Development and verification of risk prediction nomograms for overall and specific mortality in primary small bowel cancer: a population-based study

Introduction

The small intestine, as the longest part of the digestive tract, measures 5–7 m in length and comprises the duodenum, jejunum, and ileum. According to the 2022 National Comprehensive Cancer Network (NCCN) Guidelines for small bowel cancer (SBC), malignant tumors of the small intestine account for only 3% of all gastrointestinal malignancies. The incidence rate of SBC is 2.6/100,000 in males and 2.0/100,000 in females, making it a recognized rare tumor internationally (1,2). However, the incidence of SBC has been increasing over the years since its higher placement in the gastrointestinal system makes it challenging to detect the tumor through routine colonoscopy examinations (3). There are more than 40 different histological subtypes of SBC, among which neuroendocrine carcinoma, adenoma, and stromal tumor are the most common (4). The rarity of SBC cases limits research on its prognosis and treatment. Current prediction of prognosis of SBC patients mostly relies on tumor-node-metastasis (TNM) staging, which includes T (primary tumor), N (regional lymph nodes), and M (distant metastasis). This staging system only includes limited factors and neglects other important risk factors.

In recent years, traditional TNM staging has gradually been replaced by nomogram models (5,6). Patient prognosis is also influenced by numerous non-anatomical factors such as age, gender, and surgical approach (7). At present, there is limited research on risk factors for SBC, and no nomograms have been developed and validated for predicting the risk of overall mortality in SBC patients. According to cancer statistics, the incidence of SBC has been increasing in recent years. Accurately predicting the risks of both overall mortality and specific mortality of primary SBC is crucial. Thus, developing reliable and well-performing nomograms for predicting such risks is urgently needed to aid clinicians in making optimal clinical decisions and guiding the development of personalized treatment regimens.

This study aimed to construct prognostic nomograms for predicting both overall and specific mortality risks in patients with primary SBC by utilizing Cox proportional hazards and competing risk models, based on the Surveillance, Epidemiology, and End Results (SEER) database. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-818/rc).

Methods

Data source and patient selection

The SEER public database is a renowned cancer statistics database in the United States, which encompasses a vast amount of clinical retrospective research data. It provides an excellent research resource for studying malignant and rare tumors. In this study, the dataset of SBC patients was acquired from the SEER*Stat database (version 8.4.1.2; https://seer.cancer.gov/). This dataset includes patients diagnosed with SBC from 2010 to 2015.

Inclusion and exclusion criteria

Inclusion criteria were as follows: (I) primary tumors occur in the small intestine; (II) detailed and specific clinical variables were available, such as age, gender, race, and surgical information; (III) complete clinicopathological information, comprising histological type, grade, TNM staging, tumor size, and presence of bone, brain, liver, or lung metastases; (IV) non-zero and clearly recorded survival duration.

The extracted data included: (I) age, categorized as <60, 60–69,70–79 and ≥80 years; (II) race, categorized as White, Black, and other; (III) marital status, categorized as married, single, widowed, and others; (IV) sex, categorized as male and female; (V) primary site, categorized as ileum, duodenum, jejunum, and others/not otherwise specified (NOS); (VI) ICD-O-3 histology/behavior, categorized as adenocarcinoma (8140, 8144, 8210, 9211, 8245, 8255, 8260, 8261, 8262, 8263, 8480, 8481, 8560, 8574, 8576), neuroendocrine cancer (8013, 8240, 8246, 8244, 8249), mesenchymal tumor (8936), and other; (VII) grade; (VIII) derived American Joint Committee on Cancer (AJCC) T, N, M; (IX) surgery primary site, categorized as local excision (codes 10 and 20–70), excision of the primary site (codes 30 and 40), radical cure (codes 50 and 60), and others; (X) radiation; (XI) chemotherapy; (XII) regional nodes examined; (XIII) regional nodes positive; (XIV) SEER combined mets at DX-bone; (XV) SEER combined mets at DX-brain; (XVI) SEER combined mets at DX-liver; (XVII) SEER combined mets at DX-lung; (XVIII) tumor size, categorized as ≥70 and <70 mm.

As the SEER database is a public database that does not contain patient identifiers, no ethical approval or informed consent is required in this study. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Development and verification of the prediction model

In this study, overall survival (OS) and cancer-specific survival (CSS) were utilized as the primary endpoints. OS was the time from the initial diagnosis to all-cause death, while CSS was the time between the initial diagnosis to SBC-specific death. The patients were randomized into a training set (70%) and a verification set (30%). The training set was primarily employed to construct prognostic nomograms for risk prediction, while the verification set was adopted to assess the accuracy of the models. Prognostic factors were selected through univariate and multivariate analyses. According to the results of the multivariate analysis, nomograms for predicting 1-, 3-, and 5-year OS and CSS in SBC patients were constructed. The predictive performance, sensitivity, and accuracy of the nomograms were validated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and calibration curves.

Statistical analysis

Statistical analysis was carried out with R software (version 4.3.1). SBC patients who met the inclusion criteria were randomly allocated to the training group (n=4,804) and the verification group (n=2,059) at a ratio of 7:3. Cox proportional hazards and competing risk models were employed to screen risk factors for overall mortality and specific mortality, respectively. Furthermore, all statistical tests were two-sided, with a P value less than 0.05 considered statistically significant.

Univariate analysis was conducted to determine factors associated with overall mortality and specific mortality. Subsequently, variables with P<0.05 in the univariate analysis were selected for multivariate analysis to determine independent risk factors for overall mortality and specific mortality in SBC patients.

Results

Baseline characteristics of the study population

A total of 6,863 SBC patients between 2010 and 2015 from the SEER database were included in this study. The patients were randomly divided into a training set (N=4,804) and a verification set (N=2,059) at a 7:3 ratio. In the training set, there were 1,630 all-cause deaths and 1,057 cancer-specific deaths, while in the verification set, 708 patients died from all causes and 431 died from cancer-specific causes. Overall, the patients were relatively old, with over 50% of patients being above 60 years old. Adenocarcinoma was the most common histological type, representing approximately 63% in both datasets. The majority of tumors occurred in the ileum (36.2%) and duodenum (27.8%). The most common tumor differentiation grade was grade I, representing 56% in the training set and 55.8% in the verification set. Primary site resection, radical resection, and partial resection were performed in 4,761 (69.3%), 1,080 (15.7%), and 571 patients (8.31%), respectively. A total of 201 (2.93%) patients received radiation therapy, and 1,468 (21.4%) patients received chemotherapy. There were no significant differences between the two sets. The baseline characteristics of these SBC patients are provided in Table 1.

Establishment of prognostic nomograms

In this study, univariate Cox analysis and univariate competing risk models were initially employed to screen for prognostic factors for OS and CSS in SBC patients. The variables with P<0.05 were included in the multivariate analysis (Tables 2,3). For OS risk prediction, the following prognostic factors were identified: age, race, marital status, tumor primary site, pathological type, histological grade, tumor (T) stage, node (N) stage, metastasis (M) stage, surgical type, bone metastasis, and lung metastasis (12 variables). For CSS risk prediction, the following prognostic factors were identified: age, marital status, tumor primary site, pathological type, histological grade, T stage, N stage, M stage, surgical type, radiation therapy, bone metastasis, and lung metastasis (12 variables). Based on the aforementioned predictive risk factors, prognostic nomograms for OS and CSS were constructed, respectively. The scores for each variable were summed to obtain the predicted survival rates at 1, 3, and 5 years (Figures 1,2).

Figure 1 Nomogram for predicting OS in SBC. M, distant metastasis; N, regional lymph nodes; OS, overall survival; SBC, small bowel cancer; T, primary tumor.

Figure 2 Nomogram for predicting CSS in SBC. CSS, cancer-specific survival; M, distant metastasis; N, regional lymph nodes; SBC, small bowel cancer; T, primary tumor.

Nomogram verification

In this study, the calibration curve, C-index, and area under the receiver operating characteristic curve (AUC) were employed to validate the predictive ability of the nomograms. The C-index values of the nomogram for predicting the overall mortality risk were 0.789 (95% CI: 0.777–0.801) and 0.785 (95% CI: 0.767–0.803) in the training set and verification set, respectively. The C-index of the nomogram for predicting the specific mortality risk was 0.878 (95% CI: 0.874–0.882) in the training set and 0.851 (95% CI: 0.845–0.857) in the verification set. The results showed that the competing risk model had better predictive performance for specific mortality risk than the Cox proportional hazards model for overall mortality risk prediction.

Time-dependent ROC curve analysis was performed on the nomograms (Figure 3). The AUC values of the nomogram for predicting 1-, 3-, and 5-year OS in SBC patients were 0.827 (95% CI: 0.809–0.848), 0.803 (95% CI: 0.790–0.821), and 0.787 (95% CI: 0.775–0.806) in the training set, respectively. In the verification set, the corresponding AUC values were 0.794 (95% CI: 0.758–0.821), 0.800 (95% CI: 0.775–0.821), and 0.807 (95% CI: 0.783–0.828). The AUC values for the nomogram for predicting CSS at 1, 3, and 5 years were 0.865 (95% CI: 0.847–0.883), 0.861 (95% CI: 0.847–0.875), and 0.847 (95% CI: 0.833–0.862) in the training set, respectively. In the verification set, the corresponding AUC values were 0.843 (95% CI: 0.812–0.873), 0.847 (95% CI: 0.824–0.870), and 0.850 (95% CI: 0.829–0.871). As shown in Figure 4, the calibration curves for 1-, 3-, and 5-year OS and CSS approach the diagonal line, indicating that the predicted values of the nomograms are close to the actual ones.

Figure 3 ROC curves of the nomograms for 1-, 3-, and 5-year OS and CSS. AUC, area under the curve; CI, confidence interval; CSS, cancer-specific survival; OS, overall survival; ROC, receiver operating characteristic.

Figure 4 Calibration curves of the nomograms for predicting 1-, 3-, and 5-year OS and CSS. CSS, cancer-specific survival; OS, overall survival.

Discussion

In this study, we developed and validated prognostic nomograms predicting 1-, 3-, and 5-year OS and CSS in patients with SBC using data from the SEER database. A total of 6,863 SBC patients were included and divided into training and verification sets. Twelve independent predictors of OS and CSS were identified using the Cox proportional hazards model and the competing risks model. The constructed nomograms showed strong discriminative ability and good calibration, with C-index values of 0.789 and 0.785 for OS and 0.878 and 0.851 for CSS in the training and verification sets, respectively. AUC values at different time points further confirmed the accuracy and robustness of the model.

Through univariate and multivariate Cox regression models, 12 independent risk factors for OS were found in this study, including age, ethnicity, TNM stage, primary site, marital status, grade, histological type, surgical type, bone metastasis, and lung metastasis. Additionally, the effect on CSS was further assessed using a competing risk model, revealing 12 independent risk factors for CSS, such as age, primary site, tumor grade, TNM stage, histological type, surgical type, radiotherapy, marital status, bone metastasis, and lung metastasis. A competing risk model is an analytical method for handling competing risk events. Currently, there are two main competing risk models, namely the cause-specific risk function (CS) model and the Fine-Gray model. The CS model is suitable for etiologic studies, whereas the Fine-Gray model is suitable for estimating disease risk and prognostic factors (8). The study by Wolbers et al. demonstrated that the Fine-Gray model is more suitable for clinical prediction model analyses (9). Compared with the traditional Cox model, which may overestimate the occurrence risk of specific events, the competing risk model can more truly reflect the influence of covariates on specific mortality risks by modeling multiple outcome events simultaneously, thus improving the accuracy and clinical utility of the prediction model.

In this study, the results of multivariate regression analysis showed that age was an independent risk factor affecting OS and CSS in patients with primary SBC, consistent with the results of previous studies (10,11). Although the effect of age on CSS was relatively small, it had a higher weight in the overall mortality risk and might be related to the fact that older patients are more prone to other complications not directly associated with SBC. Furthermore, advanced age has been recognized as a common adverse prognostic factor for various malignancies, which may be closely associated with declining immune function and overall physiological resilience. In contrast, ethnicity was only associated with overall mortality risk and not significantly associated with cancer-specific mortality. In addition, marital status was also identified as an independent prognostic factor for OS and CSS in this study. Similar conclusions were observed in nomogram model studies focusing on small bowel adenocarcinoma (SBA), where unmarried patients demonstrated significantly worse CSS than married patients, suggesting that marriage may influence patient outcomes through mechanisms such as social support, treatment compliance, and psychological status (4,12). This result further enhances the potential of nomogram models for prognostic prediction in this study. It is important to note that, compared to the aforementioned studies focusing only on patients with adenocarcinoma, all types of primary SBC (including adenocarcinoma, neuroendocrine tumors, and stromal tumors) were included in this study, indicating the model has a stronger broad applicability. Among all included pathological types, the pathological type itself emerged as the most significant independent risk factor affecting overall and specific mortality. Adenocarcinoma demonstrated the highest nomogram score, suggesting its strongest predictive power for prognosis.

Because SBC is rare and highly heterogeneous, there are few studies on the best treatment modalities. The management of SBC has long been based on treatment strategies for colorectal cancer (13). The management of small bowel tumors depends primarily on histological type and stage at diagnosis (14). Surgery is typically the main treatment for SBC (15-17). A German study showed that the proportion of patients with small intestinal adenocarcinoma undergoing surgery was as high as 84.6% (18). and the surgical approach varies depending on the primary tumor’s location and tumor type. Small intestinal neuroendocrine tumors (SB-NETs), the most common tumor type in the small intestine, have more clearly defined surgical treatment objectives (19). One study showed that surgery significantly prolonged survival and effectively improved 5-year survival in SBC patients compared with nonsurgical intervention (20). In patients with liver metastasis, whether the primary lesion is adenocarcinoma or neuroendocrine carcinoma, current evidence suggests that resection of the primary tumor and liver metastatic lesions is beneficial to extending patient survival (21-23). Consistent with these studies, our study identified non-surgical intervention as an important prognostic risk factor for SBC patients.

T stage, N stage, and M stage were shown to be independent prognostic factors for OS and CSS in SBC patients, consistent with previous studies (24). In addition, our model suggests that T4, N2, and M1 are important risk factors for SBC patients. In a prospective study based on 347 SBA patients, T4 (P=0.001) was associated with a higher mortality risk and was an independent prognostic factor for OS in SBA patients (25). Another retrospective study also highlighted T4 as an independent prognostic factor for OS in SBA patients (10). A previous retrospective study has shown adverse effects of lymph node metastases in SBA patients (26). T4 indicates tumor penetration through the intestinal wall with potential invasion of adjacent organs. N2 reflects widespread regional lymph node metastasis. M1 indicates that distant metastasis has been present. These factors all suggest that the tumor biological behavior is more aggressive, thus posing a greater threat to patient survival. Although most studies focused on OS, this study also confirmed that T, N, and M stages also have independent prognostic significance in CSS, further consolidating the important value of TNM staging system in SBC risk stratification and clinical decision-making.

Due to limitations in the SEER database’s recorded data, only 3.3% of patients in this study had radiation therapy information, while 21.4% had chemotherapy information. Non-radiation therapy was identified as an independent prognostic risk factor in the nomogram for OS, and both non-radiation therapy and non-chemotherapy were identified as independent prognostic risk factors in the nomogram for CSS. Systemic chemotherapy remains the most common treatment for small bowel tumors. In a study by Gu et al., it was noted that chemotherapy prolonged survival compared with non-chemotherapy interventions (HR =0.502, P<0.001) (11). Another retrospective study showed that adjuvant chemotherapy following surgery did not improve OS and disease-free survival (DFS) in SBA patients (27). However, chemotherapy did not improve the prognosis of SBC patients in our study. This discrepancy may be attributed to differences in patient characteristics, including disease stage and potential comorbidities, which could affect chemotherapy efficacy. In addition, the SEER database lacks detailed information on chemotherapy regimen, dose, and duration of treatment, limiting our assessment of specific treatment strategies.

Radiation therapy is less frequently mentioned in the treatment of SBC and is relatively understudied. However, one study involving 11 patients suggested a trend toward improved 5-year OS with radiation therapy (28). A study by Yang et al. showed that receiving chemotherapy was not significantly associated with survival in SBC patients (P=0.19) (29). In a study conducted by Ecker et al., the combination of radiation and chemotherapy did not significantly extend survival compared to chemotherapy alone (30). In this study, we found no significant correlation between radiotherapy and OS in SBC patients (P=0.46), but a significant correlation with CSS in a competing risk model (P=0.047). These findings may be attributed to the challenges in delivering precise radiation therapy due to the variable position of the small intestine and the possibility of overlapping between different segments, resulting in potential damage to the surrounding non-tumor-bearing segments (31).

The nomogram for predicting 1-, 3-, and 5-year OS in SBC patients was constructed using 12 factors, including age, race, TNM staging, primary site, marital status, grade classification, histological type, surgical type, bone metastasis, and lung metastasis. The nomogram for predicting 1-, 3-, and 5-year CSS in SBC patients was constructed using 12 factors, including age, primary site, grade classification, TNM staging, histological type, surgical type, radiation therapy, marital status, bone metastasis, and lung metastasis. Individual scores were obtained based on patient conditions, and a higher cumulative total score indicates a lower survival rate. These nomograms provide clinicians with a practical and personalized tool for assessing the survival probability of SBC patients. By integrating multiple demographics and clinicopathological variables, these models can facilitate risk-stratification of patients, guide clinical decision-making, personalize follow-up strategies, and enhance physician-patient communication. These models can also help identify high-risk individuals who may benefit from more intensive treatment or enhanced monitoring.

Inevitably, certain limitations exist in this study. First, this is a retrospective study based on the SEER database. The selection of included factors is limited by the availability of recorded data, omitting the prognostic value of certain hematological indicators and gene mutations. Second, there is also a lack of detailed treatment-related variables, such as specific surgical techniques, chemotherapy regimens, and radiotherapy doses. The absence of comorbidity information that may affect survival further limits the comprehensiveness of the model. Third, the lack of imaging data restricts the integration of deep learning methods, which can otherwise improve the model accuracy. Finally, the verification set was randomly partitioned from the same SEER database without external verification. However, model performance was assessed using C-index, ROC curve, and calibration plot, which showed good predictive accuracy of the model.

Conclusions

This study established and validated nomograms for predicting both overall mortality risk and specific mortality risk in rare SBC. These nomograms have strong predictive power and significant clinical relevance and can assist clinicians in predicting the risks of overall mortality and specific mortality among SBC patients, enabling them to balance treatment options and follow-up protocols.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-818/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-818/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-818/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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