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Development and Validation a Survival Prediction Model and a Risk Stratification for Elderly Locally Advanced Breast Cancer

  • Xiangdi Meng
    Affiliations
    Department of Radiotherapy, The First Affiliated Hospital of Weifang Medical College, Weifang, Shandong, China

    Department of Radiotherapy, Weifang People's Hospital, Weifang, Shandong, China
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  • Xiaolong Chang
    Affiliations
    Department of Radiotherapy, The First Affiliated Hospital of Weifang Medical College, Weifang, Shandong, China

    Department of Radiotherapy, Weifang People's Hospital, Weifang, Shandong, China
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  • Xiaoxiao Wang
    Affiliations
    Department of Radiotherapy, The First Affiliated Hospital of Weifang Medical College, Weifang, Shandong, China

    Department of Radiotherapy, Weifang People's Hospital, Weifang, Shandong, China
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  • Yinghua Guo
    Correspondence
    Address for correspondence: Yinghua Guo, MD, Department of Radiotherapy, The First Affiliated Hospital of Weifang Medical College, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, Shandong, 261041, China
    Affiliations
    Department of Radiotherapy, The First Affiliated Hospital of Weifang Medical College, Weifang, Shandong, China

    Department of Radiotherapy, Weifang People's Hospital, Weifang, Shandong, China
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Open AccessPublished:June 25, 2022DOI:https://doi.org/10.1016/j.clbc.2022.06.002

      Highlights

      • Elderly LABC is a heterogeneous disease with multiple clinical manifestations.
      • Few studies have focused on its prognosis.
      • This is the first nomogram concerning on predicting the OS of elderly LABC.
      • This model can assist clinicians in improving treatment and follow-up strategies.
      • The study also developed a risk stratification and web version of the nomogram.

      Abstract

      Purpose

      we aimed to develop an individualized survival prediction model for elderly locally advanced breast cancer (LABC) and stratify its risk to assist in the treatment and follow-up of patients.

      Methods

      Elderly LABC data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The best model was screened using Cox, least absolute shrinkage and selection operator (LASSO) and best subset regression to construct the nomogram. After internal and external validation of this model, risk stratification was established, and differences between risk groups were assessed using Kaplan-Meier method.

      Results

      A total of 10,697 elderly LABC patients were divided into a training group (n = 7131) and a validation group (n = 3566) with a 5-year overall survival rate of 57.6% [confidence interval (CI): 56.4%-58.7%]. A nomogram was developed using age, marital status, histological grading, estrogen and progesterone receptors, surgery, radiation therapy, and chemotherapy as predictors. This model was evaluated and validated to perform well, with a discrimination index of 0.744 (95% CI: 0.734-0.753). Patients were divided into low, medium and high groups based on risk scores, and there was a significant difference in survival between the 3 groups.

      Conclusion

      The prognosis of elderly LABC was poor. The nomogram constructed based on prognostic factors could accurately predict the prognosis, which would provide a reference for treatment and follow-up.

      Keywords

      Introduction

      Locally advanced breast cancer (LABC) is a heterogeneous disease with multiple clinical manifestations and a relatively poor prognosis, and its precise management remains an important clinical challenge.
      • Aebi S
      • Karlsson P
      • Wapnir IL.
      Locally advanced breast cancer.
      • Yalcin B.
      Overview on locally advanced breast cancer: defining, epidemiology, and overview on neoadjuvant therapy.
      • Madigan LI
      • Dinh P
      • Graham JD.
      Neoadjuvant endocrine therapy in locally advanced estrogen or progesterone receptor-positive breast cancer: determining the optimal endocrine agent and treatment duration in postmenopausal women-a literature review and proposed guidelines.
      • Giordano SH.
      Update on locally advanced breast cancer.
      • Tryfonidis K
      • Senkus E
      • Cardoso MJ
      • Cardoso F.
      Management of locally advanced breast cancer-perspectives and future directions.
      At M.D. Anderson Cancer Center, LABC was defined as breast cancer with maximum diameter of primary tumor ≥ 5 cm and/or involvement of the skin, chest wall, or disease presenting with 4 or more lymph node metastases, ie.., stage T3N0M0 or stage III.
      • Singletary SE
      • Allred C
      • Ashley P
      • et al.
      Revision of the American joint committee on cancer staging system for breast cancer.
      To date, few researchers have focused on the long-term prognosis of LABC, in part because of the small proportion of such patients and the difficulty of following up on their poor prognosis.
      • Tryfonidis K
      • Senkus E
      • Cardoso MJ
      • Cardoso F.
      Management of locally advanced breast cancer-perspectives and future directions.
      ,
      • Hornova J
      • Bortlicek Z
      • Majkova P
      • et al.
      Locally advanced breast cancer in elderly patients.
      • Dhanushkodi M
      • Sridevi V
      • Shanta V
      • et al.
      Locally Advanced Breast Cancer (LABC): real-world outcome of patients from cancer institute, Chennai.
      • Varghese F
      • Wong J.
      Breast cancer in the elderly.
      Second, it has been noted that the elderly is more likely to develop LABC and age itself is a survival disadvantage, so overall survival (OS) tends to be worse in the elderly with LABC.
      • Hornova J
      • Bortlicek Z
      • Majkova P
      • et al.
      Locally advanced breast cancer in elderly patients.
      ,
      • Agborbesong O
      • Helmer SD
      • Reyes J
      • Strader LA
      • Tenofsky PL.
      Breast cancer treatment in the elderly: Do treatment plans that do not conform to NCCN recommendations lead to worse outcomes?.
      This is because advanced age meant that their physical condition, self-care and compliance were less likely to benefit from multidisciplinary treatment or clinical trials.
      • Varghese F
      • Wong J.
      Breast cancer in the elderly.
      What's more, the fact that no prognostic assessment strategy has been established for elderly LABC makes its follow-up and treatment more difficult.
      • Mangieri CW
      • Ruffo J
      • Chiba A
      • Howard-McNatt M.
      Treatment and outcomes of women with large locally advanced breast cancer.
      Therefore, there is an urgent need for an individualized prognostic prediction system to accurately identify high-risk patients to assist in treatment decisions and precise management of elderly LABC.
      Previous studies showed that the nomogram could synthesize patient's clinicopathological characteristics and has been widely used to predict prognosis, risk stratification and treatment options.
      • Iasonos A
      • Schrag D
      • Raj GV
      • Panageas KS.
      How to build and interpret a nomogram for cancer prognosis.
      • Balachandran VP
      • Gonen M
      • Smith JJ
      • DeMatteo RP.
      Nomograms in oncology: more than meets the eye.
      • Meng X
      • Ma H
      • Yin H
      • et al.
      Nomogram predicting the risk of locoregional recurrence after mastectomy for invasive micropapillary carcinoma of the breast.
      Many studies have been done to establish nomograms for breast cancer, unfortunately, only the elderly LABC is missing.
      • Xiong Y
      • Cao H
      • Zhang Y
      • et al.
      Nomogram-predicted survival of breast cancer brain metastasis: a SEER-based population study.
      ,
      • Yu Y
      • Tan Y
      • Xie C
      • et al.
      Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer.
      Previously, Kimmick et al. and Carey et al. have developed prediction models for elderly breast cancer patients and provided predictions of 2-year mortality based on their clinicopathological characteristics and physical condition.
      • Kimmick GG
      • Major B
      • Clapp J
      • et al.
      Using ePrognosis to estimate 2-year all-cause mortality in older women with breast cancer: Cancer and Leukemia Group B (CALGB) 49907 and 369901 (Alliance A151503).
      ,
      • Carey EC
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      • Lindquist K
      • Covinsky KE.
      Development and validation of a functional morbidity index to predict mortality in community-dwelling elders.
      Our study will focus on the locally advanced stage of elderly patients and expects to predict longer-term OS, as this group of patients is likely to have worse survival and needs to be evaluated individually for their prognosis. Therefore, we developed a nomogram and constructed a novel risk stratification for elderly LABC with the aim of providing accurate prognostic information and assisting clinicians in identifying high-risk patients.

      Material and Methods

      Patient Source, Screening and Variable Selection

      In this study, clinicopathological data of breast cancer patients from 2010 to 2017 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database using SEER*Stat 8.3.9.1 software. Inclusion criteria: age ≥ 65 years at diagnosis; American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (Eighth edition) of T3N0M0 and stage III. Exclusion criteria: no confirmed histopathological diagnosis; not the first primary malignancy; 0 months of follow-up; missing necessary clinicopathological information. The screened elderly LABC meeting the criteria were randomly divided into training and validation groups in a 2:1 ratio. Age at diagnosis, marital status at diagnosis [married or unmarried (widowed, single, unmarried or domestic partner, divorced, separated, unknown], TNM stage, histological grading, immunohistochemistry, molecular subtypes and primary treatment information was used as study variables in this study. The study endpoint was OS, and the follow-up period was the time period from diagnosis to death or the last follow-up date or cut-off date of the patient. Before the study started, we signed the SEER Data Research Use Agreement and obtained database access under the login 15029-Nov2020. The SEER database is a public database and thus approved by the institutional ethics committee.

      Statistical Analysis

      Clinicopathological Characteristics

      Categorical variables were counted and percentages reported, and differences in the distribution of variables between the training and validation groups were examined using a chi-square test or Fisher's exact test. Under the condition of normality and homogeneity of variance, continuous variables would be reported as mean and standard deviation and subjected to t-test, and if this condition was not met, median and interquartile range (IQR) will be reported and subjected to Wilcoxon rank-sum test.

      Predictor Screening and Development of Nomogram

      The prognostic correlates were screened based on univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and best subset regression (BSR), respectively. Variables with univariate analysis P < .05 or corresponding to 1 standard error of the penalty coefficient (Lambda) of the least mean-square error (MSE) of the LASSO or corresponding to the maximum adjusted R² of the BSR were included in the multivariate Cox regression with stepwise backward for validation, respectively. The best model was determined by the minimum value of the Akaike information criterion (AIC) and the maximum value of the R-squared (R²), and a nomogram created from this. When using the nomogram, each predictor was quantitatively assigned a point and then summed to obtain the total patient survival point, which then corresponded to the patient's individualized 3-, 5- and 8-years OS.

      Verification and Application of Nomogram

      The nomogram was evaluated and validated in the training and validation groups, respectively. Model discrimination was assessed using the concordance index (C-index), where a value closer to 1.00 indicated better model discrimination. Stability was evaluated using the receiver operating characteristic (ROC) curve, and the less the area under the curve (AUC) changed over time, the more stable the model was. Accuracy was assessed using calibration plots, where the closer the curve was to the 45° line the more consistent the model prediction was with the actual OS. Clinical utility was analyzed using decision curve analysis (DCA).
      The total points of all patients in the training group were calculated. These points divided into low, medium and high groups equally using trichotomous method were used to establish the risk stratification for elderly LABC. Kaplan-Meier method was used to compare and evaluate the stratification effect of the new risk stratification with TNM staging. To facilitate communication and use, we have also created a web version of this model. All statistical analyses for this study were performed using R language (version 4.1.0). P < .05 was considered as a statistically significant difference.

      Results

      Study Population Characteristics

      A total of 10,697 elderly LABC patients with a median follow-up of 38 months (IQR: 20 months and 62 months) and a median age of 73 years (IQR: 68 years and 81 years) were included in this study (Figure 1). There were 7131 patients in the training group and 3,566 in the validation group of the study, and no significant difference between these 2 groups in the clinicopathological characteristics (P > .05, Table 1). Kaplan-Meier curve showed that the 3-, 5- and 8-year OS of elderly LABC were 72.2% [95% confidence interval (CI): 71.3%-73.1%], 57.6% (95% CI: 56.4%-58.7%) and 43.3% (95% CI: 41.8%-44.9%), respectively, with a median survival time of 78 months (95% CI: 75 months-81 months) (Figure 2).
      Figure 1
      Figure 1Flow chart for screening of elderly locally advanced breast. Abbreviations: SEER, Surveillance, Epidemiology, and End Results; TNM, Tumor-Node-Metastasis; ER, Estrogen receptor; PR, Progesterone receptor; HER 2, Human Epidermal Growth Factor Receptor 2.
      Table 1Clinicopathologic Characteristics of Elderly Locally Advanced Breast Cancer
      CharacteristicsWhole CohortTraining GroupValidation GroupP value
      n = 10697 (%)n = 7131 (%)n = 3566 (%)
      Age (years).368
       65∼745884 (55.01)3907 (54.79)1977 (55.44)
       75∼843203 (29.94)2165 (30.36)1038 (29.11)
       > 841610 (15.05)1059 (14.85)551 (15.45)
      Marital status.498
       Married4505 (42.11)3020 (42.35)1485 (41.64)
       Unmarried6192 (57.89)4111 (57.65)2081 (58.36)
      Site.831
       Upper-outer3227 (30.17)2165 (30.36)1062 (29.78)
       Lower-outer702 (6.56)465 (6.52)237 (6.65)
       Upper-inner790 (7.39)511 (7.17)279 (7.82)
       Lower-inner396 (3.70)269 (3.77)127 (3.56)
       Center1065 (9.96)705 (9.89)360 (10.10)
       Other4517 (42.23)3016 (42.29)1501 (42.09)
      Laterality.658
       Left5499 (51.41)3653 (51.23)1846 (51.77)
       Right5196 (48.57)3476 (48.74)1720 (48.23)
       Other2 (0.02)2 (0.03)0 (0.00)
      T status.394
       T016 (0.15)9 (0.13)7 (0.20)
       T11061 (9.92)683 (9.58)378 (10.60)
       T22709 (25.32)1815 (25.45)894 (25.07)
       T34136 (38.67)2754 (38.62)1382 (38.75)
       T42775 (25.94)1870 (26.22)905 (25.38)
      N status.244
       N02525 (23.60)1699 (23.83)826 (23.16)
       N12390 (22.34)1609 (22.56)781 (21.90)
       N23605 (33.70)2411 (33.81)1194 (33.48)
       N32177 (20.35)1412 (19.80)765 (21.45)
      TNM stage.255
       T3N0M01714 (16.02)1148 (16.10)566 (15.87)
       IIIA4474 (41.82)3001 (42.08)1473 (41.31)
       IIIB2332 (21.80)1570 (22.02)762 (21.37)
       IIIC2177 (20.35)1412 (19.80)765 (21.45)
      Grade.409
       I-II6001 (56.10)3980 (55.81)2021 (56.67)
       III-IV4696 (43.90)3151 (44.19)1545 (43.33)
      ER.653
       Negative2359 (22.05)1563 (21.92)796 (22.32)
       Positive8338 (77.95)5568 (78.08)2770 (77.68)
      PR.777
       Negative3834 (35.84)2563 (35.94)1271 (35.64)
       Positive6863 (64.16)4568 (64.06)2295 (64.36)
      HER2.919
       Negative8803 (82.29)5866 (82.26)2937 (82.36)
       Positive1894 (17.71)1265 (17.74)629 (17.64)
      Molecular subtypes.819
       Luminal A7253 (67.80)4849 (68.00)2404 (67.41)
       Luminal B1206 (11.27)804 (11.27)402 (11.27)
       HER2 enriched688 (6.43)461 (6.46)227 (6.37)
       Triple Negative1550 (14.49)1017 (14.26)533 (14.95)
      Surgery.166
       No1305 (12.20)896 (12.56)409 (11.47)
       BCS2356 (22.02)1545 (21.67)811 (22.74)
       Mastectomy7036 (65.78)4690 (65.77)2346 (65.79)
      Radiotherapy.681
       No4912 (45.92)3285 (46.07)1627 (45.63)
       Yes5785 (54.08)3846 (53.93)1939 (54.37)
      Chemotherapy.671
       No4859 (45.42)3250 (45.58)1609 (45.12)
       Yes5838 (54.58)3881 (54.42)1957 (54.88)
      Abbreviations: TNM stage = tumor-node-metastasis stage; ER = estrogen receptor; PR = progesterone receptor; HER2 = human epidermal growth factor receptor 2; BCS = breast conservation surgery.
      Figure 2
      Figure 2Kaplan-Meier curve for overall survival in elderly locally advanced breast cancer.

      Survival Predictors Screening

      Univariate Cox regression, LASSO regression and BSR were performed for each of the 7,131 patients in the training group. Forest plots of the univariate analysis showed 12 variables associated with OS in elderly LABC, and the LASSO regression and the BSR had 10 and 7 variables, respectively (Figure 3). The combination of variables screened by these 3 methods was included in multivariate Cox regression with stepwise backward showed the best fit of the model screened by LASSO regression (R² = 0.22, AIC = 43399.65, Supplemental Table 1). Ultimately, the prognostic predictors of elderly LABC were age, marital status, T stage, N stage, histological grade, estrogen receptor (ER), progesterone receptor (PR), surgery, radiotherapy and chemotherapy, and their multivariate Cox regression results were shown in Table 2 (All P < .05).
      Figure 3
      Figure 3Univariate Cox regression forest plot, least absolute shrinkage and selection operator (LASSO) regression with 5-fold cross-validation and best subset regression (BSR) for screening predictors.
      Table 2Multivariate Cox Regression of Predictors Used to Construct the Nomogram
      CharacteristicsMultivariate Cox Regression
      HR (95% CI)P value
      Age (years)
       65∼74
       75∼841.61 (1.47-1.76)<.001
       >842.24 (2.00-2.52)<.001
      Marital status
       Married
       Unmarried1.17 (1.07-1.27)<.001
      T status
       T0
       T12.07 (0.66-6.50).214
       T22.69 (0.86-8.41).088
       T33.10 (0.99-9.70).051
       T44.12 (1.32-12.86).015
      N status
       N0
       N11.18 (1.06-1.33).004
       N21.63 (1.44-1.85)<.001
       N32.16 (1.90-2.45)<.001
      Grade
       I-II
       III-IV1.40 (1.28-1.52)<.001
      ER
       Negative
       Positive0.67 (0.60-0.76)<.001
      PR
       Negative
       Positive0.75 (0.67-0.83)<.001
      Surgery
       No
       BCS0.46 (0.40-0.53)<.001
       MAST0.51 (0.46-0.56)<.001
      Radiotherapy
       No
       Yes0.64 (0.58-0.69)<.001
      Chemotherapy
       No
       Yes0.63 (0.58-0.70)<.001
      Abbreviations: HR = hazard ratio; CI = confidence interval; TNM stage = tumor-node-metastasis stage; ER = estrogen receptor; PR = progesterone receptor; HER2 = human epidermal growth factor receptor 2; BCS = breast conservation surgery.

      Development and Validation of the Nomogram

      We created a nomogram for predicting individualized OS of elderly LABC based on the above 10 predictors (Figure 4). In addition, a web version of the nomogram has been developed for the convenience of clinicians and patients (Supplemental Figure 1, https://impcofmxd.shinyapps.io/ElderlyLABC/). The C-indexes of the nomogram were 0.744 (95% CI: 0.734-0.753) and 0.749 (95% CI: 0.735-0.763) in the training and validation groups, respectively. The time-dependent ROC curves showed good stability of the model with insignificant changes in AUC at 3-, 5- and 8-years (Figures 5A and B). The calibration curves demonstrated that the predicted values of the nomogram were in good agreement with the actual values (Figures 5C and D). The DCA curves showed that the triggered medical interventions guided by the nomogram resulted in a better net benefit for the elderly LABC patients (Figures 5E and F).
      Figure 4
      Figure 4Nomogram for predicting overall survival of elderly locally advanced breast cancer. Abbreviations: ER, estrogen receptor; PR, progesterone receptor.
      Figure 5
      Figure 5Validation of nomogram and risk stratification assessment: Time-dependent receiver operating characteristic (ROC) curves in training and validation groups [77.3% (95% CI:76.0%-78.7%), 76.7% (95% CI: 75.2%-78.1%), 77.3% (95% CI: 74.6%-79.9%) for 3-, 5- and 8-years AUC, 5A and 5B]; Calibration plots in training and validation groups (5C and 5D); Decision curve analysis in training and validation groups (5E and 5F) and Kaplan-Meier curves for risk stratification and TNM stage (5G and 5H).

      Risk Stratification

      Based on the risk points calculated from the nomogram, we further constructed a risk stratification for the OS of elderly LABC, with a risk point ≤ 184 in the low-risk group (48.06% of patients, 3427/7131), between 184 and 288 in the intermediate-risk group (44.41% of patients, 3167/7131), and >288 in the high-risk group (7.53% of patients, 537/7131). Kaplan-Meier curves showed that these 3 risk subgroups established based on nomogram could be separated from each other and had significant OS differences (log-rank, P < .001), whereas in TNM staging, there was no significant difference in survival between patients in the T3N0M0 group and stage IIIA, and between patients in stage IIIC and IIIB (Figures 5G and H).

      Discussion

      In this work, we developed and validated the first survival prediction nomogram for elderly LABC using age, marriage, T stage, N stage, histological grade, ER, PR, surgery, radiotherapy and chemotherapy as predictors. This model allowed individualized prediction of 3-, 5- and 8-years OS, and its web version was more convenient and faster. Meanwhile, the classification capability of the new risk stratification constructed from the nomogram was superior to the current TNM staging.
      Few studies have been conducted on the long-term prognosis of elderly LABC and there is a lack of effective prognostic assessment strategies. Currently, LABC is still predominantly multidisciplinary comprehensive treatment,
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      First, elderly patients were not suitable for surgery or difficult to operate immediately because of their age, medical comorbidities or poor compliance, and even if they underwent surgery, complications such as upper limb edema could cause great suffering.
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      These may be part of the reasons for the poor prognosis and low number of studies of elderly LABC. Second, some of those who can participate in clinical trials were often patients with better physical conditions and were less representative, making some results not fully applicable to elderly LABC.
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      • Mangieri CW
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      Treatment and outcomes of women with large locally advanced breast cancer.
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      • Sanchez AM
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      New challenges in multimodal workout of locally advanced breast cancer.
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      • Oakman C
      • et al.
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      However, it has also been shown that elderly LABC also had better biological characteristics such as high hormone receptor positivity, good differentiation and low HER 2 expression. Thus, patients could still benefit from endocrine therapy and achieve long-term survival.
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      Currently, there is still a lack of standard treatment protocols or guidelines for elderly LABC, and its treatment is generally derived from early or more advanced breast cancer.
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      Therefore, there is an urgent need for a specific prognostic assessment tool to obtain more accurate prognostic information to guide follow-up and treatment of elderly LABC.
      In this study, we developed the first survival prognostic prediction model for elderly LABC. As a statistical model, nomogram has been widely used to quantify risk, predict prognosis, stratify risk and guide treatment. It has been proposed as a new method or standard because of its comprehensiveness and excellent predictive performance.
      • Iasonos A
      • Schrag D
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      How to build and interpret a nomogram for cancer prognosis.
      However, such model has never been built in elderly LABC. To avoid under- or over-fitting the model, we performed screening of prognostic correlates by Cox regression, BSR and LASSO regression, and the results showed that the model screened based on LASSO regression had the best fit. Previous studies have shown that traditional prognostic predictors were still valid for LABC.
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      • Giordano SH.
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      • Hornova J
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      • Majkova P
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      Locally advanced breast cancer in elderly patients.
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      Thus, we selected widely recognized clinicopathological features for analysis and found that age, marriage, T stage, N stage, histological grade, ER, PR, surgery and radiotherapy were all independent prognostic factors affecting elderly LABC. Previous studies have shown that marriage was a predictor of breast cancer survival, both in early and late stages.
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      • Wirth LS
      • Clancy JM
      • Schwartz T.
      The effect of marital status on breast cancer-related outcomes in women under 65: A SEER database analysis.
      • Liu YL
      • Wang DW
      • Yang ZC
      • et al.
      Marital status is an independent prognostic factor in inflammatory breast cancer patients: an analysis of the surveillance, epidemiology, and end results database.
      • Ding W
      • Ruan G
      • Lin Y
      • Zhu J
      • Tu C
      • Li Z.
      Dynamic changes in marital status and survival in women with breast cancer: a population-based study.
      And our study found a 1.17-fold increase in the risk of death for those without a partner at diagnosis, which was fully consistent with the finding (HR = 1.18) of Hinyard L et al.
      • Hinyard L
      • Wirth LS
      • Clancy JM
      • Schwartz T.
      The effect of marital status on breast cancer-related outcomes in women under 65: A SEER database analysis.
      The absence of a spouse may have many impacts on patient psychology, finances, support, and care, especially in elderly LABC, which can lead to barriers to their treatment.
      • Biganzoli L
      • Wildiers H
      • Oakman C
      • et al.
      Management of elderly patients with breast cancer: updated recommendations of the International Society of Geriatric Oncology (SIOG) and European Society of Breast Cancer Specialists (EUSOMA).
      ,
      • Hinyard L
      • Wirth LS
      • Clancy JM
      • Schwartz T.
      The effect of marital status on breast cancer-related outcomes in women under 65: A SEER database analysis.
      • Liu YL
      • Wang DW
      • Yang ZC
      • et al.
      Marital status is an independent prognostic factor in inflammatory breast cancer patients: an analysis of the surveillance, epidemiology, and end results database.
      • Ding W
      • Ruan G
      • Lin Y
      • Zhu J
      • Tu C
      • Li Z.
      Dynamic changes in marital status and survival in women with breast cancer: a population-based study.
      In addition, WP Tew et al. also supported the use of molecular subtypes, T and N stage as treatment selection criteria.
      • Tew WP
      • Muss HB
      • Kimmick GG
      • Von Gruenigen VE
      • Lichtman SM.
      Breast and ovarian cancer in the older woman.
      Based on the results of these previous studies and clinical practice, we identified these variables to develop the nomogram. After rigorous evaluation and validation, our nomogram showed good discrimination, accuracy and clinical usefulness. Also, for ease of use, we created a web version to allow users to quickly obtain their prognostic information after entering the relevant parameters.
      After quantifying risk through the nomogram, accurate risk stratification for elderly LABC patients was also important as a prerequisite for developing follow-up and treatment strategies. However, the nomogram could only predict survival without risk stratification, which would hinder our stratified management. Despite the TNM stage, it lacked other individualized clinicopathological information and thus the stratification may not be accurate enough. Thus, we constructed a new risk stratification. Kaplan-Meier curves showed that patients in the low-, medium-, and high-risk groups had independent and significantly different OS from each other under the new risk stratification, whereas the current TNM staging system failed to do so.
      • Tew WP
      • Muss HB
      • Kimmick GG
      • Von Gruenigen VE
      • Lichtman SM.
      Breast and ovarian cancer in the older woman.
      Therefore, the new risk stratification might be more appropriate for elderly LABC patients and informed the hierarchical management of clinical practice.
      This study has several limitations. First, retrospective data were inevitably biased. Second, the SEER database lacked some variables such as Ki-67 and lymphovascular invasion, which limited some of our analyses. In addition, chemotherapy was not specified as neoadjuvant or adjuvant, and radiotherapy was not marked for dose and target area. Third, the SEER data were collected from multiple centers in the United States, and the performance of the model for other countries still needed further validation. Fourth, the nomogram may become less accurate over time due to a range of reasons (eg, the emergence of novel drugs, changes in the natural history of breast cancer, etc.), which is common to all models and thus still requires continuous improvement and development.

      Conclusions

      In this study, we developed and validated a survival prognostic nomogram and established a new risk stratification for elderly LABC patients. As the first predictive tool for elderly LABC, the nomogram covering both clinicopathological and treatment information can accurately predict individualized prognosis and also provide a useful reference when weighing treatment options, developing follow-up plans or grading patient management. Meanwhile, the online version makes it easier and faster to obtain prognostic information.

      Clinical Practice Points

      • Elderly LABC is a heterogeneous disease with multiple clinical manifestations and a relatively poor prognosis. Currently, the precise management of elderly LABC remains an important clinical challenge. However, few studies have focused on its prognosis.
      • To accurately predict individualized prognosis, we constructed a nomogram for predicting OS of elderly LABC using age, marital status, T stage, N stage, histological grade, ER, PR, surgery, radiotherapy and chemotherapy. To facilitate communication and use, we have also created a web version of this model (https://impcofmxd.shinyapps.io/ElderlyLABC/).
      • As the first predictive tool for elderly LABC, this nomogram covering both clinicopathological and treatment information can accurately predict individualized prognosis and also provide a useful reference when weighing treatment options, developing follow-up plans or grading patient management.

      Data availability statement

      The data used in this study were obtained from the Surveillance, Epidemiology, and End Results (SEER) database and were available through the SEER*Stat software.

      Authors' Contributions

      Conception and design: X Meng, Y Guo; Acquisition of data: X Meng, X Chang; Analysis and interpretation of data: X Meng, Y Guo, X Wang; Drafting of the manuscript: All authors; Critical revision of the manuscript for important intellectual content: All authors; Final approval of manuscript: All authors.

      Disclosure

      The authors have no conflicts of interest to declare.

      Appendix. Supplementary materials

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