.6–3.2, p < 0.05). Liu et al. (13) reported that higher clinical stage, age at diagnosis and Ki-67 levels were independent risk factors for mortality. While advanced age is known to be strongly associated with mortality, the limited treatment options provided to this population may have had an additional detrimental effect on survival outcomes. One of the main strengths of this study is the relatively long median follow-up period, which allowed a more reliable evaluation of survival outcomes. In addition, patients were analyzed according to age groups, enabling a clearer assessment of age-related differences. Another important strength is that all patients were treated at the same center and managed with a similar clinical approach, which helped minimize variability in treatment strategies. Furthermore, this study provides a comprehensive evaluation by incorporating clinical characteristics, treatment patterns, inflammatory markers, and survival data. Notably, data on the prognostic relevance of inflammatory markers in geriatric patients remain limited in the literature, and our findings contribute to this underexplored area. Nevertheless, several limitations should be acknowledged. This study was retrospective in nature and was conducted at a single center, which may limit the generalizability of the results. In addition, treatment indications were not evaluated in detail, restricting a fully adjusted assessment of survival outcomes. Moreover, given the impact of frailty and functional status on treatment decisions and survival in older adults with cancer, their omission in our study may have led to residual confounding. Finally, although the cohort included 261 patients, the sample size may still be insufficient for more detailed subgroup analyses. 5. Conclusions In conclusion, our study demonstrated that the management of elderly breast cancer patients involves significant disparities compared with that of younger patients. We found that older patients are more likely to be diagnosed at advanced stages and are less frequently treated with surgery and adjuvant modalities, including chemotherapy and radiotherapy. A key finding of our research is that elevated dNLR serves as a significant independent risk factor for mortality, highlighting its potential utility as a practical and accessible prognostic biomarker in geriatric patients. While adjuvant treatment rates declined with age, neoadjuvant chemotherapy administration remained similar across age groups, suggesting a more balanced approach in preoperative settings. Ultimately, given that geriatric patients can achieve prolonged survival, our findings emphasize that treatment decisions should not be based on chronological age alone. Instead, a comprehensive geriatric assessment is essential to optimize personalized therapy and ensure that eligible patients are not undertreated. Abbreviations CI Confidence interval dNLR Derived Neutrophil/Lymphocyte ratio ER Estrogen receptor HER2 Human epidermal growth factor receptor 2 a HR Hazard ratio IHC immunohistochemistry LDH Lactate dehydrogenase NLI Neutrophil/lymphocyte index NLR Neutrophil/lymphocyte ratio NR Not Reached PR Progesterone Receptor ROC Receiver operating characteristic Declarations Ethics approval and consent to participate: This study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of Dokuz Eylül University (Approval No: 2023/13-13, 26 April 2023). Due to the retrospective design of the study, the requirement for informed consent was waived by the Ethics Committee. Consent for publication Not applicable. Data availability statement: The data used in this study are not publicly available because they contain confidential patient information. However, the datasets can be shared by the corresponding author upon reasonable request, provided that approval is obtained from the institutional ethics committee. Funding: This research received no external funding. Author Contributions: D.A., H.İ.E. and E.A.; methodology D.A., E.A.; software, D.A.; validation, D.A.; formal analysis, D.A., H.İ.E.; investigation, D.A., E.A.; resources, E.A.; data curation, D.A., H.İ.E.; writing, original draft preparation, D.A.; writing, review and editing, H.S.S.; data curation, editing, O.H.A.; editing, A.K.; supervision. E.A.; visualization, E.A., D.A.; project administration. All the authors have read and agreed to the published version of the manuscript. Competing interests The authors declare that they have no competing interests. Acknowledgments No acknowledgments exist. References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. 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High score of LDH plus dNLR predicts poor survival in patients with HER2-positive advanced breast cancer treated with trastuzumab emtansine. BMC Cancer. 2022 Jan 3;22(1):29. doi: 10.1186/s12885-021-09131-6. PMID: 34980025; PMCID: PMC8722106. Pelizzari G, Gerratana L, et al. A risk score integrating lymphocytes ratios (LRs) and lactate dehydrogenase (LDH) levels to predict prognosis in metastatic breast cancer (MBC) patients. Ann Oncol. 2018;29 Suppl 8: viii96 Hong H, Fang X, Huang H, Wang Z, Lin T, Yao H. The derived neutrophil-to-lymphocyte ratio is an independent prognostic factor in patients with angioimmunoblastic T-cell lymphoma. Br J Hematol. 2020 Jun;189(5):908-912. doi: 10.1111/bjh.16447. Epub 2020 Feb 27. PMID: 32103494. Petrelli F, Cabiddu M, Coinu A, Borgonovo K, Ghilardi M, Lonati V, Barni S. Prognostic role of lactate dehydrogenase in solid tumors: a systematic review and meta-analysis of 76 studies. Acta Oncol. 2015 Jul;54(7):961-70. doi: 10.3109/0284186X.2015.1043026. 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Breast Cancer Res Treat. 2017 Dec;166(3):657-668. doi: 10.1007/s10549-017-4448-5. Epub 2017 Aug 12. PMID: 28803352. Bastiaannet E, Liefers GJ, de Craen AJ, Kuppen PJ, van de Water W, Portielje JE, van der Geest LG, Janssen-Heijnen ML, Dekkers OM, van de Velde CJ, Westendorp RG. Breast cancer in elderly compared to younger patients in the Netherlands: stage at diagnosis, treatment and survival in 127,805 unselected patients. Breast Cancer Res Treat. 2010 Dec;124(3):801-7. doi: 10.1007/s10549-010-0898-8. Epub 2010 Apr 29. PMID: 20428937. Liu X, Zheng D, Wu Y, Luo C, Fan Y, Zhong X, Zheng H. Treatment patterns and outcomes in older women with early breast cancer: a population-based cohort study in China. BMC Cancer. 2021 Mar 5;21(1):226. doi: 10.1186/s12885-021-07947-w. PMID: 33673816; PMCID Corbeau I, Jacot W, Guiu S. Neutrophil to Lymphocyte Ratio as Prognostic and Predictive Factor in Breast Cancer Patients: A Systematic Review. Cancers (Basel). 2020 Apr 13;12(4):958. doi: 10.3390/cancers12040958. PMID: 32295078. Geng SK, Fu SM, Fu YP, Zhang HW. Neutrophil to lymphocyte ratio is a prognostic factor for disease free survival in patients with breast cancer underwent curative resection. Medicine (Baltimore). 2018 Aug;97(35):e11898. doi: 10.1097/MD.0000000000011898. PMID: 30170382. Krenn-Pilko S, Langsenlehner U, Stojakovic T, Pichler M, Gerger A, Kapp KS, Langsenlehner T. The elevated preoperative derived neutrophil-to-lymphocyte ratio predicts poor clinical outcome in breast cancer patients. Tumor Biol. 2016 Jan;37(1):361-8. doi: 10.1007/s13277-015-3805-4. Epub 2015 Jul 29. PMID: 26219894. Corbeau I, Jacot W, Guiu S. Neutrophil to Lymphocyte Ratio as Prognostic and Predictive Factor in Breast Cancer Patients: A Systematic Review. Cancers (Basel). 2020 Apr 13;12(4):958. doi: 10.3390/cancers12040958. PMID: 32295078; PMCID: PMC7226461. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Editor invited by journal 19 Feb, 2026 Submission checks completed at journal 19 Feb, 2026 First submitted to journal 19 Feb, 2026 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8909658","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608200268,"identity":"6998c14d-8f64-4589-9a1c-f9724d6c795b","order_by":0,"name":"Dogancan Akpalamut","email":"data:image/png;base64,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","orcid":"","institution":"Buca Seyfi Demirsoy Research Hospital","correspondingAuthor":true,"prefix":"","firstName":"Dogancan","middleName":"","lastName":"Akpalamut","suffix":""},{"id":608200269,"identity":"76f927aa-8fb2-4879-a68d-a1e9162a671b","order_by":1,"name":"Halil İbrahim Ellez","email":"","orcid":"","institution":"Sanlıurfa Mehmet Akif Inan Research and Training Hospital","correspondingAuthor":false,"prefix":"","firstName":"Halil","middleName":"İbrahim","lastName":"Ellez","suffix":""},{"id":608200270,"identity":"df5fff01-260c-4c88-8e46-b9b4ea56b56d","order_by":2,"name":"Elif ATAĞ","email":"","orcid":"","institution":"Dokuz Eylul University","correspondingAuthor":false,"prefix":"","firstName":"Elif","middleName":"","lastName":"ATAĞ","suffix":""},{"id":608200271,"identity":"f4eaef72-0d5f-4765-930a-c5fb5802a4ab","order_by":3,"name":"Oktay Halit Aktepe","email":"","orcid":"","institution":"Dokuz Eylul University","correspondingAuthor":false,"prefix":"","firstName":"Oktay","middleName":"Halit","lastName":"Aktepe","suffix":""},{"id":608200272,"identity":"fde5293c-0112-41cb-a9b8-d8f977368b0b","order_by":4,"name":"Huseyin Salih Semiz","email":"","orcid":"","institution":"Dokuz Eylul University","correspondingAuthor":false,"prefix":"","firstName":"Huseyin","middleName":"Salih","lastName":"Semiz","suffix":""},{"id":608200273,"identity":"affdc016-adaf-4897-bd24-b71aeb7b0e45","order_by":5,"name":"Aziz Karaoglu","email":"","orcid":"","institution":"Dokuz Eylul University","correspondingAuthor":false,"prefix":"","firstName":"Aziz","middleName":"","lastName":"Karaoglu","suffix":""}],"badges":[],"createdAt":"2026-02-18 13:08:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8909658/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8909658/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105149094,"identity":"bee06ba2-7a87-4d06-b01b-7e673127dad2","added_by":"auto","created_at":"2026-03-22 14:52:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC analysis of inflammatory variables.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003edNLR: derived neutrophil–lymphocyte ratio; LDH: lactate dehydrogenase\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8909658/v1/962951fcbaebc2c86d433307.png"},{"id":105149096,"identity":"57611d2b-1ee1-4553-b2f6-c4d3a26b237c","added_by":"auto","created_at":"2026-03-22 14:52:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan‒Meier analysis of overall survival by age group (A), estrogen receptor status (B), derived neutrophil‒lymphocyte ratio (C), progesterone receptor status (D), stage (E), clinical subtype (F) and first-line treatment comparison (G) among those who experienced de novo metastasis or recurrence.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8909658/v1/1be539261f4944b594273cc8.png"},{"id":105563256,"identity":"2659b3cd-19d0-40b8-99ed-f22fe941c17e","added_by":"auto","created_at":"2026-03-27 12:46:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1176271,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8909658/v1/1c2bdda5-a2ec-474d-b892-390a85390794.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Factors and Survival in Elderly Breast Cancer Patients: Roles of Age, Stage and Inflammatory Markers","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBreast cancer is one of the most common female cancers and the leading cause of cancer-related death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Over one-third of invasive cancers are reported among those older than 70 years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Moreover, the percentage of those who are diagnosed with breast cancer in the elderly age group is expected to increase. Limited data are available for those patients because clinical trial data that predominantly involve younger patients do not provide sufficient information to accurately assess the outcomes of therapy in older adults [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarly diagnosis of breast cancer and timely, effective implementation of treatment strategies are crucial for reducing breast cancer-related mortality. Several factors, including patient age, life expectancy, comorbidities, disease stage and molecular markers, play a significant role in determining the most appropriate treatment approach [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition to hormone status, stage and age, inflammatory markers such as the dNLR (derived neutrophil\u0026ndash;to-lymphocyte ratio) have prognostic value in these patients. In one study, high dNLRs and LDH levels were related to worse outcomes in HER2-positive breast cancer patients.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, the dNLR can reflect the immune state of a disease, and its prognostic importance has been shown in several studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Other inflammatory markers, such as lactate dehydrogenase (LDH), can have prognostic importance and can be used to evaluate treatment efficacy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite these insights, factors influencing overall survival in elderly patients with breast cancer remain poorly defined, particularly regarding the role of serum biomarkers in guiding treatment decisions and surveillance strategies. This study aimed to investigate the clinical characteristics and determinants of overall survival in elderly patients with breast cancer. Given that comorbid conditions in this population may substantially impact survival, we also sought to examine nonmalignant factors that contribute to mortality.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePatient Selection\u003c/p\u003e\u003cp\u003eThis retrospective study analyzed geriatric patients diagnosed with breast cancer between 2010 and 2023 at Dokuz Eylul University Medical Oncology Clinic. Those who were diagnosed before the age of 65 years and those who discontinued treatment or follow-up were excluded. Demographic, clinical, and pathological characteristics as well as treatment details were collected and recorded in accordance with the ethical approval obtained from the Dokuz Eylul University Ethics Committee.\u003c/p\u003e\u003cp\u003eClinical characteristics and data collection\u003c/p\u003e\u003cp\u003ePatients\u0026rsquo; ages and stages at the time of diagnosis, hormonal status, and grades were collected. Data on surgical procedures, neoadjuvant and adjuvant chemotherapies, hormonal therapies, and anti-HER2 treatments were documented. Recurrence, metastatic status, and first-, second-, and third-line treatment regimens were also recorded. The primary endpoint of the study was overall survival (OS), defined as the time from diagnosis to death or last follow-up. Patients were stratified into three age groups: Group 1 (65\u0026ndash;74 years), Group 2 (75\u0026ndash;84 years) and Group 3 (\u0026ge;\u0026thinsp;85 years).\u003c/p\u003e\u003cp\u003eBlood parameters\u003c/p\u003e\u003cp\u003eLDH levels and complete blood counts were recorded before treatment initiation. The derived neutrophil-to-lymphocyte ratio (dNLR) was calculated as neutrophil/(leukocytes \u0026ndash; neutrophils). The NLI score was defined as an LDH level exceeding the upper normal limit combined with the dNLR score, and patients were classified into three risk groups (0: low risk, 1: intermediate risk, 2: high risk). Mortality analysis was conducted on the basis of these hematological parameters. The optimal cutoff values for LDH and the dNLR were determined via receiver operating characteristic (ROC) curve analysis.\u003c/p\u003e\u003cp\u003eStatistical analysis\u003c/p\u003e\u003cp\u003eStatistical analyses were performed via SPSS version 25.0. Descriptive statistics were used to calculate the medians and means of continuous variables. Comparisons between age groups were conducted via the log-rank test. Univariate survival analysis was performed via the Kaplan‒Meier method in R studio [ version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria], whereas multivariate analysis and hazard ratio (HR) estimations with 95% confidence intervals (CIs) were conducted via the Cox proportional hazards regression model in SPSS, in which statistically significant dependent variables were included. A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Characteristics of the Age Groups\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eA total of 261 patients were included in this study, and the median follow-up was 142 months (95% CI [130,3- 153,3]). There were 145 patients in the 65\u0026ndash;74 years age group, 99 patients in the 75\u0026ndash;84 years age group, and 17 patients in the 85 years and above age group. Among patients aged 65\u0026ndash;74 years, 4 (2.75%) were male, whereas 2 (2%) of those aged 75\u0026ndash;84 years were male. There were no male patients aged 85 years or older. The clinical and pathological characteristics of the age groups are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePathological and Clinical Characteristics of the Age Groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;74 (n:145)\u003c/p\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003cp\u003e(n, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e75\u0026ndash;84 (n:99)\u003c/p\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003cp\u003e(n, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;85 (n:17)\u003c/p\u003e\n \u003cp\u003eGroup3\u003c/p\u003e\n \u003cp\u003e(n, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eCoronary Artery Disease\u003c/p\u003e\n \u003cp\u003eRespiratory Disease\u003c/p\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003cp\u003eCognitive Dysfunction\u003c/p\u003e\n \u003cp\u003eMusculoskeletal Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(35.86)\u003c/p\u003e\n \u003cp\u003e85(58.6)\u003c/p\u003e\n \u003cp\u003e48(33.1)\u003c/p\u003e\n \u003cp\u003e25(17.2)\u003c/p\u003e\n \u003cp\u003e20(13.7)\u003c/p\u003e\n \u003cp\u003e11(7.5)\u003c/p\u003e\n \u003cp\u003e56(38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(27.2)\u003c/p\u003e\n \u003cp\u003e71(71.7)\u003c/p\u003e\n \u003cp\u003e33(33.3)\u003c/p\u003e\n \u003cp\u003e18(18.1)\u003c/p\u003e\n \u003cp\u003e17(17.1)\u003c/p\u003e\n \u003cp\u003e14(14.1)\u003c/p\u003e\n \u003cp\u003e49(49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(29.4)\u003c/p\u003e\n \u003cp\u003e11(64.7)\u003c/p\u003e\n \u003cp\u003e7(41.1)\u003c/p\u003e\n \u003cp\u003e1(5.8)\u003c/p\u003e\n \u003cp\u003e2(11.7)\u003c/p\u003e\n \u003cp\u003e3(17.6)\u003c/p\u003e\n \u003cp\u003e9(52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvazive Ductal Carcinoma\u003c/p\u003e\n \u003cp\u003eInvazive Lobulary Carcinoma\u003c/p\u003e\n \u003cp\u003eOther Type of Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (55.8)\u003c/p\u003e\n \u003cp\u003e28(19.3)\u003c/p\u003e\n \u003cp\u003e36 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(61.6)\u003c/p\u003e\n \u003cp\u003e24(24.2)\u003c/p\u003e\n \u003cp\u003e14(14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(52.9)\u003c/p\u003e\n \u003cp\u003e4(23.5)\u003c/p\u003e\n \u003cp\u003e4(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade 1\u003c/p\u003e\n \u003cp\u003eGrade 2\u003c/p\u003e\n \u003cp\u003eGrade 3\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (8.9)\u003c/p\u003e\n \u003cp\u003e65 (44.8)\u003c/p\u003e\n \u003cp\u003e48 (33.1)\u003c/p\u003e\n \u003cp\u003e19 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (13.1)\u003c/p\u003e\n \u003cp\u003e44 (44.4)\u003c/p\u003e\n \u003cp\u003e36 (36.3)\u003c/p\u003e\n \u003cp\u003e6 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (5.8)\u003c/p\u003e\n \u003cp\u003e7 (41.1)\u003c/p\u003e\n \u003cp\u003e7 (41.1)\u003c/p\u003e\n \u003cp\u003e2 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eER Pozitive\u003c/p\u003e\n \u003cp\u003eER Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118 (81.3)\u003c/p\u003e\n \u003cp\u003e27 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (81.8)\u003c/p\u003e\n \u003cp\u003e18 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (94.1)\u003c/p\u003e\n \u003cp\u003e1 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePR Pozitive\u003c/p\u003e\n \u003cp\u003ePR Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (67.5)\u003c/p\u003e\n \u003cp\u003e47 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (72.7)\u003c/p\u003e\n \u003cp\u003e27 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (70.5)\u003c/p\u003e\n \u003cp\u003e5 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2 Pozitive\u003c/p\u003e\n \u003cp\u003eHER2 Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (15.8)\u003c/p\u003e\n \u003cp\u003e122 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(8.0)\u003c/p\u003e\n \u003cp\u003e91 (91.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(17.6)\u003c/p\u003e\n \u003cp\u003e14 (82.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHormone Positive/HER2\u003c/p\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003eHER2 Positive\u003c/p\u003e\n \u003cp\u003eTriple Negative\u003c/p\u003e\n \u003cp\u003eHormone/HER2 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106(73.1)\u003c/p\u003e\n \u003cp\u003e15(10.3)\u003c/p\u003e\n \u003cp\u003e16(11.0)\u003c/p\u003e\n \u003cp\u003e8(5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79(79.7)\u003c/p\u003e\n \u003cp\u003e3(3.0)\u003c/p\u003e\n \u003cp\u003e12(12.1)\u003c/p\u003e\n \u003cp\u003e5(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(70.5)\u003c/p\u003e\n \u003cp\u003e1(5.8)\u003c/p\u003e\n \u003cp\u003e2(11.7)\u003c/p\u003e\n \u003cp\u003e2(11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage 1\u003c/p\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003cp\u003eStage 3\u003c/p\u003e\n \u003cp\u003eStage 4\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (21.3)\u003c/p\u003e\n \u003cp\u003e46 (31.7)\u003c/p\u003e\n \u003cp\u003e43(29.6)\u003c/p\u003e\n \u003cp\u003e18(12.4)\u003c/p\u003e\n \u003cp\u003e7(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(16.1)\u003c/p\u003e\n \u003cp\u003e29(29.2)\u003c/p\u003e\n \u003cp\u003e29(29.2)\u003c/p\u003e\n \u003cp\u003e19(19.1)\u003c/p\u003e\n \u003cp\u003e6(6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(11.7)\u003c/p\u003e\n \u003cp\u003e1(5.8)\u003c/p\u003e\n \u003cp\u003e7(41.1)\u003c/p\u003e\n \u003cp\u003e3(17.6)\u003c/p\u003e\n \u003cp\u003e4(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edNLR\u0026thinsp;\u0026gt;\u0026thinsp;1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90(62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(52.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH\u0026thinsp;\u0026lt;\u0026thinsp;259\u003c/p\u003e\n \u003cp\u003eLDH\u0026thinsp;\u0026gt;\u0026thinsp;259\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (84.1)\u003c/p\u003e\n \u003cp\u003e17 (11.7)\u003c/p\u003e\n \u003cp\u003e6 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (84.8)\u003c/p\u003e\n \u003cp\u003e12 (12.1)\u003c/p\u003e\n \u003cp\u003e3 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (94.1)\u003c/p\u003e\n \u003cp\u003e1 (5.8)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLI Good\u003c/p\u003e\n \u003cp\u003eIntermadiate\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (53.1)\u003c/p\u003e\n \u003cp\u003e54 (37.2)\u003c/p\u003e\n \u003cp\u003e8 (5.5)\u003c/p\u003e\n \u003cp\u003e6 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (48.4)\u003c/p\u003e\n \u003cp\u003e39 (39.3)\u003c/p\u003e\n \u003cp\u003e9 (9)\u003c/p\u003e\n \u003cp\u003e3 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (52.9)\u003c/p\u003e\n \u003cp\u003e8 (47)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eCKD: chronic kidney disease, dNLR: derived neutrophil\u0026ndash;lymphocyte ratio, ER: estrogen receptor, HER: human epidermal receptor, LDH: lactate dehydrogenase, NLI: LDH combined with the dNLR index (**only those who have comorbidities are included in this table).\u003c/p\u003e\n \u003cp\u003eWe demonstrated that older patients were more likely to be diagnosed at advanced stages of the disease (p\u0026thinsp;=\u0026thinsp;0.049). However, no statistically significant differences were observed among the age groups regarding other clinicopathological characteristics or comorbidities.\u003c/p\u003e\n \u003cp\u003eAmong patients with nonmetastatic disease, surgical intervention was significantly less common in those aged over 85 years when treatment modalities were compared across age groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, the use of adjuvant radiotherapy and chemotherapy declined progressively with increasing age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These comparisons are summarized in Tables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProportions of surgical interventions for patients with surgery indications.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003cp\u003en: 127\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003cp\u003en: 80\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003cp\u003en: 14\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCS (n, %)\u003c/p\u003e\n \u003cp\u003eTM (n, %)\u003c/p\u003e\n \u003cp\u003eNot Operated (n, %)\u003c/p\u003e\n \u003cp\u003eNot Defined Surgery (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (60.68)\u003c/p\u003e\n \u003cp\u003e36 (26.2)\u003c/p\u003e\n \u003cp\u003e1 (11.03)\u003c/p\u003e\n \u003cp\u003e3 (2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (36.36)\u003c/p\u003e\n \u003cp\u003e38 (39.39)\u003c/p\u003e\n \u003cp\u003e6 (19.19)\u003c/p\u003e\n \u003cp\u003e5 (5.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (35.29)\u003c/p\u003e\n \u003cp\u003e3 (17.64)\u003c/p\u003e\n \u003cp\u003e5 (47.05)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eBCS: Breast Conserving Surgery; TM: Total Mastectomy\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of adjuvant/neoadjuvant chemotherapy\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;126\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup2\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;74\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup3\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeoadjuvant Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11(14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjuvant Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69 (54.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24(32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjuvant Hormonotherapy\u003c/p\u003e\n \u003cp\u003eAdjuvant RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101(80.1)\u003c/p\u003e\n \u003cp\u003e117(92.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65(87.8)\u003c/p\u003e\n \u003cp\u003e56(75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(88.8)\u003c/p\u003e\n \u003cp\u003e6(66.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eRT: Radiotherapy\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eROC analysis of inflammatory markers for mortality analysis.\u003c/strong\u003e 3.2 ROC analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCut-of Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpesifity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eLDH: lactate dehydrogenase, dNLR: derived lymphocyte\u0026ndash;neutrophil ratio\u003c/p\u003e\n \u003cp\u003eFor the inflammatory biomarkers, the dNLR and LDH values were analyzed. With a cutoff value of 1.74 for the dNLR, there was 53% sensitivity and 74% specificity (AUC\u0026thinsp;=\u0026thinsp;0.625, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). With the 259 mg/dl cutoff value of the LDH value, 18% sensitivity and 97% specificity were detected (AUC\u0026thinsp;=\u0026thinsp;0.545, p\u0026thinsp;=\u0026thinsp;0.212) (Fig. 1) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Owing to a lack of significance, LDH values, as well as NLI values, were not used in survival analysis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Overall survival\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe median survival time was 211 months (95% CI, 175\u0026ndash;246) for stage 1 patients, 142 months (95% CI, 110\u0026ndash;173) for stage 2 patients, 106 months (95% CI, 77\u0026ndash;134) for stage 3 patients and 36 months (95% CI, 17\u0026ndash;54) for stage 4 patients. A significant trend toward shorter survival durations was observed with increasing disease stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eER-positive patients demonstrated a significantly longer median OS of 126 months (95% CI: 102\u0026ndash;149) than did ER-negative patients (95% CI: 34\u0026ndash;111; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, no significant difference was observed based on PR status (PR-positive: 120 months [95% CI: 92\u0026ndash;143] vs. PR-negative: 96 months [95% CI: 54\u0026ndash;137]; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). The median OS was 132 months (95% CI: 115\u0026ndash;161) for hormone-positive/HER2-negative patients, 91 months (95% CI: 70\u0026ndash;NR) for HER2-positive/HR-negative patients, 45 months (95% CI: 33\u0026ndash;NR) for triple-negative patients, and 69 months (95% CI: 57\u0026ndash;NR) for HR+/HER2\u0026thinsp;+\u0026thinsp;patients (Figure F). Overall survival varied significantly across clinical subtypes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the luminal A/B subgroup exhibiting superior survival outcomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Owing to an insufficient number of events during the follow-up period, the upper limit of the 95% confidence interval could not be estimated for certain subgroups.\u003c/p\u003e\n \u003cp\u003eIn the survival analysis stratified by age group, the median overall survival was 148 months (95% CI, 123.7\u0026ndash;172.3) in group 1, whereas it was 91 months (95% CI, 71.1\u0026ndash;110.9) and 58 months (95% CI, 25.7\u0026ndash;90.3) in groups 2 and 3, respectively. Overall survival differed significantly among the age groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eEighty-four patients had de novo metastatic disease or experienced recurrence/metastasis, 75 of whom (89.3%) received first-line treatment. Among those who received first-line treatment, 51 patients (68%) also received second- and third-line treatment. Patients who received first-line treatment had longer survival than did those who did not, with median survival times of 58 months (95% CI, 45.75\u0026ndash;70.24) and 30 months (95% CI, 18.31\u0026ndash;41.68), respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e\n \u003cp\u003eThe dNLR marker was also included in the survival analysis, and the median survival was 166 months (95% CI, 135.95\u0026ndash; 196.04) for patients with a dNLR\u0026thinsp;\u0026lt;\u0026thinsp;1.74 and 73 months (95% CI, 58.52\u0026ndash; 87.47) for patients with a dNLR\u0026thinsp;\u0026gt;\u0026thinsp;1.74 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eIn our study, ER negativity, advanced age at diagnosis, advanced stage, clinical subtype and higher dNLR values were associated with a worse prognosis, and in multivariate regression analysis, stage at diagnosis, age group and dNLR value were found to be independent risk factors for mortality (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003eUnivariate and multivariate analyses of survival\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"520\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS m (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Groups\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e65- 74\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;75- 84\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;85 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e148 (123-172) \u0026nbsp; \u0026nbsp;p\u0026lt; 0.05\u003c/p\u003e\n \u003cp\u003e91 (71- 110)\u003c/p\u003e\n \u003cp\u003e58 (25- 90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; p\u0026lt; 0.05\u003c/p\u003e\n \u003cp\u003e1.4 (1.03- 2.15)\u003c/p\u003e\n \u003cp\u003e3.9 (2.12- 7.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eStage 1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Stage 2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Stage 3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Stage 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e211 (175- 246) \u0026nbsp; \u0026nbsp;p\u0026lt; 0.05\u003c/p\u003e\n \u003cp\u003e142 (110- 173)\u003c/p\u003e\n \u003cp\u003e106 (77- 134)\u003c/p\u003e\n \u003cp\u003e36 \u0026nbsp;(17- 54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; p\u0026lt; 0.05\u003c/p\u003e\n \u003cp\u003e3,8 (1.8- 8.3)\u003c/p\u003e\n \u003cp\u003e5.3 (2.5- 11.4)\u003c/p\u003e\n \u003cp\u003e13.9 (6.3- 30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstrogen Receptor Status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePozitif\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Negatif\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e126 (102- 149) \u0026nbsp; \u0026nbsp; p\u0026lt; 0.05\u003c/p\u003e\n \u003cp\u003e73 \u0026nbsp;(34- 111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Subtype\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eLuminal A/B\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;HER 2 positive\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Triple Negative\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Hormon/HER2 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e132 (115- 161) \u0026nbsp; \u0026nbsp; p\u0026lt; 0.05\u003c/p\u003e\n \u003cp\u003e91 (70- NR)\u003c/p\u003e\n \u003cp\u003e45 (33- NR)\u003c/p\u003e\n \u003cp\u003e69 (57- NR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;p\u0026lt; 0.05\u003c/p\u003e\n \u003cp\u003e2.3 (1.6- 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edNLR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026lt; 1.74\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026gt; 1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e166 (135- 196) \u0026nbsp; \u0026nbsp; p\u0026lt; 0.05\u003c/p\u003e\n \u003cp\u003e73 (58- 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eCI: confidence interval; dLR: derived neutrophil‒lymphocyte ratio; HR: hazard ratio; OS: overall survival\u003c/strong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn our study, advanced disease stage, older age at diagnosis, and a higher dNLR were identified as independent risk factors for mortality. We also observed that with increasing age, fewer patients were offered curative treatment modalities, including chemotherapy and radiotherapy. In addition, elderly patients in our cohort were more frequently diagnosed at advanced stages of disease, and DNLR, an inflammatory marker, emerged as a prognostic factor in the geriatric population.\u003c/p\u003e \u003cp\u003eForty-nine (18%) patients were diagnosed with stage 1 disease, and 76 (29%) and 79 (30%) patients were diagnosed with stage 2 and 3 disease, respectively. Moreover, 15% of the patients had stage 4 disease.\u003c/p\u003e \u003cp\u003eIn the literature, Glas et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] reported that metastatic disease at presentation was observed in 8% of patients aged over 85 years, whereas it was observed in 4% of those younger than 85 years, indicating a significantly greater prevalence of advanced-stage disease among the oldest patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, this age group was notably less likely to receive intensive treatments such as chemotherapy and radiotherapy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These observations are consistent with other studies; for example, patients aged 80 years or older have been shown to present with more advanced disease stages than those in the 70\u0026ndash;79 years age bracket (8% vs. 5.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Collectively, these findings suggest that barriers to healthcare access, the presence of multiple comorbidities, or perhaps more fatalistic attitudes toward illnesses in later life may collectively contribute to delayed diagnosis and undertreatment in the geriatric population.\u003c/p\u003e \u003cp\u003eIn terms of other disease features, there were no significant correlations among the age groups in terms of tumor type, receptor status or inflammatory markers. However, with respect to treatment modalities, patients aged 85 years and above were less likely to have primary tumor and axillary surgery (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005). In a study including 127,805 participants, Bastiannet et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. showed that elderly patients underwent less surgery and received less adjuvant systemic therapy; however, they received more adjuvant hormonal monotherapy. We found that patients aged 85 years and older were less likely to receive chemotherapy and radiotherapy than were those in groups 1 and 2. In addition, surgical intervention was performed in 52.9% of patients in group 3, whereas it was performed in 88.9% and 80.8% of patients in groups 1 and 2, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). With respect to hormonotherapy, elderly patients in our cohort were more commonly treated with hormonotherapy than with chemotherapy, with 88% of patients in group 3 receiving hormonotherapy. This trend might be attributed to a clinical preference for hormonotherapy in older populations, owing to its more favorable tolerability profile.\u003c/p\u003e \u003cp\u003eIn our study, we observed a steady decline in the administration of adjuvant chemotherapy and radiotherapy as age increased (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). While no significant correlation was found across age groups regarding neoadjuvant therapy, surgical intervention rates significantly decreased in the oldest group. These findings are consistent with broader literature; for example, studies by Bastiaannet et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and de Glas et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] similarly reported that geriatric patients receive substantially less surgery and adjuvant systemic therapy than their younger counterparts do. Furthermore, Liu et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] confirmed this trend of declining treatment intensity with age but also reported (consistent with our results) no significant age-related disparity in neoadjuvant chemotherapy administration (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Collectively, our results align with the well-documented global trend of reduced treatment aggressiveness in the elderly population.\u003c/p\u003e \u003cp\u003eThe lower rates of treatment administration among geriatric patients may be attributed to factors such as shorter life expectancy, higher prevalence of comorbidities, concerns about treatment-related toxicity, and patient or physician preferences.\u003c/p\u003e \u003cp\u003eWe performed ROC analysis to determine the significance of the dNLR and LDH cutoff values for survival analysis. In the literature, dNLR values can be used as a prognostic marker in localized breast cancer patients treated with adjuvant chemotherapy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Geng et al. used a cutoff NLR of 1.878 to determine survival outcomes (95% CI, 0.663\u0026ndash;0.744; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and high NLR values were associated with mortality in breast cancer patients who underwent curative resection [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Krenn- Pilko et al also reported that a derived neutrophil‒lymphocyte ratio and a dNLR\u0026thinsp;\u0026ge;\u0026thinsp;3 were associated with poor DFS (hazard ratio (HR) 1.87, 95% CI, 1.28\u0026ndash;2.73, p\u0026thinsp;=\u0026thinsp;0.001) and OS (HR 1.67, 95% CI, 1.07\u0026ndash;2.63, p\u0026thinsp;=\u0026thinsp;0.025) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In addition, a review of the literature revealed that high NLR values are independent risk factors for mortality in breast cancer patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This result is consistent with existing evidence in this area. The prognostic value of these parameters has not been previously established in geriatric populations, and external validation is warranted. Nonetheless, inflammatory markers remain prognostically informative in elderly patients despite age-related immune alterations.\u003c/p\u003e \u003cp\u003eIn our study, high LDH levels did not provide prognostic clues, as the ROC analysis was insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05); thus, LDH values and the NLI ratio were excluded from further analysis. However, a high dNLR was associated with poor survival according to univariate and multivariate analyses. Li et al. (5) reported that the optimal cutoff value for LDH was 244 U/L (p\u0026thinsp;=\u0026thinsp;0.003), and the corresponding AUC for LDH was 0.793 in breast cancer patients prior to trastuzumab emtansine therapy. Overall, despite existing evidence supporting LDH as a prognostic indicator, our data failed to demonstrate a similar association, suggesting that further prospective studies are needed to clarify its prognostic value. Our study included relatively few metastatic patients, which may have resulted in the insignificant prognostic importance of LDH.\u003c/p\u003e \u003cp\u003eIn the univariate analysis, age at diagnosis, disease stage, estrogen receptor negativity, clinical subtype, and higher dNLR values were identified as significant prognostic factors. We demonstrated similar prognostic features in clinical subtypes between geriatric and nongeriatric patients; thus, geriatric patients should receive the same therapy as their younger counterparts. While the relationship between increasing age and survival is well recognized, we suggest that older patients are less likely to receive treatment. This may explain the lower survival rates observed in this population.\u003c/p\u003e \u003cp\u003eMultivariate analysis further confirmed that advanced disease stage, older age and elevated dNLR were independent predictors of mortality. Specifically, patients diagnosed with stage IV disease and those aged 85 years or older presented significantly greater mortality risks (HR\u0026thinsp;=\u0026thinsp;13.9, 95% CI: 6.3\u0026ndash;30.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; HR\u0026thinsp;=\u0026thinsp;3.9, 95% CI: 2.12\u0026ndash;7.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). Moreover, a dNLR greater than 1.74 was independently associated with shorter overall survival (HR\u0026thinsp;=\u0026thinsp;2.3, 95% CI: 1.6\u0026ndash;3.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Liu et al. (13) reported that higher clinical stage, age at diagnosis and Ki-67 levels were independent risk factors for mortality. While advanced age is known to be strongly associated with mortality, the limited treatment options provided to this population may have had an additional detrimental effect on survival outcomes.\u003c/p\u003e \u003cp\u003eOne of the main strengths of this study is the relatively long median follow-up period, which allowed a more reliable evaluation of survival outcomes. In addition, patients were analyzed according to age groups, enabling a clearer assessment of age-related differences. Another important strength is that all patients were treated at the same center and managed with a similar clinical approach, which helped minimize variability in treatment strategies. Furthermore, this study provides a comprehensive evaluation by incorporating clinical characteristics, treatment patterns, inflammatory markers, and survival data. Notably, data on the prognostic relevance of inflammatory markers in geriatric patients remain limited in the literature, and our findings contribute to this underexplored area.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations should be acknowledged. This study was retrospective in nature and was conducted at a single center, which may limit the generalizability of the results. In addition, treatment indications were not evaluated in detail, restricting a fully adjusted assessment of survival outcomes. Moreover, given the impact of frailty and functional status on treatment decisions and survival in older adults with cancer, their omission in our study may have led to residual confounding. Finally, although the cohort included 261 patients, the sample size may still be insufficient for more detailed subgroup analyses.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn conclusion, our study demonstrated that the management of elderly breast cancer patients involves significant disparities compared with that of younger patients. We found that older patients are more likely to be diagnosed at advanced stages and are less frequently treated with surgery and adjuvant modalities, including chemotherapy and radiotherapy. A key finding of our research is that elevated dNLR serves as a significant independent risk factor for mortality, highlighting its potential utility as a practical and accessible prognostic biomarker in geriatric patients. While adjuvant treatment rates declined with age, neoadjuvant chemotherapy administration remained similar across age groups, suggesting a more balanced approach in preoperative settings. Ultimately, given that geriatric patients can achieve prolonged survival, our findings emphasize that treatment decisions should not be based on chronological age alone. Instead, a comprehensive geriatric assessment is essential to optimize personalized therapy and ensure that eligible patients are not undertreated.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003edNLR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDerived Neutrophil/Lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eER\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstrogen receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHER2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman epidermal growth factor receptor 2\u003cb\u003ea\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIHC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLactate dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNLI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeutrophil/lymphocyte index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeutrophil/lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNot Reached\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgesterone Receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of Dokuz Eylül University (Approval No: 2023/13-13, 26 April 2023). Due to the retrospective design of the study, the requirement for informed consent was waived by the Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are not publicly available because they contain confidential patient information. However, the datasets can be shared by the corresponding author upon reasonable request, provided that approval is obtained from the institutional ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no external funding.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions:\u003c/p\u003e\n\u003cp\u003eD.A., H.İ.E. and E.A.; methodology D.A., E.A.; software, D.A.; validation, D.A.; formal analysis, D.A., H.İ.E.; investigation, D.A., E.A.; resources, E.A.; data curation, D.A., H.İ.E.; writing, original draft preparation, D.A.; writing, review and editing, H.S.S.; data curation, editing, O.H.A.; editing, A.K.; supervision. E.A.; visualization, E.A., D.A.; project administration. All the authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eNo acknowledgments exist.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424. doi: 10.3322/caac.21492. Epub 2018 Sep 12. Erratum in: CA Cancer J Clin. 2020 Jul;70(4):313. doi: 10.3322/caac.21609. PMID: 30207593.\u003c/li\u003e\n\u003cli\u003eDeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Goding Sauer A, Jemal A, Siegel RL. Breast cancer statistics, 2019. CA Cancer J Clin. 2019 Nov;69(6):438-451. doi: 10.3322/caac.21583. Epub 2019 Oct 2. PMID: 31577379.\u003c/li\u003e\n\u003cli\u003eShachar SS, Hurria A, Muss HB. Breast Cancer in Women Older Than 80 Years. J Oncol Pract. 2016 Feb;12(2):123-32. doi: 10.1200/JOP.2015.010207. PMID: 26869650.\u003c/li\u003e\n\u003cli\u003eFonseca VC, Sidiropoulou Z. Geriatric Breast Cancer: Staging, Molecular Surrogates, and Treatment. A Review \u0026amp; Meta-analysis. Aging Dis. 2024 Aug 1;15(4):1602-1618. doi: 10.14336/AD.2023.1002. PMID: 37962462; PMCID: PMC11272193.\u003c/li\u003e\n\u003cli\u003eLi L, Ai L, Jia L, Zhang L, Lei B, Zhang Q. High score of LDH plus dNLR predicts poor survival in patients with HER2-positive advanced breast cancer treated with trastuzumab emtansine. BMC Cancer. 2022 Jan 3;22(1):29. doi: 10.1186/s12885-021-09131-6. PMID: 34980025; PMCID: PMC8722106.\u003c/li\u003e\n\u003cli\u003ePelizzari G, Gerratana L, et al. A risk score integrating lymphocytes ratios (LRs) and lactate dehydrogenase (LDH) levels to predict prognosis in metastatic breast cancer (MBC) patients. Ann Oncol. 2018;29 Suppl 8: viii96\u003c/li\u003e\n\u003cli\u003eHong H, Fang X, Huang H, Wang Z, Lin T, Yao H. The derived neutrophil-to-lymphocyte ratio is an independent prognostic factor in patients with angioimmunoblastic T-cell lymphoma. Br J Hematol. 2020 Jun;189(5):908-912. doi: 10.1111/bjh.16447. Epub 2020 Feb 27. PMID: 32103494.\u003c/li\u003e\n\u003cli\u003ePetrelli F, Cabiddu M, Coinu A, Borgonovo K, Ghilardi M, Lonati V, Barni S. Prognostic role of lactate dehydrogenase in solid tumors: a systematic review and meta-analysis of 76 studies. Acta Oncol. 2015 Jul;54(7):961-70. doi: 10.3109/0284186X.2015.1043026. Epub 2015 May 18. PMID: 25984930.\u003c/li\u003e\n\u003cli\u003ePelizzari G, Basile D, Zago S, Lisanti C, Bartoletti M, Bortot L, Vitale MG, Fanotto V, Barban S, Cinausero M, Bonotto M, Gerratana L, Mansutti M, Curcio F, Fasola G, Minisini AM, Puglisi F. Lactate Dehydrogenase (LDH) Response to First-Line Treatment Predicts Survival in Metastatic Breast Cancer: First Clues for A Cost-Effective and Dynamic Biomarker. Cancers (Basel). 2019 Aug 24;11(9):1243. doi: 10.3390/cancers11091243. PMID: 31450641\u003c/li\u003e\n\u003cli\u003ede Glas N, Bastiaannet E, de Boer A, Siesling S, Liefers GJ, Portielje J. Improved survival of older patients with advanced breast cancer due to an increase in systemic treatments: a population-based study. Breast Cancer Res Treat. 2019 Nov;178(1):141-149. doi: 10.1007/s10549-019-05356-z. Epub 2019 Jul 19. PMID: 31325075\u003c/li\u003e\n\u003cli\u003eLodi M, Scheer L, Reix N, Heitz D, Carin AJ, Thi\u0026eacute;baut N, Neuberger K, Tomasetto C, Mathelin C. Breast cancer in elderly women and altered clinico-pathological characteristics: a systematic review. Breast Cancer Res Treat. 2017 Dec;166(3):657-668. doi: 10.1007/s10549-017-4448-5. Epub 2017 Aug 12. PMID: 28803352.\u003c/li\u003e\n\u003cli\u003eBastiaannet E, Liefers GJ, de Craen AJ, Kuppen PJ, van de Water W, Portielje JE, van der Geest LG, Janssen-Heijnen ML, Dekkers OM, van de Velde CJ, Westendorp RG. Breast cancer in elderly compared to younger patients in the Netherlands: stage at diagnosis, treatment and survival in 127,805 unselected patients. Breast Cancer Res Treat. 2010 Dec;124(3):801-7. doi: 10.1007/s10549-010-0898-8. Epub 2010 Apr 29. PMID: 20428937.\u003c/li\u003e\n\u003cli\u003eLiu X, Zheng D, Wu Y, Luo C, Fan Y, Zhong X, Zheng H. Treatment patterns and outcomes in older women with early breast cancer: a population-based cohort study in China. BMC Cancer. 2021 Mar 5;21(1):226. doi: 10.1186/s12885-021-07947-w. PMID: 33673816; PMCID\u003c/li\u003e\n\u003cli\u003eCorbeau I, Jacot W, Guiu S. Neutrophil to Lymphocyte Ratio as Prognostic and Predictive Factor in Breast Cancer Patients: A Systematic Review. Cancers (Basel). 2020 Apr 13;12(4):958. doi: 10.3390/cancers12040958. PMID: 32295078.\u003c/li\u003e\n\u003cli\u003eGeng SK, Fu SM, Fu YP, Zhang HW. Neutrophil to lymphocyte ratio is a prognostic factor for disease free survival in patients with breast cancer underwent curative resection. Medicine (Baltimore). 2018 Aug;97(35):e11898. doi: 10.1097/MD.0000000000011898. PMID: 30170382.\u003c/li\u003e\n\u003cli\u003eKrenn-Pilko S, Langsenlehner U, Stojakovic T, Pichler M, Gerger A, Kapp KS, Langsenlehner T. The elevated preoperative derived neutrophil-to-lymphocyte ratio predicts poor clinical outcome in breast cancer patients. Tumor Biol. 2016 Jan;37(1):361-8. doi: 10.1007/s13277-015-3805-4. Epub 2015 Jul 29. PMID: 26219894.\u003c/li\u003e\n\u003cli\u003eCorbeau I, Jacot W, Guiu S. Neutrophil to Lymphocyte Ratio as Prognostic and Predictive Factor in Breast Cancer Patients: A Systematic Review. Cancers (Basel). 2020 Apr 13;12(4):958. doi: 10.3390/cancers12040958. PMID: 32295078; PMCID: PMC7226461.\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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"geriatric patients, breast cancer, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8909658/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8909658/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eIn elderly individuals, breast cancer is a major clinical problem, and sufficient data regarding treatment in this population are lacking. This study aimed to investigate the clinical and pathological characteristics and overall survival of elderly patients with breast cancer, as well as the factors influencing survival outcomes\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective study included 261 patients aged 65 years or older who were diagnosed with breast cancer and treated at the Medical Oncology Department of Dokuz Eylül University Faculty of Medicine between 2010 and 2023. The clinical and pathological characteristics and treatment modalities of the included patients were analyzed, as were their biochemical results, which included the NLR, LDH and NLI. The patients were stratified into three age groups (65–74, 75–84, and ≥85 years), and intergroup differences were assessed. Survival analyses were conducted via Kaplan‒Meier and log-rank tests, and multivariate analyses were performed according to the Cox regression model. The diagnostic features of the biochemical variables were verified via receiver operating characteristic (ROC) analyses.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 261 patients were included in the study, with a median age at diagnosis of 73.5 years (range: 65–88). The median survival time in the 65–74 year age group (n=145) was 148 months (95% CI, 123.71–172.28), that in the 75–84 year age group was 91 months (95% CI, 71.10– 110.89), and that in the 85+ year age group was 58 months (95% CI, 25.73–90.26) (p\u0026lt;0.05). Patients aged 85 years and older were less likely to undergo surgery and receive adjuvant chemotherapy (p\u0026lt;0.05). No significant differences were observed among the age groups regarding pathological features or the rate of neoadjuvant therapy. However, older patients are more likely to be diagnosed at advanced stages than their younger counterparts are (p\u0026lt;0.05).\u003cstrong\u003e \u003c/strong\u003eUnivariate survival analysis revealed that advanced disease stage at diagnosis and a high dNLR and estrogen receptor status have prognostic importance; however, tumor grade, progesterone receptor status and whether adjuvant or neoadjuvant therapy is administered do not significantly affect overall survival. ROC analysis revealed that the LDH value was insignificant; thus, the LDH and NLI (LDH combined with the dNLR) indices were excluded from the survival analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Although clinicopathological features were similar across age groups, elderly patients—particularly those over 85—received less aggressive curative treatment. Advanced stage, age, and elevated inflammatory markers (dNLRs) are independent predictors of mortality. These findings suggest that treatment decisions should be personalized through geriatric assessment to avoid undertreatment, as this population can achieve substantial survival.\u003c/p\u003e","manuscriptTitle":"Prognostic Factors and Survival in Elderly Breast Cancer Patients: Roles of Age, Stage and Inflammatory Markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-22 14:52:46","doi":"10.21203/rs.3.rs-8909658/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-10T18:51:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T13:30:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T11:05:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210665605950899707447034775397052506184","date":"2026-03-28T10:17:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T12:27:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87715360333897458089280477110668378772","date":"2026-03-25T05:19:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128266805876491306791544263614354529793","date":"2026-03-24T15:21:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33162345203368982208797667352423402628","date":"2026-03-24T11:26:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T16:58:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T06:44:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-19T16:06:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T15:59:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-02-19T13:49:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c91b9cb5-74cd-4785-abc7-1096b05d6ff9","owner":[],"postedDate":"March 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T07:24:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-22 14:52:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8909658","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8909658","identity":"rs-8909658","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}