Background

Rectal cancer (RC) is a type of cancer that occurs at the dentate line and colon-sigmoideum junctions [1]. Colorectal cancer is the fourth deadliest cancer in the world [2], and RC represents 40% of colorectal cancer cases. In the United States, there are 44,850 new RC cases (26,650 in men; 18,200 in women) in 2022 and 46,050 new RC cases (27,440 in men, 18,610 in women) in 2023, suggesting an increasing incidence [3]. Furthermore, the morbidity and mortality of RC are projected to rise dramatically by 2035 [4]. This cancer may be caused by several controllable risk factors, such as sedentary work [OR = 1.08 (95%CI: 1.00–1.16)] [5], male obesity [RR = 1.12 (95%CI: 1.09–1.16)] [6], red meat intake [RR = 1.22 (95% CI: 1.01–1.46)] [7], meat products [RR = 1.26 (95% CI = 1.09–1.45)] [7], and high alcohol intake [OR = 1.25 (95%CI: 1.11–1.40)] [8].

The distal third of the rectum is drained by systemic veins instead of the portal venous system, which increases the likelihood of direct lung metastases [9]. RC penetrates the pelvis more easily than colon cancer, with a higher risk of lymph node metastasis in each T stage, complex anatomical structures, difficult-to-operate surgery, and a high postoperative recurrence rate. Currently, treatments for RC include local or extensive surgery, preoperative radiotherapy and systemic therapy, local ablation of metastases, palliative chemotherapy, targeted therapy, and immunotherapy [10]. The selection of treatments is based on the tumor’s clinicopathological characteristics, such as the degree of differentiation and the site of metastasis [11, 12]. RC survival is closely related to clinical decisions based on staging information. The 5-year disease-free survival (DFS) and overall survival (OS) of patients with RC were 70.1% (95%CI: 60.7%–73.0%) and 76.2% (95%CI: 73.8%–78.5%) with open surgery, and 72.2% (95%CI: 69.4%–74.8%) and 72.7% (95%CI: 69.8%–75.3%) with laparoscopic surgery, respectively [13]. Compared with long-course chemoradiotherapy, both total neoadjuvant therapy with long-course chemoradiotherapy [OR = 1.78 (95%CI: 1.43–2.26)] and total neoadjuvant therapy with short-course radiotherapy [OR = 1.75(95%CI: 1.23–2.50)] improved the pathological complete response rate; neoadjuvant chemotherapy increased DFS, but did not significantly prolong OS [14]. At present, TNM staging is the primary reference in clinical practice. Other potential prognostic factors, such as age, sex, marriage, and region, are also strongly associated with prognosis [15]. Therefore, it is necessary to explore a more comprehensive prediction method. Clinical predictive models are more personalized approaches tailored to these factors.

The specific mortality risk is more valuable than the traditional mortality risk in clinical practice due to some unforeseeable mortality events. Specifically speaking, the causes of death in RC patients are diverse, and a large number of mortality events are not caused by RC, as RC patients are often in poor functional status or accompanied by co-morbidities [16]. These deaths are unforeseeable when patients die of other diseases, such as cardiovascular disease. In addition, as competing risks often occur among the diverse risk factors for death in patients with RC, traditional survival analysis methods may ignore the occurrence of main concern events, resulting in overestimated results [17]. Therefore, traditional survival analysis methods are unsuitable for processing data containing competing outcomes. A competing risk model is more suitable for dealing with data containing competing events and can produce more accurate results [17].

Since most patients with RC have a long survival period, identifying the risk of specific death early, in the case of competing events, is necessary to facilitate the development of specific prognostic rehabilitation programs and follow-up plans, thereby reducing the risk of specific death in patients. Although various prognostic models, especially based on radiomics, have been established in recent years, predictive models in clinical practice should be highly interpretable and applicable. Therefore, we conducted this study based on the SEER database to construct an interpretable nomogram for early predicting the risk of RC-specific mortality.

Methods

Data sources and extractions

The SEER database, which comprises 18 cancer registries, is the National Cancer Institute's collection of demographic statistics on all cancers diagnosed in representative geographic regions and subpopulations. We collected patient clinicopathological data, demographic information, clinicopathological information, and follow-up data from the database. The patient data were de-identified because the SEER database is publicly available. In addition, ethical approval and informed consent are not required for our study. Our methodology strictly follows the rules of the SEER database.

We selected the “Incidence SEER Research Plus Data, 18 Registries, Nov 2020 Sub (2000–2018)” database from 2010 to 2015. According to the disease name of RC, we collected the clinicopathological information of all RC patients, including Patient ID, Age, Marriage, Race, Sex, Primary Site- labeled, ICD-O-3Hist/Behav, Grade, Laterality, AJCC stage, AJCC Tstage, AJCC N stage, AJCC M stage, Surgery, Examined LN, RX SUMM-SURG OTH REG, Radiation recode, Chemotherapy, Preoperative CEA, Pleural Effusion Recode, Tumor deposits, Regional nodes positive, Brain, Bone, Liver, Lung, CS Tumor size, Malignant tumor numbers, cause of death classification, Survival months, COD to site rec KM. Exclusion criteria were as follows: (1) unclear modeling variables NA, Blanks, unknown TNM stage (TX, NX); (2) unknown tumor size; (3) unknown surgical method; (4) survival time < 1 month or unknown (5) Age ≤ 18.

Construction and verification of competitive risk model

We developed a competing risk model by defining deaths from unknown causes or survival cases in RC as censoring events, deaths from specific causes as interest events, and deaths from other causes as competing events. Before building the model, we randomly split the data by 70% and 30% into training and validation sets. A uniform random number distributed between 0 and 1 was assigned to each case, and then these cases were ranked according to their random number. The cases with a random number < 70 percentile were assigned to the training set, and the rest of the cases were assigned to the validation set. Univariate and multivariate analyses were performed in the training set to screen the independent influencing factors of RC, and RC-specific death nomograms were constructed according to the independent influencing factors. The concordance index (C-index) and the area under the receiver operating curve (AUC) were used to measure the accuracy of the prediction nomogram, and the calibration curve was used to measure the calibration degree of the prediction nomogram.

Statistical analysis

Continuous variables were tested for normal distribution using Shapiro–Wilk test. Normal distributed continuous variables were expressed as mean ± standard deviation (SD), and an independent samples t-test was used to compare the means of the two groups. For continuous variables with skewed distribution, the Mann–Whitney U test was performed for comparisons between groups. Count data were expressed as frequency (%); and the chi-square test was used for comparison between multiple groups. A P < 0.05 indicated a statistically significant difference. R 4.2.0 was used for modeling, while SPSS 2.3.0 was used for statistical analysis.

Results

Patient characteristics

A total of 23,680 RC patients were included in the analysis. Patients were randomly assigned to the training cohort (n = 16,580) and the validation cohort (n = 7100). In the training cohort, the mean age of patients was 63.02 ± 13.38 years; the number of deaths was 6470; the number of specific deaths was 4602; and the mean follow-up time was 52.35 ± 27.20 years. In the validation cohort, the mean age of patients was 62.90 ± 13.41 years; the number of deaths was 9174; the number of specific deaths was 6537; and the mean follow-up time was 52.53 ± 27.77 years. It can be seen that the distribution of the two groups is even, and the rest of the information is shown in Table 1.

Table 1 Clinicopathological characteristics of RC patients
Full size table

Competing risk model for univariate and multivariate analysis

The variables in the present study included Age, Marriage, Race, Sex, ICD-O-3Hist/Behav, Grade, AJCC stage, T stage, N stage, Surgery, Examined LN, RX SUMM-SURG OTH, Chemotherapy, CEA, Deposits, Regional nodes in single factor Positive, Brain, Bone, Liver, Lung, Tumor size, and Malignant variables. Both univariate and multivariate analyses did not include the M stage because it overlapped with the AJCC stage. The variable Radiation (P > 0.05) was excluded from the multivariate analysis, whereas other variables in the single factor analysis were included in the multivariate analysis, with P < 0.05 (Table 2). The results showed that these variables were all independent influencing factors for RC. Accordingly, we constructed a competing risk model based on these variables.

Table 2 single factor and multi factor analysis
Full size table

Validation of predictive nomogram

Specific mortality prediction nomograms were drawn according to the competing risk model described above (Fig. 1). In the training set, the overall C statistic was 0.821 (Se 0.001), and the 1-, 3-, and 5-year AUC for predicting the specific mortality was 0.842 (95% CI 0.830–0.854), 0.830 (95% CI 0.822–0.838), 0.812 (95% CI 0.803–0.820), respectively. The results show that the model has a good identification ability (Fig. 2A). In addition, the model's calibration curve (Fig. 3A) shows that the 1-, 3- and 5-year curves are consistent with the ideal curve, indicating that the predicted curve diagram has good accuracy and calibration.

Fig. 1
figure 1

The competitive risk model nomogram of patients with Rectal Cancer at 1, 3, and 5 years

Full size image
Fig. 2
figure 2

AUC for predicting 1-, 3-, and 5-year Rectal Cancer in training cohort (A) and validation cohort (B)

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Fig. 3
figure 3

Calibration curve of the nomogram in training cohort (A) and validation cohort (B)

Full size image

At the same time, in the internal validation cohort, the overall C statistic was 0.829 (Se 0.002); the 1-, 3-, and 5-year AUC (Fig. 2B) was 0.851 (95% CI 0.832–0.869), 0.836 (95% CI 0.824–0.849), and 0.813 (95% CI 0.800–0.826). The results also show that the model has good identification power. According to the calibration curve of the model (Fig. 3B), the 1-, 3- and 5-year curves are consistent with the ideal curve, indicating that the predicted curve diagram has good accuracy and calibration.

Specific mortality estimation

We compared the 1- to 9-year specific mortality calculated by traditional survival analysis and the competing risk model. The results revealed that the competing risk model showed lower specific mortality than traditional survival analysis. For example, the 1-year mortality rate was 0.08 in the traditional survival analysis and 0.07 in the specific survival analysis. Their gap grows as the survival duration increases between 1 and 9 years. Therefore, mortality from other causes could not be ignored (Table 3).

Table 3 Comparison of 1- to 9-year specific mortality (%) between traditional survival analysis and competitive risk model
Full size table

Discussion

Based on univariate and multivariate analyses, we included the independent influencing factors of RC-specific death, including Age, Marriage, Race, Sex, ICD-O-3Hist/Behav, Grade, AJCC stage, T stage, N stage, Surgery, Examined LN, RX SUMM-SURG OTH, Chemotherapy, CEA, Deposits, Regional nodes positive, Brain, Bone, Liver, Lung, Tumor size, and Malignant. These variables were combined to construct a competing risk model for RC and a predictive nomogram, which have excellent accuracy. The overall C statistic was 0.821 (Se 0.001) in the model training cohort and 0.829 (Se 0.002) in the internal validation cohort.

Currently, there are various RC prediction nomograms based on clinical data [

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

RC:

Rectal cancer

CSS:

Cancer-specific survival

CEA:

Carcinoembryonic antigen

TDs:

Tumor deposits

LNM:

Lymph node metastasis

ROS:

Reactive oxygen species

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Acknowledgements

We would like to thank the researchers and study participants for their contributions.

Funding

This work was supported by the Henan Provincial Government-Health and Family Planning Commission Research Science Innovative Talents Project (51282).

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Authors and Affiliations

  1. Department of Gastroenterology and Hepatology, People’s Hospital of Zhengzhou University, No.7 Weiwu Road, Zhengzhou, 450003, Henan, China

    Ruobing Hu, **uling Li & Songze Ding

  2. Department of Infection Disease, Shanghai **shan District Tinglin Hospital, Shanghai, 201505, China

    **aomin Zhou

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Contributions

Conceptualization: RH, SD; Methodology: RH, SD; Formal analysis and investigation: RH, XZ, XL; Writing original draft preparation: RH; Writing review and editing: RH, SD; Funding acquisition: SD, XZ; Resources: RH, SD, XZ, XL; Supervision: SD; and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Songze Ding.

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Hu, R., Li, X., Zhou, X. et al. Development and validation of a competitive risk model in patients with rectal cancer: based on SEER database. Eur J Med Res 28, 362 (2023). https://doi.org/10.1186/s40001-023-01357-3

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