Skip to main content
Download PDF

Predictive models for overall survival in breast cancer patients with a second primary malignancy: a real-world study in Shanghai, China

BMC Women's Health volume 22, Article number: 498 (2022) Cite this article

Abstract

Background

The incidents of second primary malignancy (SPM) is increasing among breast cancer (BC) patients with long-term progression, adversely affecting survival. The purpose of this study was to screen independent overall survival (OS) risk factors and establish nomograms to predict the survival probabilities of BC patients with SPM.

Method

A total of 163 BC patients with SPM were recruited during 2002–2015 from a total of 50 hospitals in Shanghai, China. Two nomograms to predict survival from primary BC and SPM diagnosis were constructed based on independent factors screened from multivariable analysis. The calibration and discrimination of nomograms were calculated in the training and validation cohorts.

Results

The overall survival rates of BC patients with SPM were 88.34%, 64.42% and 54.66% at 5, 10 and 15 years, respectively. Factors of late TNM stage of SPM (HR = 4.68, 95% CI 2.14–10.25), surgery for SPM (HR = 0.60, 95% CI 0.36–1.00), SPM in the colon and rectum (HR = 0.49, 95% CI 0.25–0.98) and thyroid (HR = 0.08, 95% CI 0.01–0.61) independently affected the OS of BC patients with SPM (p < 0.05). In addition, a longer latency (≥ 5 years) was associated with better OS from BC diagnosis (p < 0.001). Older age (≥ 56) was associated with poor OS from SPM diagnosis (p = 0.019). Two nomograms established based on the above factors had better calibration and discrimination.

Conclusion

The TNM stage of SPM, surgery for SPM, SPM sites, latency and age at BC diagnosis are independent factors for survival and the two nomograms may provide more personalized management for BC patients with SPM.

Peer Review reports

Introduction

Breast cancer (BC) is one of the most common cancers worldwide, accounting for nearly a quarter of the newly diagnosed cancer cases in women each year [1, 2]. Owing to the increasing awareness and use of mammography for earlier detection and advanced treatments, such as chemo- and radiotherapy, hormone therapy, and targeted drugs [3], the age-standardized 5-years net survival of female BC patients increased from 2000 to 2014 in most nations but varied from 66.1% in India to 90.2% in the United States [4]. In Southeast Asia, female BC patients in Japan and Korea had higher 5-years survival rates (above 86%), while China had a rate of 83.2%, which was higher than some other developing countries [4].

However, the second primary malignancy (SPM) that occurs after BC affects the long-term progression of BC patients [5]. SPM is an in situ cancer that develops after the first primary malignancy and does not metastasize or recur [6]. Due to factors such as side effects of cancer treatment, genetic mutations and environmental pollution, the incidence of SPM is rising [7]. A previous study revealed that SPM is one of the leading causes of death in BC patients with long-term survival rates [8]. A variety of studies have evaluated the risk factors for the occurrence of SPM, including age [6], different sites [9, 10], the expression of genes [11], the status of receptors [12, 13], and different therapeutic strategies, such as radiotherapy [6, 14] and chemotherapy [15]. However, few studies have illustrated the relationship between SPM and the survival of BC patients. A previous study reported that female BC patients with SPM tended to present poorer outcomes than those who only had BC in general [15]; however, the factors influencing the survival of BC patients with SPM remain to be clarified. Knowledge of the latency period, the time from the date of primary malignancy diagnosis to the SPM diagnosis, has been hypothesized to influence survival outcomes in malignant astrocytoma [16] and lung cancer [17]. In patients with malignant astrocytoma, those who developed an SPM at 11 months or later had better outcomes, and the risk of overall mortality decreased as the latency time increased [16]. Another study proposed that patients with primary lung cancer had a lower mortality when SPM was diagnosed earlier [17]. To our knowledge, there has been no discussion about the death risk of latency in patients with primary BC.

The nomogram has been widely used in the clinic as a prognostic prediction tool in oncology [18]. It can simplify the statistical predictive models in clinical decision making, by estimating the single numerical probability of an even, for instance death, for an individual patient [19]. Hence, the aim of this study was to screen independent factors associated with the survival of BC patients with SPM. Then, we predicted the survival probabilities of BC patients with SPM by establishing nomogram models. We believe the findings will guide individualized clinical management for professional and comprehensive treatment of BC patients with SPM.

Materials and methods

Data collection

Information on demographics, breast cancer diagnosis, second primary malignancy diagnosis, survival and other data were collected from 2002 to 2015 through a total of 50 hospitals in Shanghai, China. Shanghai is the largest metropolis with 16 districts in China and has witnessed the rapid development of China [20]. The tumour reporting system of the Cancer Registry in Shanghai was established in 2002; since then, the data of cancer patients have been utilized [21]. The date of diagnosis of breast cancer was set as the starting point of observation, and the end point was set on December 31, 2015. Patients received follow-up surveys every 6 months after providing informed consent to assess the survival status via home visit or telephone according to standard epidemiologic procedures [20, 22]. These patients were followed up until death or June 30, 2017, whichever came first. Data quality was checked and evaluated according to the criteria of the Chinese Cancer Registration [23] and IARC/IACR [23].

Study participants

A total of 181 women with primary, nonmetastatic breast cancer and SPM were initially deemed eligible during 2002–2015 in Shanghai. Among these patients, 25 patients were diagnosed with first breast cancer before 2002 but were diagnosed with second primary malignancy between 2002 and 2015. Each of these participants was identified according to the International Classification of Diseases, 10th edition (ICD-10). We excluded 3 male breast cancer patients because of the low number of patients. Three patients who developed a third primary malignancy were excluded, which was beyond the scope of this study. Twelve patients were also excluded, as the IACR/IARC rules (accepted internationally) suggest that the time between the first and second primary malignancies should be more than 6 months [24, 25]. Finally, a total of 163 female breast cancer patients with a second primary malignancy were included in this study.

The quality assessment was conducted according to the applicable Enhancing the Quality and Transparency of Health Research (EQUATOR) reporting guidelines [26].

Variables

The following demographic and clinical variables were analysed in our study: age, hospital grade, surgery, American Joint Committee on Cancer (AJCC) 6th Tumour node metastasis (TNM) staging classification [27], latency (the time interval between the date of primary breast cancer diagnosis and the date of second primary malignancy diagnosis [6]), and the sites of SPM.

Statistical analysis

The primary outcomes of this study were overall survival (OS) and cancer-specific survival (CSS). OS was defined as death due to any cause, and CSS was defined as death due to breast cancer or SPM. The survival time was calculated in months from the date of BC diagnosis or SPM diagnosis to the endpoint (death or censoring). Chi-square or Fisher’s exact tests allowed for comparisons between categorical variables. The Kaplan–Meier method was used to compare survival curves with the log-rank test. Univariate and multivariate Cox regression analyses were performed to estimate the risk of survival with hazard ratios (HRs) and associated 95% confidence intervals (CIs). In all Cox regression models, the proportional hazards (PH) assumption was evaluated by a log-minus-log plot, hazard function plot and scaled and unscaled Schoenfeld residuals test. Variables that violated the PH [28] were transformed to a time-varying covariate with the time function and added to the Cox-PH model, and covariates were selected based on variables clinically important and with a p < 0.25 [29] in univariate analysis. To control the impact of confounders, we used the forward stepwise procedure based on the likelihood ratio test.

Two nomogram models were constructed to predict the OS of BC patients with SPM based on factors selected from multivariable Cox regression analysis with a p value less than 0.05. Model 1 was established to predict OS after BC diagnosis. Model 2 was established to predict OS after SPM diagnosis. A total of 163 patients were randomly divided into a training cohort and validation cohort, and each cohort included half of the patients [30]. The characteristics showed no significance between these two cohorts. Receiver characteristic curves (ROCs) and calibration curves were plotted and calculated to evaluate the calibration for predicting the survival rates of BC patients with SPM.

All statistical analyses and graphical representations were performed using GraphPad Prism (Version 8.0.1, Inc.), Statistical Package for the Social Sciences software version 26.0 (SPSS, Chicago, IL, USA), and R software 3.63 (R Foundation for Statistical Computing). All statistical tests were two-sided, and statistical significance was set at p < 0.05.

Results

Baseline characteristics

A total of 163 women breast cancer patients with SPM were enrolled in this study between 2002 and 2015. These patients were all over 24 years old. The baseline characteristics of the patients are presented in Table 1. The average age of the patients was 56.47 ± 11.62 years old. Among all 163 breast cancer patients, 77 (47.24%) developed SPM within 5 years, while 86 (52.76%) developed SPM beyond 5 years. As shown in Table 1, there were significant differences in surgery for breast cancer (p = 0.001) and SPM sites (p = 0.004). Compared to patients with a latency over 5 years, patients who had a latency less than 5 years received more surgery for breast cancer (61.04% vs. 38.37%, p = 0.001), and more SPMs occurred in the thyroid (28.57% vs. 6.98%, p = 0.004). All SPM sites are presented in Additional file 1. The number of patients with SPM in the colon and rectum ranked first (n = 30), followed by the thyroid (n = 28) and lung and bronchus (n = 24), and each other site included fewer than 10 patients. Thus, the SPM sites in the colon and rectum, thyroid and lung and bronchus were included in further analysis.

Table 1 Demographics and characteristics of 163 breast cancer patients with a second primary malignancy, n (%)
Full size table

Altogether, 45 (27.61%) deaths occurred among 163 patients during the 101.5-months median follow-up. At the end of follow-up, 74 patients had died, among whom 73 patients had died from cancer and only one patient had died from other causes. All patients survived at the 1-year follow-up, and the 5-years, 10-years and 15-years OS rates were 88.34%, 64.42% and 56.44%, respectively (Table 2).

Table 2 Overall survival rate of breast cancer patients who developed a second primary malignancy with different characteristics, n (%)
Full size table

Univariate analysis of factors associated with poor outcomes of breast cancer patients with second primary malignancy

TO screen the risk factors for overall and cancer-specific survival from BC and SPM, we conducted univariate analysis of all variables. As shown in Table 3, the HRs of variables were similar to the OS and CSS both from BC and SPM diagnosis, as there was only one patient who died from noncancer causes. For survival from BC, factors of older age (≥ 56 years old), late TNM stage of SPM (III + IV), nonsurgery for SPM, and SPM occurring in nonthyroidsites were significantly associated with poor survival (p < 0.05). The latency was transformed to a time-varying covariate with the time function. The hazard ratio (HR) for breast cancer patients with a longer latency (> 5 years) was 0.002 (95% CI 0–0.07, p = 0.001) for both OS and CSS, and the interaction between latency and time had an HR of 1.08 (p = 0.001). For survival from SPM, factors of older age at BC (≥ 56 years old) and SPM (≥ 62 years old) diagnosis, late TNM stage of SPM (III + IV), SPM treated in secondary hospital, nonsurgery for SPM, SPM occurring in nonthyroid sites and latency were significantly associated with poor survival (p < 0.05). These significant risk factors for OS were also analysed in Kaplan‒Meier curves, as shown in Fig. 1.

Table 3 Univariate analysis of factors influencing the survival time of breast cancer patients with second primary malignancy
Full size table
Fig. 1
figure 1

Overall survival of breast cancer patients with SPM with different characteristics. A–E Survival from BC diagnosis. F–L Survival from SPM diagnosis. Overall survival with different characteristics in (A, F) age at BC diagnosis, B, H TNM stage of SPM, (C, J) surgery for SPM, D, K SPM in thyroid, E, L latency, G age at SPM diagnosis, I SPM therapeutic hospital grade. BC, breast cancer; SPM, second primary malignancy

Full size image

Multivariate analysis of factors influencing the survival time of breast cancer patients with a second primary malignancy

To determine which factors were independently associated with the overall survival of breast cancer patients with SPM, multivariate Cox regression analysis was applied. The age of BC and SPM diagnosis, TNM stage of SPM, SPM therapeutic hospital grade, surgery for SPM, sites of SPM and latency were selected for models. The final models are shown in Table 4. The independent factors of TNM stage of SPM, surgery for SPM, SPM in colon and rectum and thyroid remained significant in the forward stepwise multivariable model for predicting overall survival from BC and SPM diagnosis. Notably, the late TNM stage of SPM (III + IV) was an independent factor for predicting poor survival from BC (HR = 4.68, 95% CI 2.14–10.25, p < 0.001) and SPM (HR = 5.76, 95% CI 2.65–12.51, p < 0.001). In addition, surgery for SPM, SPM in the colon and rectum or thyroid was a protective factor for survival from BC (HR 0.60, 0.49 and 0.08, respectively; p < 0.05) and SPM (HR 0.58, 0.43 and 0.07, respectively; p < 0.05). In the Cox model with time-varying covariates for survival from BC diagnosis, the longer latency (> 5 years) had an HR of 0.002 (p < 0.001), and the interaction between latency and time had an HR of 1.07 (p = 0.003). In the model for survival from SPM diagnosis, an older age of SPM diagnosis (≥ 56 years) was associated with poor survival after SPM diagnosis (HR = 1.80, 95% CI 1.10–2.83, p = 0.019).

Table 4 Factors influencing the overall survival of breast cancer patients with second primary malignancy by multivariate Cox regression model
Full size table

Nomogram establishment and validation

To establish predictive nomogram models for the survival of BC patients with SPM, we selected the independent factors from multivariate Cox analysis and found no significant collinearity. A total of 163 patients were randomly divided into a training cohort and validation cohort with no significant differences in characteristics (see Additional file 2). Model 1 included possible predictors of TNM stage of SPM, surgery for SPM, sites of SPM and latency to predict OS probabilities after BC diagnosis (Fig. 2A). Model 2 included possible predictors of age at BC diagnosis, TNM stage of SPM, surgery for SPM and sites of SPM to predict OS probabilities after SPM diagnosis (Fig. 2B). For an individual patient, the total nomogram-related score was calculated by summing the points corresponding to the status of the predictors.

Fig. 2
figure 2

Nomograms for overall survival prediction of female BC patients with SPM. A Model 1: nomogram for predicting survival probability after BC diagnosis. The points according to the presence/absence of the categorized variables could be obtained according to the point of the horizontal axes. According to the sum of total points, a vertical line was drawn through the total point axis and survival axes to determine the probabilities of 5-, 10- and 15-years OS. B Model 2: nomogram for predicting 3-, 5- and 10-years OS after SPM diagnosis. BC, breast cancer; TNM, tumour node metastasis; SPM, second primary malignancy; OS, overall survival

Full size image

The calibration curves showed that the nomograms had relatively good consistency between the predicted and observed survival probabilities in the training and validation cohorts in both models (Fig. 3A, B). The ROC curves presented better discriminatory capacity of the nomogram models. In model 1 (Fig. 3C), the areas under the curve (AUCs) were 0.88, 0.82 and 0.89 in the training cohort and 0.89, 0.77 and 0.78 in the validation cohort for 5-, 10- and 15-years OS prediction, respectively. In model 2 (Fig. 3D), the AUCs were 0.88, 0.88 and 0.94 in the training cohort and 0.78, 0.81 and 1.00 in the validation cohort for 3-, 5- and 10-years OS prediction, respectively.

Fig. 3
figure 3

Validation of two nomograms for overall survival prediction of female BC patients with SPM. A Calibration and C ROC curves of 5-, 10- and 15-years OS from BC diagnosis in the training cohort (left) and validation cohort (right). The grey line represents the performance of the ideal nomogram where the predicted probability perfectly corresponds to the observed probability. B Calibration and D ROC curves of 3-, 5- and 10-years OS from SPM diagnosis in the training cohort (left) and validation cohort (right). ROC, receiver operating characteristic; BC, breast cancer; SPM, second primary malignancy; OS, overall survival

Full size image

Discussion

Previous studies have claimed that SPM is one of the leading causes of death in BC patients. Our study found that the survival rate of female BC patients with SPM was lower than that of patients with only BC, which proved the threat of SPM to survival. Furthermore, factors that affected the OS of femaleBC patients with SPM were SPM, including TNM stage, surgery, sites, and latency. In addition, younger age at BC diagnosis predicted poor survival in these participants. Based on these risk factors, two nomograms established to predict survival probabilities after BC and SPM were diagnosed presented better discriminatory capacity, which demonstrated potential predictive value for BC management in the clinic.

Among the patients in our study, those who developed SPM had a survival rate of 75.32%, while those who did not develop SPM had a survival rate of 100% at the 5-years follow-up. In addition, the 10-years and 15-years survival rates of BC patients who developed SPM were decreased to 64.42% and 56.44%, respectively. It has been proven that the survival rate of BC is associated with socioeconomic status and culture [31]. In America, a report based on the Surveillance, Epidemiology, and End Results (SEER) database showed that the 5-years survival rate of female BC patients was 91.1% during 2008–2014 [32]. In China, the 5-years female BC survival rate was 80.4–83.3% from 2005 to 2014, which was relatively lower than that in the US [4]. In our study, the 5-years survival rate of female BC patients was 88.34% during 2002–2015, which is higher than the reported 80.4–83.3%. This discrepancy may be due to the economic advantages and advanced medical care available in Shanghai, but this needs to be further clarified.

In addition, regarding the age of BC patients, a previous study based on SEER found that in the US, 60.3% of female BC patients with SPM were 60 years old and older [6]. In our study, these patients (< 56 years old) accounted for 62.58%, which indicated that BC patients with SPM tend to be younger in China. Interestingly, it is a protective factor compared to those who were younger than 56 years old when predicting the overall survival probability of BC patients after developing SPM.

In this study, we constructed univariable and multivariable analyses as well as well-discriminatory prognostic nomogram models to predict OS in BC patients with SPM. TNM stage is an important predictor of cancer outcomes. Consistent with prior studies in other cancers [33,34,35], we found that late-stage (III + IV) SPM was associated with poor survival in BC patients with SPM. One reason for this result may be a delayed diagnosis of SPM in that patients diagnosed with late-stage SPM may not receive early detection and comprehensive treatment in a timely manner [36]. Evidence-based studies need to be further analysed. This is a reminder that early cancer screening is of importance even for a patient already diagnosed with BC.

Surgery is a vital treatment for cancer therapies. Although a study pointed out that women nursing home residents who received BC surgery had high 1-year mortality [37], our study showed that surgical treatment for primary BC had no significant effect on the OS or CSS for BC patients with SPM. However, receiving surgical treatment for SPM was shown to be protective for survival, which is similar to the findings from a study on nasopharyngeal tumours [38]. Our results may provide a reference for clinicians making treatment decisions for BC patients with SPM.

Different SPM sites were related to significant survival outcomes. In our results, SPM in the colon and rectum and thyroid predicted better survival. Colorectal cancer (CRC) is the third most common cancer worldwide [39] and the top five most common cancers in China [40]. It has been proven that the CRC screening program could improve CRC patient survival in developed and developing countries [41], which provides a reason for our result. In particular, BC patients with SPM in the thyroid had an excellent 15-years OS of 96.43%. Although the incidence of thyroid cancer has increased recently worldwide, thyroid cancer patients still have better survival rates; for instance, the 5-years survival rate of thyroid cancer patients reached 98% in America [42]. It is worth noting that a high cumulative radioiodine dose increased the incidence of SPM in thyroid cancer patients, which is a vital risk factor for death [43]. Therefore, stricter surveillance of BC patients is needed to facilitate early diagnosis and screening of SPM, and more professional and comprehensive treatment should be offered.

The risk effect of latency period on developing SPM has been studied in many cancers, but the association between latency and survival has rarely been studied. Previous studies proved that longer latency was associated with lower mortality in astrocytoma [16] and lung cancer [17]. In BC, our study first pointed out that developing an SPM with a longer latency (more than 5 years) was associated with a decreased risk of all-cause death, which is consistent with related studies of other cancers. A literature showed that adolescents and young adults who developed BC within 5 years had a 2.6-fold increased risk of death [7], however, BC was an SPM rather than a primary cancer in this study.

In the present study, two nomograms were established based on surgical treatment for SPM, SPM sites, TNM stage of SPM, latency, and age of BC diagnosis to facilitate a quantitative assessment of survival of BC patients who developed SPM once their clinical data were available. Internal validation statistically indicated that the current nomograms were accurate in their predictive abilities. Particularly, in predicting BC patients 10-years OS from SPM diagnosis, the AUC reached 0.94 and 1.00 in the training and validation cohorts, respectively. These models would enable a survival prediction of BC patients who developed an SPM, but TNM stage could not be classified in the clinic. Since the factors of latency and surgery were included, the nomogram could longitudinally evaluate overall survival and weight the risk of various management options, including surgery, at optimal time points.

Few studies have illustrated the factors influencing the survival of BC patients with SPM. Our study first identified latency, which affected the overall survival of these patients. It is worth paying attention to latency because longer latency is associated with improved survival. In addition, with respect to exploring survival risk factors for BC patients with SPM, obesity and metabolic diseases should be considered and are worthy of study, as they are important risk factors for cancers including BC [44, 45].

There are several limitations in the present study. First, although our data were collected from 50 hospitals in Shanghai during 2002–2015, the study only included 163 BC female patients with SPM, which was relatively small and affected the calibration of the nomogram models. Second, due to the long-term follow-up, some patient information about surgery for BC was not completed, which resulted in some missing bias. Third, we did not obtain health data related to obesity and metabolic diseases of participants, which may be risk factors for breast cancer [45]. Finally, we evaluated the nomogram models by internal validation, and they need to be externally validated in future real-world studies to assess the accuracy and verify their utility for clinicians.

Conclusion

IN summary, for BC patients with SPM, we determined the SPM factors for TNM stage, surgery, and sites as independent predictors for OS from BC diagnosis and SPM diagnosis. The latency might be used in predicting 5-, 10- and 15-years survival from BC diagnosis. In addition, the age of BC diagnosis might be used in predicting 3-, 5- and 10-years survival from SPM diagnosis. The nomograms may provide a reference for individualized survival prediction and management options for BC patients with SPM.

Availability of data and materials

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.

Abbreviations

BC:

Breast cancer

SPM:

Second primary malignancy

ICD-10:

International Classification of Diseases, 10th edition

AJCC:

American Joint Committee on Cancer

TNM:

Tumour node metastasis

OS:

Overall survival

CSS:

Cancer-specific survival

HRs:

Hazard ratios

CIs:

Confidence intervals

PH:

Proportional hazard

ROCs:

Receiver characteristic curves

References

  1. Parkin DM, Fernández LM. Use of statistics to assess the global burden of breast cancer. Breast J. 2006;12(Suppl 1):S70-80. https://doi.org/10.1111/j.1075-122X.2006.00205.x.

    Article  Google Scholar 

  2. Coughlin SS, Ekwueme DU. Breast cancer as a global health concern. Cancer Epidemiol. 2009;33(5):315–8. https://doi.org/10.1016/j.canep.2009.10.003.

    Article  Google Scholar 

  3. Berry DA, Cronin KA, Plevritis SK, et al. Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med. 2005;353(17):1784–92. https://doi.org/10.1056/NEJMoa050518.

    Article  CAS  Google Scholar 

  4. Allemani C, Matsuda T, Di Carlo V, et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018;391(10125):1023–75. https://doi.org/10.1016/s0140-6736(17)33326-3.

    Article  Google Scholar 

  5. Soerjomataram I, Coebergh JW. Epidemiology of multiple primary cancers. Methods Mol Biol. 2009;471:85–105. https://doi.org/10.1007/978-1-59745-416-2_5.

    Article  Google Scholar 

  6. Li Z, Wang K, Shi Y, et al. Incidence of second primary malignancy after breast cancer and related risk factors-Is breast-conserving surgery safe? A nested case-control study. Int J Cancer. 2020;146(2):352–62. https://doi.org/10.1002/ijc.32259.

    Article  CAS  Google Scholar 

  7. Goldfarb M, Rosenberg AS, Li Q, et al. Impact of latency time on survival for adolescents and young adults with a second primary malignancy. Cancer. 2018;124(6):1260–8. https://doi.org/10.1002/cncr.31170.

    Article  CAS  Google Scholar 

  8. Clarke M, Collins R, Darby S, et al. Effects of radiotherapy and of differences in the extent of surgery for early breast cancer on local recurrence and 15-year survival: an overview of the randomised trials. Lancet. 2005;366(9503):2087–106. https://doi.org/10.1016/s0140-6736(05)67887-7.

    Article  CAS  Google Scholar 

  9. Yesensky J, Kyrillos A, Kuchta K, et al. Risk of development of second primary head and neck cancer following an index breast cancer. Otolaryngol Head Neck Surge Off J Am Acad Otolaryngol Head Neck Surg. 2018;158(2):303–8. https://doi.org/10.1177/0194599817737271.

    Article  Google Scholar 

  10. Vakharia PP, Kelm RC, Orrell KA, et al. Risks for noncutaneous second primary malignancy in cutaneous malignant melanoma survivors: an analysis of data from the surveillance, epidemiology, and end results (SEER) program. Int J Dermatol. 2020;59(4):463–8. https://doi.org/10.1111/ijd.14781.

    Article  Google Scholar 

  11. Ketteler P, Hülsenbeck I, Frank M, et al. The impact of RB1 genotype on incidence of second tumours in heritable retinoblastoma. Eur J Cancer. 2020;133:47–55. https://doi.org/10.1016/j.ejca.2020.04.005.

    Article  CAS  Google Scholar 

  12. Liu J, Jiang W, Mao K, et al. Elevated risks of subsequent endometrial cancer development among breast cancer survivors with different hormone receptor status: a SEER analysis. Breast Cancer Res Treat. 2015;150(2):439–45. https://doi.org/10.1007/s10549-015-3315-5.

    Article  Google Scholar 

  13. Schonfeld SJ, Curtis RE, Anderson WF, et al. The risk of a second primary lung cancer after a first invasive breast cancer according to estrogen receptor status. Cancer Causes Control CCC. 2012;23(10):1721–8. https://doi.org/10.1007/s10552-012-0054-3.

    Article  Google Scholar 

  14. Huang YJ, Huang TW, Lin FH, et al. Radiation therapy for invasive breast cancer increases the risk of second primary lung cancer: a nationwide population-based cohort analysis. J Thorac Oncol Off Publ Int Assoc Stud Lung Cancer. 2017;12(5):782–90. https://doi.org/10.1016/j.jtho.2017.01.021.

    Article  Google Scholar 

  15. Wei JL, Jiang YZ, Shao ZM. Survival and chemotherapy-related risk of second primary malignancy in breast cancer patients: a SEER-based study. Int J Clin Oncol. 2019;24(8):934–40. https://doi.org/10.1007/s10147-019-01430-0.

    Article  Google Scholar 

  16. Wang W. Increased incidence of second primary malignancy in patients with malignant astrocytoma: a population-based study. 2019. Biosci Rep. https://doi.org/10.1042/BSR20181968.

  17. Deng L, Harðardottír H, Song H, et al. Mortality of lung cancer as a second primary malignancy: a population-based cohort study. Cancer Med. 2019;8(6):3269–77. https://doi.org/10.1002/cam4.2172.

    Article  Google Scholar 

  18. Yang J, Pan Z, Zhou Q, et al. Nomogram for predicting the survival of patients with malignant melanoma: a population analysis. Oncol Lett. 2019;18(4):3591–8. https://doi.org/10.3892/ol.2019.10720.

    Article  Google Scholar 

  19. Iasonos A, Schrag D, Raj GV, et al. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364–70. https://doi.org/10.1200/jco.2007.12.9791.

    Article  Google Scholar 

  20. Li X, Deng Y, Tang W, et al. Urban-rural disparity in cancer incidence, mortality, and survivals in Shanghai, China, During 2002 and 2015. Front Oncol. 2018;8:579. https://doi.org/10.3389/fonc.2018.00579.

    Article  Google Scholar 

  21. Chen W, Zheng R, Zhang S, et al. Cancer incidence and mortality in China, 2013. Cancer Lett. 2017;401:63–71. https://doi.org/10.1016/j.canlet.2017.04.024.

    Article  CAS  Google Scholar 

  22. Ji J, Luo Z, Chen Y, et al. Characteristics and trends of childhood cancer in Pudong, China, 2002–2015. BMC Public Health. 2020;20(1):1430. https://doi.org/10.1186/s12889-020-09493-9.

    Article  Google Scholar 

  23. Ferlay J, Burkhard C, Whelan S, et al. Check and Conversion programs for cancer registries (IARC/ IACR Tools for cancer Registries); IARC technical Report No. 42. 2005.

  24. Copur MS, Manapuram S. Multiple primary tumors over a lifetime. Oncology. 2019;33(7):629384.

    Google Scholar 

  25. Shah BK, Khanal A, Hewett Y. Second primary malignancies in adults with gastric cancer: a US population-based study. Front Oncol. 2016;6:82. https://doi.org/10.3389/fonc.2016.00082.

    Article  Google Scholar 

  26. Faggion CM Jr. EQUATOR reporting guidelines should also be used by clinicians. J Clin Epidemiol. 2020;117:149–50. https://doi.org/10.1016/j.jclinepi.2019.09.015.

    Article  Google Scholar 

  27. Tibaldi C, Vasile E, Bernardini I, et al. Baseline elevated leukocyte count in peripheral blood is associated with poor survival in patients with advanced non-small cell lung cancer: a prognostic model. J Cancer Res Clin Oncol. 2008;134(10):1143–9. https://doi.org/10.1007/s00432-008-0378-2.

    Article  CAS  Google Scholar 

  28. In J, Lee DK. Survival analysis: part II—applied clinical data analysis. Korean J Anesthesiol. 2019;72(5):441–57. https://doi.org/10.4097/kja.19183.

    Article  Google Scholar 

  29. Mardhiah K, Wan-Arfah N, Naing NN, et al. Comparison of Cox proportional hazards model, Cox proportional hazards with time-varying coefficients model, and lognormal accelerated failure time model: application in time to event analysis of melioidosis patients. Asian Pac J Trop Med. 2022;15(03):128–34.

    Article  Google Scholar 

  30. Zhang A, Zhang Z, Zhang WB, et al. Development of a nomogram for predicting clinical outcome in patients with angiogram-negative subarachnoid hemorrhage. CNS Neurosci Ther. 2021;27(11):1339–47. https://doi.org/10.1111/cns.13712.

    Article  CAS  Google Scholar 

  31. DeSantis C, Siegel R, Bandi P, et al. Breast cancer statistics, 2011. CA Cancer J Clin. 2011;61(6):409–18. https://doi.org/10.3322/caac.20134.

    Article  Google Scholar 

  32. Noone AMHN, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (eds). SEER cancer statistics review, 1975–2015 [Z]//Noone AM H N, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (eds). Bethesda, MD; National Cancer Institute. 2018.

  33. Mcsorley ST, Black DH, Horgan PG, et al. The relationship between tumour stage, systemic inflammation, body composition and survival in patients with colorectal cancer. Clin Nutr. 2018;37(4):1279–85.

    Article  Google Scholar 

  34. Huang H, Fang W, Lin Y, et al. Predictive model for overall survival and cancer-specific survival in patients with esophageal adenocarcinoma. J Oncol. 2021;2021:4138575. https://doi.org/10.1155/2021/4138575.

    Article  Google Scholar 

  35. Wang Z, Yin Y, Wang J, et al. Standardized incidence rate, risk and survival outcomes of second primary malignancy among renal cell carcinoma survivors: a nested case-control study. Front Oncol. 2021;11:716741. https://doi.org/10.3389/fonc.2021.716741.

    Article  Google Scholar 

  36. Noer MC, Antonsen SL, Ottesen B, et al. Type I versus type II endometrial cancer: differential impact of comorbidity. Int J Gynecol Cancer Off J Int Gynecol Cancer Soc. 2018;28(3):586–93. https://doi.org/10.1097/igc.0000000000001184.

    Article  Google Scholar 

  37. Tang V, Zhao S, Boscardin J, et al. Functional status and survival after breast cancer surgery in nursing home residents. JAMA Surg. 2018;153(12):1090–6. https://doi.org/10.1001/jamasurg.2018.2736.

    Article  Google Scholar 

  38. Finegersh A, Said M, Deconde A, et al. Open and endoscopic surgery improve survival for squamous and nonsquamous cell nasopharyngeal carcinomas: an NCDB cohort study. Int Forum Allergy Rhinol. 2022. https://doi.org/10.1002/alr.23000.

    Article  Google Scholar 

  39. Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87–108. https://doi.org/10.3322/caac.21262.

    Article  Google Scholar 

  40. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32. https://doi.org/10.3322/caac.21338.

    Article  Google Scholar 

  41. Li X, Zhou Y, Luo Z, et al. The impact of screening on the survival of colorectal cancer in Shanghai, China: a population based study. BMC Public Health. 2019;19(1):1016. https://doi.org/10.1186/s12889-019-7318-8.

    Article  Google Scholar 

  42. ASCO. Thyroid Cancer: Statistics [Z]//ASCO. the United States; American Society of Clinical Oncology.

  43. Teng CJ, Hu YW, Chen SC, et al. Use of radioactive iodine for thyroid cancer and risk of second primary malignancy: a nationwide population-based study. J Natl Cancer Inst. 2016. https://doi.org/10.1093/jnci/djv314.

    Article  Google Scholar 

  44. Vargiu V, Rosati A, Capozzi VA, et al. Impact of obesity on sentinel lymph node mapping in patients with apparent early-stage endometrial cancer: the ObeLyX study. Gynecol Oncol. 2022;165(2):215–22. https://doi.org/10.1016/j.ygyno.2022.03.003.

    Article  Google Scholar 

  45. Shu XO, Jin F, Dai Q, et al. Association of body size and fat distribution with risk of breast cancer among Chinese women. Int J Cancer. 2001;94(3):449–55. https://doi.org/10.1002/ijc.1487.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We sincerely acknowledge and appreciate all participants in our study and the great work of hospitals in high quality data collection.

Funding

This work was supported by the Soft Science Project of Shanghai Science and Technology Commission (Grant Number 22692107200) and the Shanghai Public Health System Construction Three-year Action Plan Outstanding Youth Talent Training Program (Grant Number GWV-10.2-YQ43). Data extraction was funded by the National Natural Science Foundation of China (Grant Number 71603182) and the National Key Research and Development Program of China (Grant Number 2022YFC3601505). The analysis and interpretation of the data were guided by the Shanghai Education Science Research Project (Grant Number C2021039).

Author information

Author notes

  1. Ling Yuan and Yichen Chen are co-first authors of this work

Authors and Affiliations

  1. School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China

    Ling Yuan

  2. Center for Disease Control and Prevention, Pudong New Area, Shanghai, 200136, China

    Yichen Chen

  3. Fudan University Pudong Institute of Preventive Medicine, Pudong New Area, Shanghai, China

    Yichen Chen

  4. Department of Health Management Center, Zhongshan Hospital, Shanghai Medical College of Fudan University, 180 Fenglin RD, Shanghai, 200032, China

    Xiaopan Li

  5. Department of General Practice, Yangpu Hospital, School of Medicine, Tongji University, 450 Tengyue RD, Shanghai, 200090, China

    Hua Jin & Jianwei Shi

  6. Shanghai General Practice and Community Health Development Research Center, Shanghai, 200090, China

    Hua Jin

  7. Department of Social Medicine and Health Management, School of Public Health, Shanghai Jiaotong Universtiy School of Medicine, 227 South Chongqing RD, Shanghai, 200025, China

    Jianwei Shi

Authors
  1. Ling Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yichen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaopan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hua Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianwei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LY and YCC contributed to writing the manuscript. YCC prepared Tables 1 and 2. LY prepared Tables 3 and 4, Figs. 1, 2 and 3, and Additional files. HJ, XPL and JWS contributed to writing-review and editing. JWS guaranteed the integrity of the entire study. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiaopan Li, Hua Jin or Jianwei Shi.

Ethics declarations

Ethics approval and consent to participate

The study conformed to the ethical guidelines of the Helsinki Declaration of 1975 and was approved by the institutional review board of the School of Public Health of Fudan University (IRB#2016-04-0586). Our study did not involve any health-related human interventions. Informed consent was obtained from all participants or their families.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file1

. Figure S1: The sites of second primary malignancy in female breast cancer patients.

Additional file2

. Table S1: Clinical characteristics in the training cohort and validation cohort.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuan, L., Chen, Y., Li, X. et al. Predictive models for overall survival in breast cancer patients with a second primary malignancy: a real-world study in Shanghai, China. BMC Women's Health 22, 498 (2022). https://doi.org/10.1186/s12905-022-02079-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12905-022-02079-0

Keywords

BMC Women's Health

ISSN: 1472-6874

Contact us