A nomogram for survival prediction in 275,812 U.S. patients with breast cancer: a population-based cohort study based on the SEER database.

Background: Nomograms can assess the risk of clinicopathological features by quantifying the biological and clinical variables of cancer patients. However, the nomogram based on significant factors that influence the survival of breast cancer in a large population has been rarely explored. This study was to investigate the predictive effectiveness of a nomogram for the survival of patients with breast cancer.
Methods: Demographic and clinical data of 275,812 breast cancer patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. All patients aged ≥20 years in this retrospective cohort study were classified as two groups in a random manner, namely the training set (n=193,069) and validation set (n=82,743). The outcomes of our study were the 3- and 5-year survival of breast cancer. The potential predictors of cancer mortality were screened by univariate and multivariable Cox regression analyses. The nomogram was conducted based on the predictors. Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curve was utilized to evaluate the performance of the nomogram.
Results: The age at diagnosis, race, marital status, tumor size, first malignant primary indicator, American Joint Committee on Cancer (AJCC) T stage, M stage, tumor grade, and number of malignant tumors were independent predictors for the death of patients with breast cancer. The C-indexes of the training set and the validation set were 0.782 and 0.778, respectively. The area under the curve (AUC) values of the nomogram for predicting the 3- and 5-year survival of breast cancer were 0.770 and 0.756, respectively. Furthermore, the C-index values of our nomogram were 0.816, 0.775, 0.773, 0.734, and 0.750 for predicting survival in Asian, White, Hispanic, American Indian, and Black populations, respectively. Conclusions: The nomogram may have predictive performance for predicting the 3- and 5-year survival of breast cancer patients, and future studies need to validate our findings. 2022 Gland Surgery. All rights reserved.

Introduction

Breast cancer is one of the most lethal malignant tumors and has become the second leading cause of female death (1). It is estimated that about 287,850 females have suffered from breast cancer and approximately 43,250 patients have died from the cancer in 2022 so far (1). With advancements in surgery, chemotherapy, radiation, and hormone therapy, the survival of breast cancer has been improved in the past few decades (2-4). The improvement of multimodal therapies is significant for the survival of cancer patients, with a 5-year relative survival over 90% in developed countries (5). However, these women still have a poor prognosis worldwide.

Nomograms, a statistic-based tool, can assess the risk of clinicopathological features by quantifying the biological and clinical variables of patients with cancer (6). To date, nomograms have been widely applied in the personalized prediction of cancer, such lung, cervical, prostate, and hepatocellular carcinomas (7-11). To the best of our knowledge, several studies have reported risk factors associated with breast cancer (12-17). However, the nomogram based on significant factors that influence the survival of breast cancer in a large population has been rarely explored. Huang et al. (18) reported a nomogram to assess the overall mortality risk in young breast cancer patients. Johnson et al. (19) mentioned that the incidence of breast cancer in adolescents and young adults is relatively low. It is of clinical significance to establish a model to predict the mortality risk of all breast cancer patients.

Herein, we attempted to investigate the predictive factors for the death of breast cancer patients, and develop and validate a nomogram based on the Surveillance, Epidemiology, and End Results (SEER) database, with the aim of reliably predicting the 3- and 5-year survival of breast cancer patients. In addition, we further explored the predictive performance of our nomogram in different ethnicities. We present the following article in accordance with the TRIPOD reporting checklist (available at https://gs.amegroups.com/article/view/10.21037/gs-22-321/rc).

Methods

Study design and population

In this retrospective cohort study, data on patients with breast cancer were extracted from the SEER database, a public registry from the National Cancer Institute (NCI), which covers approximately 27.8% of the U.S. cancer population (20). A total of 275,812 breast cancer patients aged 18 years and over were included. These patients were diagnosed as breast cancer via histopathological examination between 1975 and 2015. The study was carried out in accordance with the Declaration of Helsinki (as revised in 2013).

Study variables

Basic information on patients was collected, including the age at diagnosis (20–39, 40–59, 60–79, or ≥80 years), year of diagnosis, race (American Indian, Asian, Black, Hispanic, and White), marital status (single, married, divorced/separated, or widowed), vital status (alive or dead), American Joint Committee on Cancer (AJCC) T stage (Tis & T1, T2, T3, or T4), N stage (N0, N1, N2, or N3), grade (I, II, III, or IV), type of reporting source item (hospital inpatients or others), regional nodes positive (no or yes), tumor size (<4 or ≥4 cm), laterality (right or left), first malignant primary indicator (no or yes), and number of malignant tumors (1, 2, or ≥3). The outcomes of our study were the 3- and 5-year survival rates of breast cancer patients. The follow-up duration was 5 years. The follow-up was terminated when the death occurred during the follow-up period.

Establishment and validation of the nomogram

All eligible patients were randomly classified as two groups in a random manner, namely the training set (n=193,069) and validation set (n=82,743). Univariate and multivariate Cox regression analyses were used to assess the predictors of cancer mortality using the training set. The regression model was recalculated using significant variables from the multivariate Cox regression analysis. The nomogram was drawn to predict the 3- or 5-year survival of patients.

Harrell’s concordance index (C-index) was utilized to assess the performance of the nomogram (no predictive power: C=0.50, low predictive power: 0.51–0.70, moderate predictive power: 0.71–0.90, and high predictive power: >0.90) (21). The discriminative ability of the nomogram was evaluated using the receiver operating characteristic (ROC) and calibration curves.

Statistical analysis

Normal distributed measurement data were compared through one-way analysis of variance and shown as the mean ± standard deviation (), and those with skewed distribution were compared via the rank-sum test and presented as the median and quartile [M (Q1, Q3)]. The χ2 test or Fisher’s exact test was utilized for comparisons between enumeration data that were expressed as number and proportion [n (%)]. R software (version 4.0.2, The R Foundation for Statistical Computing, Vienna, Austria) and SAS software (version 9.4, SAS Institute Inc., USA) were used for statistical analysis. Two-sided P<0.05 was deemed statistically significant.

Results

The characteristics of patients with breast cancer

Totally 275,812 women with breast cancer who were registered in the SEER database were enrolled in this study. Among them, 193,069 cases were in the training set, with a mean survival time of 42.22±21.36 months. The ages at diagnosis were 20–39 (n=9,329, 4.83%), 40–59 (n=79,750, 41.31%), 60–79 (n=89,615, 46.42%), and ≥80 (n=14,375, 7.45%) years. In terms of marital status, 58.87% (n=113,668) of cases were married, 15.42% (n=29,768) were single, 13.13% (n=25,347) were widowed, and 12.58% (n=24,286) were divorced or separated.

The comparison of the baseline characteristics of women with breast cancer between the training and validation sets is shown in . There were no differences between the two sets regarding age at diagnosis (20–39, 40–59, 60–79, or ≥80 years), year of diagnosis (2010, 2011, 2012, 2013, 2014, or 2015), race (American Indian, Asian, Black, Hispanic, and White), marital status (single, married, divorced/separated, or widowed), AJCC T stage (Tis & T1, T2, T3, or T4), N stage (N0, N1, N2, or N3), grade (I, II, III, or IV), type of reporting source item (hospital inpatients or others), regional nodes positive (no or yes), tumor size (<4 or ≥4 cm), laterality (right or left), first malignant primary indicator (no or yes), number of malignant tumors (1, 2, or ≥3), and vital status (alive or dead), all with P>0.05.

Table 1

The baseline characteristics of patients with breast cancer between the two groups

Variables	Total (n=275,812)	Groups		Statistics	P
		Training set (n=193,069)	Validation set (n=82,743)
Age at diagnosis (years), n (%)				Z=1.070	0.285
20–39	13,381 (4.85)	9,329 (4.83)	4,052 (4.90)
40–59	113,673 (41.21)	79,750 (41.31)	33,923 (41.00)
60–79	128,145 (46.46)	89,615 (46.42)	38,530 (46.57)
≥80	20,613 (7.47)	14,375 (7.45)	6,238 (7.54)
Year of diagnosis, n (%)				Z=−1.684	0.092
2010	42,881 (15.55)	29,905 (15.49)	12,976 (15.68)
2011	44,062 (15.98)	30,759 (15.93)	13,303 (16.08)
2012	45,105 (16.35)	31,568 (16.35)	13,537 (16.36)
2013	46,833 (16.98)	32,832 (17.01)	14,001 (16.92)
2014	47,397 (17.18)	33,296 (17.25)	14,101 (17.04)
2015	49,534 (17.96)	34,709 (17.98)	14,825 (17.92)
Race, n (%)				χ2=3.244	0.518
American Indian	1,467 (0.53)	1,042 (0.54)	425 (0.51)
Asian	23,857 (8.65)	16,777 (8.69)	7,080 (8.56)
Black	28,900 (10.48)	20,267 (10.50)	8,633 (10.43)
Hispanic	30,243 (10.97)	21,089 (10.92)	9,154 (11.06)
White	191,345 (69.38)	133,894 (69.35)	57,451 (69.43)
Marital status, n (%)				χ2=6.373	0.095
Single	42,591 (15.44)	29,768 (15.42)	12,823 (15.50)
Married	162,606 (58.96)	113,668 (58.87)	48,938 (59.14)
Separated/divorced	34,694 (12.58)	24,286 (12.58)	10,408 (12.58)
Widowed	35,921 (13.02)	25,347 (13.13)	10,574 (12.78)
T stage, n (%)				Z=0.267	0.790
Tis & T1	168,672 (61.15)	118,114 (61.18)	50,558 (61.10)
T2	83,921 (30.43)	58,670 (30.39)	25,251 (30.52)
T3	16,301 (5.91)	11,488 (5.95)	4,813 (5.82)
T4	6,918 (2.51)	4,797 (2.48)	2,121 (2.56)
N stage, n (%)				Z=−0.692	0.489
N0	186,438 (67.60)	130,400 (67.54)	56,038 (67.73)
N1	65,622 (23.79)	46,094 (23.87)	19,528 (23.60)
N2	14,851 (5.38)	10,386 (5.38)	4,465 (5.40)
N3	8,901 (3.23)	6,189 (3.21)	2,712 (3.28)
Tumor size (cm), n (%)				χ2=0.485	0.486
<4	9,388 (3.40)	6,602 (3.42)	2,786 (3.37)
≥4	26,6424 (96.60)	186,467 (96.58)	79,957 (96.63)
Grade, n (%)				Z=−1.055	0.292
I	63,996 (23.20)	44,701 (23.15)	19,295 (23.32)
II	122,430 (44.39)	85,699 (44.39)	36,731 (44.39)
III	88,632 (32.13)	62,154 (32.19)	26,478 (32.00)
IV	754 (0.27)	515 (0.27)	239 (0.29)
Type of reporting source item, n (%)				χ2=0.001	0.986
Hospital inpatients	263,759 (95.63)	184,631 (95.63)	79,128 (95.63)
Others	12,053 (4.37)	8,438 (4.37)	3,615 (4.37)
Regional nodes positive, n (%)				χ2=0.988	0.320
No	189,198 (68.60)	132,328 (68.54)	56,870 (68.73)
Yes	86,614 (31.40)	60,741 (31.46)	25,873 (31.27)
Laterality, n (%)				χ2=1.076	0.300
Left	139,797 (50.69)	97,983 (50.75)	41,814 (50.53)
Right	136,015 (49.31)	95,086 (49.25)	40,929 (49.47)
First malignant primary indicator, n (%)				χ2=0.784	0.376
No	39,445 (14.30)	27,537 (14.26)	11,908 (14.39)
Yes	236,367 (85.70)	165,532 (85.74)	70,835 (85.61)
Malignant tumors, n (%)				Z=0.437	0.662
1	210,697 (76.39)	147,513 (76.40)	63,184 (76.36)
2	53,762 (19.49)	37,699 (19.53)	16,063 (19.41)
≥3	11,353 (4.12)	7,857 (4.07)	3,496 (4.23)
Vital status, n (%)				χ2=0.415	0.519
Alive	249,773 (90.56)	174,887 (90.58)	74,886 (90.50)
Dead	26,039 (9.44)	18,182 (9.42)	7,857 (9.50)
Survival time (months), x¯±s	42.26±21.37	42.22±21.36	42.35±21.39	t=−1.450	0.148

Predictors of the mortality of breast cancer patients in the training set

The results of univariate Cox regression analysis are listed in . There were significant differences in age at diagnosis, race, marital status, T stage, N stage, tumor size, grade, type of reporting source item, regional nodes positive, first malignant primary indicator, and the number of malignant tumors (all P<0.001).

Table 2

Univariate Cox regression analysis for the mortality of breast cancer patients

Variables	β	S.E	χ2	P	HR	95% CI
						Lower	Upper
Age at diagnosis (years)
20–39					Ref
40–59	−0.439	0.037	139.328	<0.001	0.645	0.600	0.694
60–79	0.017	0.036	0.215	0.643	1.017	0.948	1.091
≥80	1.202	0.038	1,015.921	<0.001	3.325	3.088	3.580
Year of diagnosis
2010					Ref
2011	−0.013	0.021	0.375	0.540	0.987	0.947	1.029
2012	−0.046	0.023	3.918	0.048	0.955	0.912	0.999
2013	−0.037	0.026	2.042	0.153	0.964	0.917	1.014
2014	−0.031	0.030	1.087	0.297	0.970	0.915	1.028
2015	−0.044	0.038	1.330	0.249	0.957	0.888	1.031
Race
Asian					Ref
White	0.465	0.041	130.719	<0.001	1.591	1.470	1.723
Hispanic	0.488	0.034	204.199	<0.001	1.628	1.523	1.741
American Indian	0.729	0.096	57.237	<0.001	2.073	1.716	2.503
Black	0.928	0.038	597.119	<0.001	2.531	2.349	2.726
Marital status
Married					Ref
Separated/divorced	0.396	0.023	296.106	<0.001	1.486	1.420	1.555
Single	0.443	0.021	433.584	<0.001	1.557	1.493	1.623
Widowed	1.029	0.018	3,130.847	<0.001	2.797	2.698	2.900
T stage
Tis & T1					Ref
T2	0.833	0.017	2,424.379	<0.001	2.300	2.225	2.378
T3	1.391	0.024	3,333.377	<0.001	4.019	3.834	4.213
T4	2.220	0.026	7,186.235	<0.001	9.204	8.744	9.689
N stage
N0					Ref
N1	0.775	0.017	2,028.604	<0.001	2.171	2.099	2.245
N2	1.332	0.024	3,126.498	<0.001	3.787	3.615	3.968
N3	1.858	0.025	5,660.915	<0.001	6.410	6.107	6.728
Tumor size (cm)
<4					Ref
≥4	1.015	0.065	246.547	<0.001	2.760	2.432	3.133
Grade
I					Ref
II	0.411	0.024	293.627	<0.001	1.508	1.439	1.581
III	1.079	0.023	2,175.440	<0.001	2.942	2.812	3.079
IV	1.278	0.099	165.585	<0.001	3.588	2.954	4.359
Type of reporting source item
Hospital inpatients					Ref
Other	−0.190	0.039	23.731	<0.001	0.827	0.766	0.893
Regional nodes positive
No					Ref
Yes	1.024	0.015	4,726.128	<0.001	2.785	2.705	2.867
Laterality
Left					Ref
Right	−0.023	0.015	2.455	0.117	0.977	0.949	1.006
First malignant primary indicator
No					Ref
Yes	−0.491	0.018	730.276	<0.001	0.612	0.590	0.634
Malignant tumors
1					Ref
2	0.481	0.017	807.050	<0.001	1.618	1.565	1.672
≥3	0.854	0.027	970.342	<0.001	2.348	2.225	2.478

S.E, standard error; HR, hazard ratio; CI, confidence interval.

Multivariate Cox regression analysis was used to assess the predictive factors of the mortality of patients with breast cancer, and the details are listed in . The findings showed that compared with women aged 20–39 years, the risk of cancer mortality in women aged 60–79 years [hazard ratio (HR) =1.484, 95% confidence interval (CI): 1.380 to 1.596] and ≥80 years (HR =3.872, 95% CI: 3.575 to 4.195) was higher, while the risk of cancer mortality in those aged 40–59 years was lower (HR =0.856, 95% CI: 0.796 to 0.922). The mortality risks of White (HR =1.456, 95% CI: 1.362 to 1.558), Hispanic (HR =1.462, 95% CI: 1.349 to 1.583), American Indian (HR =1.843, 95% CI: 1.526 to 2.227), and Black (HR =1.924, 95% CI: 1.785 to 2.074) patients were higher than Asian patients, respectively. Compared with married cases, the mortality risks of single, divorced/separated, or widowed patients were higher, with HR values of 1.285 (95% CI: 1.228 to 1.344), 1.352 (95% CI: 1.295 to 1.411), and 1.530 (95% CI: 1.469 to 1.593), respectively. Breast cancer patients with tumor size ≥4 cm had a high mortality risk compared to those with tumor size <4 cm (HR =1.322, 95% CI: 1.163 to 1.503). The first malignant primary indicator (HR =1.064, 95% CI: 1.011 to 1.120), T stage (T2, HR =1.608, 95% CI: 1.551 to 1.667; T3, HR =2.328, 95% CI: 2.210 to 2.453; T4, HR =4.110, 95% CI: 3.879 to 4.354), N stage (N2, HR =1.512, 95% CI: 1.315 to 1.738; N3, HR =2.189, 95% CI: 1.902 to 2.519), and tumor grade (II, HR =1.167, 95% CI: 1.112 to 1.224; III, HR =2.037, 95% CI: 1.942 to 2.136; IV, HR =2.411, 95% CI: 1.983 to 2.931) were associated with the mortality of women with breast cancer. Patients with positive regional nodes had a higher risk of death than in patients without (HR =1.596, 95% CI: 1.401 to 1.819). When the number of malignant tumors was 2 (HR =1.577, 95% CI: 1.510 to 1.646) or ≥3 (HR =2.223, 95% CI: 2.077 to 2.380), patients had a higher risk of death.

Table 3

Multivariate Cox regression analysis for the mortality of breast cancer patients

Variables	β	S.E	χ2	P	HR	95% CI
						Lower	Upper
Age at diagnosis (years)
20–39					Ref
40–59	−0.155	0.037	17.099	<0.001	0.856	0.796	0.922
60–79	0.395	0.037	112.671	<0.001	1.484	1.380	1.596
≥80	1.354	0.041	1,103.175	<0.001	3.872	3.575	4.195
Race
Asian					Ref
White	0.376	0.034	120.320	<0.001	1.456	1.362	1.558
Hispanic	0.379	0.041	86.893	<0.001	1.462	1.349	1.583
American Indian	0.612	0.096	40.232	<0.001	1.843	1.526	2.227
Black	0.654	0.038	291.673	<0.001	1.924	1.785	2.074
Marital status
Married					Ref
Separated/divorced	0.250	0.023	116.634	<0.001	1.285	1.228	1.344
Single	0.301	0.022	191.052	<0.001	1.352	1.295	1.411
Widowed	0.425	0.021	418.100	<0.001	1.530	1.469	1.593
Tumor size (cm)
<4					Ref
≥4	0.279	0.066	18.150	<0.001	1.322	1.163	1.503
First malignant primary indicator
No					Ref
Yes	0.062	0.026	5.636	0.018	1.064	1.011	1.120
Regional nodes positive
No					Ref
Yes	0.468	0.067	49.407	<0.001	1.596	1.401	1.819
T stage
Tis & T1					Ref
T2	0.475	0.018	665.191	<0.001	1.608	1.551	1.667
T3	0.845	0.027	1,010.133	<0.001	2.328	2.210	2.453
T4	1.413	0.029	2,299.684	<0.001	4.110	3.879	4.354
N stage
N0					Ref
N1	0.078	0.068	1.332	0.249	1.082	0.947	1.236
N2	0.413	0.071	33.834	<0.001	1.512	1.315	1.738
N3	0.784	0.072	119.696	<0.001	2.189	1.902	2.519
Grade
I					Ref
II	0.154	0.024	40.042	<0.001	1.167	1.112	1.224
III	0.711	0.024	851.166	<0.001	2.037	1.942	2.136
IV	0.880	0.100	77.918	<0.001	2.411	1.983	2.931
Malignant tumors
1					Ref
2	0.455	0.022	431.758	<0.001	1.577	1.510	1.646
≥3	0.799	0.035	529.101	<0.001	2.223	2.077	2.380

S.E, standard error; HR, hazard ratio; CI, confidence interval.

The predictive performance of the nomogram

The variables with statistical significance obtained from the multivariate Cox regression analysis were utilized to construct a nomogram to predict the 3- and 5-year survival rates of breast cancer patients, including age at diagnosis, marital status, race, T stage, N stage, grade, regional nodes positive, first malignant primary indicator, tumor size, and number of malignant tumors (). The C-index of our nomogram using the training set was 0.782, with a standard error of 0.002. The C-index in the validation set was 0.778, with a standard error of 0.003. The ROC curves on the 3- and 5-year survival prediction were shown in . The area under the curve (AUC) values of the nomogram for predicting the 3- and 5-year survival of breast cancer were 0.770 and 0.756, respectively. There were no differences between the training and validation sets (Z=0.383, P=0.998), indicating that the nomogram had good predictive power. The calibration curves of the nomogram for predicting the 3- and 5-year survival rates of breast cancer patients are shown in (the training set) and (the validation set).

Figure 1

The nomogram for the 3- and 5-year survival prediction among breast cancer patients.

Figure 2

The ROC curves on the 3- and 5-year survival prediction of breast cancer. (A) Three-year survival; (B) 5-year survival. ROC, receiver operating characteristic.

Figure 3

The calibration curves of the nomogram for predicting the survival rates of breast cancer patients in the training set. (A) Three-year survival; (B) 5-year survival.

Figure 4

The calibration curves of the nomogram for predicting the survival rates of breast cancer patients in the validation set. (A) Three-year survival; (B) 5-year survival.

The predictive effect of the nomogram in different races

The patients from the validation set were classified into 5 risk subgroups based on race. As shown in , the C-index values of our nomogram in the Asian, White, Hispanic, American Indian, and Black groups were 0.816, 0.775, 0.773, 0.734, and 0.750, respectively. The predictive effect of the nomogram in Asian cases was superior to that in White (Z=3.927, P<0.001), Hispanic (Z=3.196, P=0.001), American Indian (Z=2.253, P=0.024), and Black (Z=5.407, P<0.001) cases with breast cancer.

Table 4

The predictive performance of the nomogram in different races

Race	C-index	S.E	Z	P
Asian	0.816	0.010	–	–
White	0.775	0.003	3.927	<0.001
Hispanic	0.773	0.009	3.196	0.001
American Indian	0.734	0.035	2.253	0.024
Black	0.750	0.007	5.407	<0.001

S.E, standard error.

Discussion

In the present study, 275,812 patients with breast cancer were screened from the SEER database. We found that the age at diagnosis, marital status, race, T stage, N stage, grade, regional nodes positive, first malignant primary indicator, tumor size, and number of malignant tumors were independent predictors for the death of patients with breast cancer. A nomogram that contained the predictive factors associated with breast cancer death was established. The C-indexes of the nomogram for the training and validation sets were 0.782 and 0.778, respectively. Our findings indicated that our nomogram had predictive ability for predicting the 3- and 5-year survival rates of breast cancer patients.

Previous studies have reported that nomograms play an important role in the personalized prediction of cancer (22-25), which helps clinicians optimize the therapeutic protocols of patients on the basis of individual information. The nomogram is a visual tool to effectively reflect the results of Cox regression analysis. The most valuable aspect of a nomogram is in predicting the outcomes, and the length of the lines can be used to indicate the impact of different variables on the outcomes, as well as the effects of various values of these variables on the outcomes. The larger the C-index, the more accurate the predictive effectiveness of the nomogram (26). However, the predictive power of the nomogram is limited in predicting the mortality risk of breast cancer patients, especially for different ethnicities. In this study, we developed a nomogram according to the related predictive factors from the general information and tumor characteristics of patients in the SEER database to estimate the 3- and 5-year survival rates of patients with breast cancer, and further assessed the predictive performance of the nomogram for the survival of breast cancer patients with different ethnicities.

The predictive factors were considered in the nomogram on the basis of our findings obtained from the univariate and multivariate analyses. We discovered that the risk of cancer death in patients aged 40–59, 60–79, and >80 years was higher than that in the 20–39 years age group. It was suggested that the risk of dying from breast cancer gradually increased with the increase of age. This may be related to less aggressive treatments, poor endurance of stress, impaired compensatory mechanisms, or other chronic diseases in the elderly, whereas younger patients may often exhibit more aggressive biological behaviors of malignant tumors (27). Cases with malignant tumors had a higher risk of death than cases with in situ tumors. The occurrence of lymph node metastases in patients resulted in a higher risk of death than in patients without lymph node metastases. Early evidence showed that lymph node metastases is a common clinical feature in the progression of breast cancer (28-30). Compared with patients with grade I tumors, the mortality risks of those with grade II, III, and IV tumors were higher, with HR values of 1.167, 2.037, and 2.411, respectively. Our results found that tumor size ≥4 cm could increase the mortality risk of breast cancer. When the number of malignant tumors was over 2, patients had a higher risk of death. It was indicated that high tumor grade, large tumor size, and a large number of tumors were associated with cancer death.

Validation of the nomogram was carried out through randomly selecting 275,812 patients with breast cancer. The nomogram showed greater discrimination for the prediction of the survival time of patients, and the C-index for the validation set was 0.778, indicating that our nomogram had good effectiveness in predicting the prognosis of breast cancer patients. Furthermore, patients in the validation set were further classified into different ethnicities. Ethnicity is an independent risk factor of the occurrence of breast cancer and affects the prognosis of breast cancer. In the current study, we discovered that the predictive performance of the nomogram in Asian patients was superior to that in other ethnicities, which may suggest that the prognosis of breast cancer is racially heterogeneous. Hence, the specific race-based risk and prognostic prediction model was more precise and personalized for outcome prediction.

This was a population-based study that covered U.S. cancer registry data of high quality. The SEER database incorporates relatively complete follow-up information for breast cancer, indicating that the death-related information is reliable. Nonetheless, some limitations should be noted. More information on systemic therapy, such as targeted therapy records and chemotherapy protocols, was not available in the SEER database, and the predictive factors were only limited to this database. Additionally, this nomogram was developed using retrospective data, and further studies with external validation in large and prospective cohorts are needed.

Conclusions

In summary, we established and validated a nomogram based on the SEER database to predict the 3- and 5-year survival of breast cancer patients. Our nomogram had the effectiveness for predicting survival, especially in the Asian population, which may provide clinicians with useful guidelines for the individualized treatment of patients.

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