Nomograms Constructed for Predicting Diagnosis and Prognosis in Cervical Cancer Patients with Second Primary Malignancies: A SEER Database Analysis

doi:10.21203/rs.3.rs-3133466/v1

Download PDF

Research Article

Nomograms Constructed for Predicting Diagnosis and Prognosis in Cervical Cancer Patients with Second Primary Malignancies: A SEER Database Analysis

https://doi.org/10.21203/rs.3.rs-3133466/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 21 Jul, 2023

Read the published version in Journal of Cancer Research and Clinical Oncology →

You are reading this latest preprint version

Purpose

Cervical cancer (CC) patients are more likely to develop second primary malignancies (SPMs) than general population. With the advancement in cancer therapy, CC patients are achieving long-term survival, leading SPMs to our attention. Our study aims to establish diagnostic and prognostic nomograms for CC patients with second primary malignancies (CCSPMs) to help make personalized follow-up plans and treatments.

Methods

Data of CCSPMs between 2000 and 2019 was extracted from SEER. The proportions and the average interval time of CCSPM onset were calculated. 11 related clinical characteristics, including age, race, marital status, grade, FIGO stage, radiotherapy, chemotherapy, and surgery, were further explore. Logistic and Cox regressions were employed to predict risk factors for CCSPMs diagnosis. Finally, two nomograms were developed to predict the probability occurrence and prognosis of CCSPMs, respectively.

Results

For diagnostic nomogram construction, 59,178 CC patients were randomly divided into training (n = 41,426) and validation cohorts (n = 17,752). For prognostic nomogram construction, 3,527 CCSPMs patients were randomly divided into training (n = 2,469) and validation cohorts (n = 1,058). The diagnostic nomogram consisting of above eleven independent risk factors (all P < 0.05), had high accuracy (AUCtraining = 0.851 and AUCvalidating = 0.845). The prognostic nomogram integrated with eight independent prognostic factors such as treatments, FIGO stage and TNM stage, performed well in predicting 5-year OS (AUCtraining = 0.835 and AUCvalidating = 0.837).

Conclusion

Our diagnostic and prognostic nomograms could facilitate clinicians to quantify individual SPMs risk and survival probabilities and optimize the surveillance recommendations and personalized clinical decision-making.

Second primary malignancies

cervical cancer

nomogram

diagnosis

prognosis

risk factors

Cervical cancer (CC) is the fourth most common female cancer worldwide. In 2020, there were approximately 604,100 new CC cases and 341,831 CC-related deaths worldwide[1]. According to NCCN guidelines, the main treatments for CC patients include surgery, radiotherapy, and chemotherapy. The newly approved immunotherapy and targeted therapy were applied in patients with advanced stages or recurrence[2]. With the application of scale-up vaccination, early detection, and advancement in treatment, the survival rate of CC patients has been significantly improved in recent periods[3].A recent retrospective study involving 295,64 CC patients in 660 countries between 2010–2014 showed the 5-year net survival of patients in all stages could be as high as 50%~70%[4]. Long-term survival of CC patients has brought second primary malignancies (SPMs) to our views.

Cancer survivors were more prone to obtain SPMs. A retrospective study of Japan[5] showed cancer survivors in Osaka were at higher risk of SPMs. More than 5.8% of all cancer survivors developed SPMs within ten years of their first cancer diagnosis. A study from Korea[6]also found that the 10-year overall survival (OS) for CCSPM patients was worse than CC patients without SPMs (65.5% vs. 73.1%). So, estimating SPMs risk and seeking active measures to minimize the threat has been the focus of research in recent years. However, previous studies on SPMs always focused on identifying risk factors[7–9], and there were few studies to quantify individualized risk probability. It is meaningful to search the individualized risk probability of CC patients who suffer SPMs.

Based on the SEER database, our study aims to establish and validate practical nomograms with the diagnosis and prognosis prediction values in CCSPMs. These nomograms demonstrated remarkable predictive accuracy and clinical utilities. Clinicians could quickly identify risk factors for SPMs, quantify individual SPMs' risks and survival probabilities, then determine the early surveillance recommendations and personalized treatments.

Patient selection

We systematically collected publicly available data of CC patients, confirmed as the first primary malignancy, from the SEER database (version 8.4.0) (www.seer.cancer.gov). The follow-up time was ranged from January 1, 2000, to December 31, 2019. We included patients with pathological diagnoses. Patients were excluded as the following criteria (1) patients who were under 18 years old. (2) patients who had more than two kinds of primary tumors.(3) patients with unknown histologies. There is no need for the ethics committee's approval due to the public availability of the SEER database.

Data collection

Data from CC patients were recorded, including tumor grade, treatment modality, age, marital status, race, FIGO stage, and TNM stage. The tumor locations of CCSPMs were categorized into nine groups: the female genital organs, thyroid, urinary system, skin, digestive system, lymph nodes, lungs, and breasts. When CC patients experienced CCSPMs or death, the follow-up time ended. Otherwise, the end of the follow-up time was December 31, 2022.

Statistical analysis

Chi-square tests were used to assess the significance of differences in Categorical variables between the non-SPMs group and the CCSPMs group. In both univariate and multivariate analyses, Logistic regression analysis was employed to estimate the relative variables of the diagnosis of CCSPMs. Cox regression analysis was used to identify variables associated with the prognosis of CCSPMs. The results were presented as odds ratios (ORs) with 95% confidence intervals (CIs). All analyses were conducted with R V. 3.6.3 (https://www.r-project.org). All statistical tests were two-sided, and P-values less than 0.05 were considered statistically significant.

Patient characteristics

59,178 CC patients were included in our study. Patients were divided into Non-SPMs and SPMs groups. Characteristics are detailed in Table 1. The median age at CC diagnosis was 63 years for the non-SPMs group and 64 years for the SPMs groups (P＜0.001). Among the SPMs group, most were in marriage status (2,017, 57.2%) and whites (2,935, 83.2%). The SPMs group has a higher proportion of patients with I/II grade (61.5% vs. 55.3%), I and II FIGO stage (39.6% vs. 37.7%), T1 and T2 (45.9% vs. 40.5%), N0 (49.4% vs 45.1%), and M0 (64.5% vs. 63.6%) than patients in non-SPMs group. In terms of treatments, patients in the SPMs group were less likely to be treated with surgery (1,016, 28.8%)) and radiotherapy (1,448, 41.1%), while more frequently with chemotherapy (2,848, 80.7%).

Table 1

Characteristics of CC patients in SEER, 2000–2019.
Characteristics	Total N = 59178	Non-SPMs N = 55651(%)	SPMs N = 3527(%)	P
Age (years)	63 (18–100)	63 (18–100)	64 (21–97)	< 0.001
Marital status				< 0.001
Married	29627	27610 (49.6%)	2017 (57.2%)
Unmarried	12105	11525 (20.7%)	580 (16.4%)
Other	17446	16516 (29.7%)	930 (26.4%)
Race				< 0.001
Black	8863	8373 (15.0%)	490 (13.9%)
White	47544	44609 (80.2%)	2935 (83.2%)
Other	2771	2669 (4.8%)	102 (2.9%)
Grade				< 0.001
I/II	32930	30761 (55.3%)	2169 (61.5%)
III/IV	10006	9441 (17.0%)	565 (16.0%)
Unknown	16242	15449 (27.8%)	793 (22.5%)
FIGO stage				< 0.001
I	15513	14569 (26.2%)	944 (26.8%)
II	6855	6404 (11.5%)	451 (12.8%)
III	7588	7190 (12.9%)	398 (11.3%)
IV	13769	13246 (23.8%)	523 (14.8%)
Unknown	15453	14242 (25.6%)	1211 (34.3%)
T stage				< 0.001
T1	14806	13788 (24.8%)	1018 (28.9%)
T2	9340	8741 (15.7%)	599 (17.0%)
T3	8212	7787 (14.0%)	425 (12.0%)
T4	6655	6377 (11.5%)	278 (7.9%)
Unknown	20165	18958 (34.1%)	1207 (34.2%)
N stage				< 0.001
N0	26817	25074 (45.1%)	1743 (49.4%)
N1	11949	11402 (20.5%)	547 (15.5%)
Unknown	20412	19175 (34.5%)	1237 (35.1%)
M stage				< 0.001
M0	37685	35410 (63.6%)	2275 (64.5%)
M1	1443	1412 (2.5%)	31 (0.9%)
Unknown	20050	18829 (33.8%)	1221 (34.6%)
Radiotherapy				< 0.001
No/Unknown	16422	14343 (25.8%)	2079 (58.9%)
Yes	42756	41308 (74.2%)	1448 (41.1%)
Chemotherapy				< 0.001
No/Unknown	39022	38343 (68.9%)	679 (19.3%)
Yes	20156	17308 (31.1%)	2848 (80.7%)
Surgery				< 0.001
No/Unknown	37372	34861 (62.6%)	2511 (71.2%)
Yes	21806	20790 (37.4%)	1016 (28.8%)

CCSPMs characteristics

As shown in Fig. 1a, The first three common locations for CCSPMs patients were the female genital organs (20.13%), lung (18.30%), and breast (16.97%). The lowest three sites were the thyroid (4.62%), lymph (3.02%), and nodes melanoma of the skin (2.20%). The mean interval time from CC onset to the occurrence of SPMs were 174 months for lung and bronchus cancers, 142 months for lymph nodes, 127 months for breast cancer, 117 months for the digestive system, 91 months for gynecology oncology, 16 months for urinary system tumors, 15 months for skin melanoma, and 13 months for thyroid cancer (Fig. 1b). 86.39% of CCSPMs were diagnosed five years after CC onset.

Risk factors and nomograms for CCSPMs

CC patients were randomly divided into training (n = 41,426) and validation (n = 17,752) cohorts according to a 7:3 ratio. Eleven Risk factors, including age, marital status, race, grade, FIGO stage, TNM stage, chemotherapy, surgery, and radiotherapy, were independent factors for the CCSPM diagnosis (all P < 0.05, Table 2). The diagnostic nomogram plotted based on those factors had high AUC in the training cohort (AUC = 0.851 ) and in the validation cohort ( AUC = 0.845) (Fig. 2, Figs. 3a, b). Our diagnostic nomogram was performed well by the calibration curves and decision curve analysis (DCA) (Figs. 3c-f). Further, we evaluated the prognostic impact of the above risk factors on 3,527 CCSPMs. The univariate and multivariate Cox regression showed eight of eleven factors were capable of predicting the prognosis of CCSPMs (all P < 0.05, Table 3), which were used to develop a prognostic nomogram (Fig. 4). The prognostic nomogram had a robust ability to predict 5-year OS in the training set (AUC = 0.835), and validation set (AUC = 0.837), and the calibration curves and DCA showed its high prediction power (Figs. 5a-f).

Table 2

The univariate and multivariate Logistic regression analysis.
Characteristic	Univariate Analysis			Multivariate Analysis
Characteristic	OR	95%CI	P	OR	95%CI	P
Age (years)	1.009	1.005–1.013	< 0.001	1.007	1.002–1.011	0.005
Marital status
Unmarried vs Married	0.698	0.622–0.783	< 0.001	0.764	0.667–0.875	< 0.001
Other vs Married	0.770	0.698–0.848	< 0.001	0.725	0.648–0.812	< 0.001
Race
White vs Black	1.091	0.969–1.229	0.149	0.978	0.851–1.125	0.756
Other vs Black	0.687	0.532–0.886	0.004	0.747	0.560–0.997	0.048
Grade
III/VI vs I/II	0.860	0.767–0.965	0.010	0.712	0.623–0.813	< 0.001
Unknown vs I/II	0.694	0.626–0.769	< 0.001	0.586	0.520–0.661	< 0.001
FIGO stage
II vs I	1.110	0.963–1.278	0.149	0.553	0.463–0.660	< 0.001
III vs I	0.895	0.773–1.035	0.135	0.209	0.175–0.249	< 0.001
IV vs I	0.636	0.557–0.727	< 0.001	0.131	0.111–0.154	< 0.001
Unknown vs I	1.397	1.255–1.556	< 0.001	0.592	0.516–0.680	< 0.001
T stage
T2 vs T1	0.942	0.829–1.069	0.355	1.026	0.881–1.196	0.740
T3 vs T1	0.774	0.673–0.890	< 0.001	0.876	0.738–1.040	0.130
T4 vs T1	0.599	0.507–0.706	< 0.001	0.708	0.578–0.866	0.001
Unknown vs T1	0.89	0.801–0.989	0.030	0.866	0.594–1.263	0.455
N stage
N1 vs N0	0.705	0.626–0.794	< 0.001	0.834	0.718–0.969	0.018
Unknown vs N0	0.963	0.879–1.055	0.416	1.029	0.702–1.509	0.882
M stage
M1 vs M0	0.296	0.185–0.473	< 0.001	0.349	0.209–0.581	< 0.001
Unknown vs M0	1.035	0.949–1.128	0.443	1.026	0.700-1.504	0.894
Radiotherapy
Yes vs No/Unknown	0.241	0.222–0.263	< 0.001	0.100	0.090–0.112	< 0.001
Chemotherapy
Yes vs No/Unknown	9.410	8.483–10.439	< 0.001	36.107	31.652–41.190	< 0.001
Surgery
Yes vs No	0.684	0.624–0.749	< 0.001	0.830	0.744–0.927	0.001

Table 3

Univariate and multivariate Cox regression analysis for CCSPMs prognosis
Characteristics	Univariate Analysis			Multivariate Analysis
Characteristics	HR	95%CI	P	HR	95%CI	P
Age (years)	1.063	1.021–1.105	0.031	1.007	0.928–1.097	0.053
Marital status
Unmarried vs Married	0.696	0.639–0.757	< 0.001	0.925	0.655–1.302	0.600
Other vs Married	0.782	0.729–0.840	< 0.001	1.067	0.895–1.234	0.438
Race
White vs Black	1.130	1.034–1.234	0.007	1.065	0.956–1.184	0.251
Other vs Black	0.697	0.579–0.839	< 0.001	0.889	0.756–1.054	0.113
Grade
III/VI vs I/II	1.202	1.179–1.328	0.007	1.262	1.119–1.438	< 0.001
Unknown vs I/II	1.278	1.186–1.401	< 0.001	1.295	1.123–1.510	< 0.001
FIGO stage
II vs I	1.020	0.957–1.288	0.102	1.021	0.958–1.288	0.202
III vs I	1.168	1.097–1.233	0. 013	1.068	0.898–1.193	0.103
IV vs I	1.483	1.379–1.517	< 0.001	1.472	1.375–1.573	< 0.001
Unknown vs I	1.125	1.056–1.193	0.026	1.121	1.054–1.189	0.013
T stage
T2 vs T1	1.077	1.011–1.123	0.060	1.038	0.896–1.125	0.431
T3 vs T1	1.161	1.083–1.276	< 0.001	1.138	1.077–1.225	0.008
T4 vs T1	1.237	1.181–1.411	< 0.001	1.259	1.192–1.407	< 0.001
Unknown vs T1	1.153	1.087–1.218	< 0.001	1.129	1.068–1.233	0.015
N stage
N1 vs N0	1.168	1.064–1.225	< 0.001	1.228	1.173–1.418	< 0.001
Unknown vs N0	1.055	1.006–1.101	0.020	1.027	0.897–1.138	0.257
M stage
M1 vs M0	1.235	1.127–1.481	< 0.001	1.236	1.154–1.373	< 0.001
Unknown vs M0	0.992	0.834–1.062	0.932	0.937	0.876–1.096	0.093
Radiotherapy
Yes vs No/Unknown	0.240	0.225–0.255	< 0.001	0.121	0.112–0.133	< 0.001
Chemotherapy
Yes vs No/Unknown	0.427	0.307–0.601	< 0.001	0.553	0.343–0.872	< 0.001
Surgery
Yes vs No/Unknown	0.680	0.636–0.726	< 0.001	0.624	0.576–0.678	< 0.001

Advances in diagnosis and therapeutic technologies have brought increased life expectancy for most cancer survivors[10]. As a “side effect,” it also has increased the risk of SPMs. Approximately 5.78%-6.4% of cancer patients have a history of multiple cancers, and the rate is uprising. Most CC patients are unconcerned about SPMs and only undergo further examination after symptoms are obvious. Consequently, the survival rate decreased significantly[6]. It is meaningful to timely assess the risk probability of SPMs in CC survivors and the survival probability of CCSPM patients to get a prolonged life span.

In this study, 86.39% of CCSPMs were diagnosed five years later since CC onset. Female genital organs are the most common location of SPMs in CC patients. It is attributable to solid location relationships and shared risk factors, including HPV infection and smoking. CC survivors tend to repeat pelvic/abdomen computed tomography to measure treatment response and post-treatment surveillance[12]. It leads to an incidental finding of SPMs located in the gynecologic system. CC survivors with older age, married, Black population, lower grade, and earlier clinical stages were more likely to have SPMs. Histology grade, clinical stage, and treatment(surgery, chemotherapy, and radiotherapy) were the independent factors in predicting the occurrence probability and prognosis of CCSPMs. The diagnostic and prognostic nomograms demonstrated perfect consistency and discriminative ability between the anticipated risks and observed results. Our nomograms had potential clinical utility for future clinical practice.

Understanding SPMs' locations and interval time trends are essential for screening recommendations for CC survivors. Our data showed that the top three SPMs sites were female genital tracts, lungs, and breasts, which accounted for over 50% of all SPMs. Each SPM has specific mean interval times. The pelvic MRI was recommended around 91 months in the prevention of female system tumors. The molybdenum target was also suggested around 127 months to prevent breast cancer. Chest CT should be performed at about 174 months to detect lung cancer. Based on the occurrence trends of different types of SPMs for CC survivors, we could make up a focused follow-up strategy with a better economic risk-benefit ratio. The five years is widely acknowledged as the cut-off point to distinguish the high and low rates of potential CC recurrence and metastasis.[13] However, it doesn't mean CC survivors are "safe" after five years. CC survivors could still acquire SPMs in their lifetime since more than 80% CCSPMs occurred five years later in our study. We suggest CC survivors still need careful, long-term follow-up strategies for SPMs prevention.

On the other hand, although not available in the SEER dataset, the influence of shared risk factors for SPMs should be carefully weighted. HPV infection, tobacco use, diet, reproductive factor, and hormones have been well-proven to be associated with CC carcinogenesis[14]. In our study, CC patients' second tumor location was more frequent in female genital sites and digestive systems, which have been well proven to be associated with HPV infection[15].Tobacco is also regarded as a shared carcinogenic factor for tumors of the lung and bronchus, digestive system, female genital organs, and urinary system[16]. Properly handling these shared risk factors could help to reduce the incidence of SPMs.

Clinical stages (T, N, M, and FIGO stage) and treatment approaches (surgery, chemotherapy, and radiotherapy) were the most influential factors in the diagnostic nomogram. They could attribute or alleviate the susceptibility of SPMs. Previous studies have shown that early clinical stages contributed to the occurrence of SPMs, due to the longer lifespan compared to advanced clinical stages[17, 18]. Hysterectomy and oophorectomy directly reduce the incidence of future gynecologic cancers and indirectly decrease the occurrence of hormone-related breast cancers[19, 20].In contrast, platinum-based chemotherapy was positively associated with the event of SPMs in gastrointestinal cancer, testicular malignancy, and HPV-associated cancers[21]. In previous studies, radiotherapy was often recognized as a risk factor for SPMs.[22, 23] Interestingly, in our study, patients with a radiation history have a reduced risk of SPMs susceptibility. Previous studies[24–26]also showed patients with rectal and endometrial cancers had no increased risk of SPMs after pelvic irradiation. “incidental” radiation of the second pelvic tumors and only a tiny proportion of SPMs attributable to radiotherapy[24, 27]. Treatment approaches and clinical stages were also independent factors in our prognostic nomogram. Patients who refused treatment or with advanced clinical stage had poor 5-year overall survival. These results indicated that anti-CC therapy and earlier clinical stage not only could positively influence SPMs prognosis but also evoke the occurrence of SPMs to some extent. The diagnostic and prognostic nomograms may assist clinicians in timely evaluating the diagnosis and prognosis of CC patients with SPMs and optimize individualized treatment plans.

To our knowledge, this study is the first to identify the diagnostic and prognostic factors associated with SPMs among CC survivors in SEER. However, our study has certain limitations. First, the SEER database has no detailed information on shared risk factors, including HPV infection, smoking, diet, and hormones, so how these factors contribute to the development of SPMs needs further exploration. Second, since few CC survivors had SPMs in the thyroid, lymph nodes, and skin, we did not investigate their prognosis. Third, the study's statistical results may be biased because of many unknown (No/Unknow) variables in SEER.

Our cohort study highlighted the need for individualized consolation for CC patients when making treatments and follow-up strategies. The SPMs often occurs in female reproductive systems in CC patients. We should also pay attention to the interval times of different SPMs. Our diagnostic and prognostic nomograms could benefit SPMs prevention, early detection, and treatment.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Ethical approval statement

There is no need for the ethics committee's approval due to the public availability of the SEER database.

Acknowledgments

The authors sincerely thank the Surveillance, Epidemiology, and End Results (SEER) program for their efforts in establishing the SEER database.

Author Contributions

All authors contributed to the study conception and design. Conceptualization, Ning Xie and Jie Lin; methodology, Ning Xie; software, Jie Lin; validation, Linying Liu; formal analysis, Sufang Deng; resources, Linying Liu; data curation, Sufang Deng; visualization, Haijuan Yu; writing—original draft preparation, Ning Xie, Jie Lin and Yang Sun; writing—review and editing, Yang Sun; supervision, Yang Sun; All authors have read and agreed to the published version of the manuscript.

Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71, 209–249, doi:10.3322/caac.21660.
Koh, W.-J.; Abu-Rustum, N.R.; Bean, S.; Bradley, K.; Campos, S.M.; Cho, K.R.; Chon, H.S.; Chu, C.; Clark, R.; Cohn, D.; et al. Cervical Cancer, Version 3.2019, NCCN Clinical Practice Guidelines in Oncology. Journal of the National Comprehensive Cancer Network 2019, 17, 64–84, doi:10.6004/jnccn.2019.0001.
Simms, K.T.; Steinberg, J.; Caruana, M.; Smith, M.A.; Lew, J.-B.; Soerjomataram, I.; Castle, P.E.; Bray, F.; Canfell, K. Impact of Scaled up Human Papillomavirus Vaccination and Cervical Screening and the Potential for Global Elimination of Cervical Cancer in 181 Countries, 2020–99: A Modelling Study. Lancet Oncol 2019, 20, 394–407, doi:10.1016/S1470-2045(18)30836-2.
Allemani, C.; Matsuda, T.; Di Carlo, V.; Harewood, R.; Matz, M.; Nikšić, M.; Bonaventure, A.; Valkov, M.; Johnson, C.J.; Estève, J.; 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. The Lancet 2018, 391, 1023–1075, doi:10.1016/S0140-6736(17)33326-3.
Odani, S.; Tabuchi, T.; Nakata, K.; Morishima, T.; Kuwabara, Y.; Koyama, S.; Kudo, H.; Kato, M.; Miyashiro, I. Incidence and Relative Risk of Metachronous Second Primary Cancers for 16 Cancer Sites, Osaka, Japan, 2000–2015: Population‐based Analysis. Cancer Med 2022, 11, 507–519, doi:10.1002/cam4.4457.
Lim, M.C.; Won, Y.-J.; Lim, J.; Kim, Y.-J.; Seo, S.S.; Kang, S.; Lee, E.S.; Oh, J.H.; Kim, J.-Y.; Park, S.-Y. Second Primary Cancer after Diagnosis and Treatment of Cervical Cancer. Cancer Res Treat 2016, 48, 641–649, doi:10.4143/crt.2014.326.
Suk, R.; Mahale, P.; Sonawane, K.; Sikora, A.G.; Chhatwal, J.; Schmeler, K.M.; Sigel, K.; Cantor, S.B.; Chiao, E.Y.; Deshmukh, A.A. Trends in Risks for Second Primary Cancers Associated With Index Human Papillomavirus–Associated Cancers. JAMA Netw Open 2018, 1, e181999, doi:10.1001/jamanetworkopen.2018.1999.
Arnold, M.; Liu, L.; Kenter, G.G.; Creutzberg, C.L.; Coebergh, J.W.; Soerjomataram, I. Second Primary Cancers in Survivors of Cervical Cancer in the Netherlands: Implications for Prevention and Surveillance. Radiotherapy and Oncology 2014, 111, 374–381, doi:10.1016/j.radonc.2014.04.011.
Adjei Boakye, E.; Grubb, L.; Peterson, C.E.; Osazuwa-Peters, N.; Grabosch, S.; Ladage, H.D.; Huh, W.K. Risk of Second Primary Cancers among Survivors of Gynecological Cancers. Gynecol Oncol 2020, 158, 719–726, doi:10.1016/j.ygyno.2020.06.492.
Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer Statistics, 2019. CA Cancer J Clin 2019, 69, 7–34, doi:10.3322/caac.21551.
Chen, C.Y.; Lai, C.H.; Lee, K. Der; Huang, S.H.; Dai, Y.M.; Chen, M.C. Risk of Second Primary Malignancies in Women with Cervical Cancer: A Population-Based Study in Taiwan over a 30-Year Period. Gynecol Oncol 2012, 127, 625–630, doi:10.1016/J.YGYNO.2012.09.004.
Ye, Y.; Otahal, P.; Wills, K.E.; Neil, A.L.; Venn, A.J. Temporal Trends in the Risk of Second Primary Cancers among Survivors of Adult-Onset Cancers, 1980 through 2013: An Australian Population-Based Study. Cancer 2018, 124, 1808–1818, doi:10.1002/cncr.31247.
Woopen, H.; Sehouli, J.; Davis, A.; Lee, Y.C.; Cohen, P.A.; Ferrero, A.; Gleeson, N.; Jhingran, A.; Kajimoto, Y.; Mayadev, J.; et al. GCIG-Consensus Guideline for Long-Term Survivorship in Gynecologic Cancer: A Position Paper from the Gynecologic Cancer Intergroup (GCIG) Symptom Benefit Committee. Cancer Treat Rev 2022, 107, 102396, doi:10.1016/j.ctrv.2022.102396.
Pimple, S.; Mishra, G. Cancer Cervix: Epidemiology and Disease Burden. Cytojournal 2022, 19, 21, doi:10.25259/CMAS_03_02_2021.
Deshmukh, A.A.; Suk, R.; Shiels, M.S.; Damgacioglu, H.; Lin, Y.-Y.; Stier, E.A.; Nyitray, A.G.; Chiao, E.Y.; Nemutlu, G.S.; Chhatwal, J.; et al. Incidence Trends and Burden of Human Papillomavirus-Associated Cancers Among Women in the United States, 2001-2017. JNCI: Journal of the National Cancer Institute 2021, 113, 792–796, doi:10.1093/jnci/djaa128.
Lortet-Tieulent, J.; Goding Sauer, A.; Siegel, R.L.; Miller, K.D.; Islami, F.; Fedewa, S.A.; Jacobs, E.J.; Jemal, A. State-Level Cancer Mortality Attributable to Cigarette Smoking in the United States. JAMA Intern Med 2016, 176, 1792, doi:10.1001/jamainternmed.2016.6530.
Mitani, S.; Kato, K.; Daiko, H.; Ito, Y.; Nozaki, I.; Kojima, T.; Yano, M.; Nakagawa, S.; Ueno, M.; Watanabe, M.; et al. Second Primary Malignancies in Patients with Clinical T1bN0 Esophageal Squamous Cell Carcinoma after Definitive Therapies: Supplementary Analysis of the JCOG Trial: JCOG0502. J Gastroenterol 2022, 57, 455–463, doi:10.1007/s00535-022-01870-y.
Xu, J.; Huang, C.; Wu, Z.; Xu, H.; Li, J.; Chen, Y.; Wang, C.; Zhu, J.; Qin, G.; Zheng, X.; et al. Risk Prediction of Second Primary Malignancies in Primary Early-Stage Ovarian Cancer Survivors: A SEER-Based National Population-Based Cohort Study. Front Oncol 2022, 12, doi:10.3389/fonc.2022.875489.
Lovett, S.M.; Sandler, D.P.; O’Brien, K.M. Hysterectomy, Bilateral Oophorectomy, and Breast Cancer Risk in a Racially Diverse Prospective Cohort Study. JNCI: Journal of the National Cancer Institute 2023, doi:10.1093/jnci/djad038.
Huo, N.; Smith, C.Y.; Gazzuola Rocca, L.; Rocca, W.A.; Mielke, M.M. Risk of de Novo Cancer after Premenopausal Bilateral Oophorectomy. Am J Obstet Gynecol 2022, 226, 539.e1-539.e16, doi:10.1016/j.ajog.2021.10.040.
Henderson, T.O.; Fowler, B.W.; Hamann, H.A.; Nathan, P.C.; Whitton, J.; Leisenring, W.M.; Oeffinger, K.C.; Neglia, J.P.; Turcotte, L.M.; Arnold, M.A.; et al. Subsequent Malignant Neoplasms in the Childhood Cancer Survivor Study: Occurrence of Cancer Types in Which Human Papillomavirus Is an Established Etiologic Risk Factor. Cancer 2022, 128, 373–382, doi:10.1002/cncr.33922.
Kamran, S.C.; Berrington de Gonzalez, A.; Ng, A.; Haas-Kogan, D.; Viswanathan, A.N. Therapeutic Radiation and the Potential Risk of Second Malignancies. Cancer 2016, 122, 1809–1821, doi:10.1002/cncr.29841.
Wallis, C.J.D.; Mahar, A.L.; Choo, R.; Herschorn, S.; Kodama, R.T.; Shah, P.S.; Danjoux, C.; Narod, S.A.; Nam, R.K. Second Malignancies after Radiotherapy for Prostate Cancer: Systematic Review and Meta-Analysis. BMJ 2016, i851, doi:10.1136/bmj.i851.
Wiltink, L.M.; Nout, R.A.; Fiocco, M.; Meershoek-Klein Kranenbarg, E.; Jürgenliemk-Schulz, I.M.; Jobsen, J.J.; Nagtegaal, I.D.; Rutten, H.J.T.; van de Velde, C.J.H.; Creutzberg, C.L.; et al. No Increased Risk of Second Cancer After Radiotherapy in Patients Treated for Rectal or Endometrial Cancer in the Randomized TME, PORTEC-1, and PORTEC-2 Trials. Journal of Clinical Oncology 2015, 33, 1640–1646, doi:10.1200/JCO.2014.58.6693.
Nout, R.; Smit, V.; Putter, H.; Jürgenliemk-Schulz, I.; Jobsen, J.; Lutgens, L.; van der Steen-Banasik, E.; Mens, J.; Slot, A.; Kroese, M.S.; et al. Vaginal Brachytherapy versus Pelvic External Beam Radiotherapy for Patients with Endometrial Cancer of High-Intermediate Risk (PORTEC-2): An Open-Label, Non-Inferiority, Randomised Trial. The Lancet 2010, 375, 816–823, doi:10.1016/S0140-6736(09)62163-2.
Chaturvedi, A.K.; Engels, E.A.; Gilbert, E.S.; Chen, B.E.; Storm, H.; Lynch, C.F.; Hall, P.; Langmark, F.; Pukkala, E.; Kaijser, M.; et al. Second Cancers Among 104760 Survivors of Cervical Cancer: Evaluation of Long-Term Risk. JNCI Journal of the National Cancer Institute 2007, 99, 1634–1643, doi:10.1093/jnci/djm201.
Martling, A.; Smedby, K.E.; Birgisson, H.; Olsson, H.; Granath, F.; Ekbom, A.; Glimelius, B. Risk of Second Primary Cancer in Patients Treated with Radiotherapy for Rectal Cancer. British Journal of Surgery 2017, 104, 278–287, doi:10.1002/bjs.10327.

No competing interests reported.

Download PDF

Journal Publication

published 21 Jul, 2023

Read the published version in Journal of Cancer Research and Clinical Oncology →

Editorial decision: Major revision
06 Jul, 2023
Reviews received at journal
06 Jul, 2023
Reviewers agreed at journal
05 Jul, 2023
Reviewers invited by journal
05 Jul, 2023
Editor assigned by journal
05 Jul, 2023
Submission checks completed at journal
05 Jul, 2023
First submitted to journal
02 Jul, 2023

You are reading this latest preprint version

Nomograms Constructed for Predicting Diagnosis and Prognosis in Cervical Cancer Patients with Second Primary Malignancies: A SEER Database Analysis

Status:

Journal Publication

Version 1

Abstract

Purpose

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

Patient selection

Data collection

Statistical analysis

Results

Patient characteristics

CCSPMs characteristics

Risk factors and nomograms for CCSPMs

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1