Prognostic prediction nomogram of elderly patients with metastasis colon cancer and distant metastasis pattern: a SEER database analysis

Introduction

Colon cancer (CC) is a prevalent gastrointestinal tumor, ranking fifth in both cancer incidence rate and cancer-related mortality (1). In global cancer surveys, CC ranks as the second most common malignant tumor in women and the third most common in men. The global annual incidence of CC is approximately 1.4 million cases, resulting in nearly 700,000 deaths (2,3).

Elderly individuals—typically defined as those aged over 60 years—account for a substantial proportion of CC cases and experience worse survival outcomes due to age-related decline in physiological reserve, increased comorbidities, and reduced tolerance to aggressive therapies. Despite these challenges, most existing staging systems and prognostic models focus on the general population and often fail to capture the unique prognostic determinants relevant to elderly patients with metastatic CC (4,5). Metastasis is a major cause of mortality, with nearly 50% of patients eventually developing distant metastatic disease, and approximately 25% presenting with metastasis at initial diagnosis (6). In CC patients with distant metastasis, the survival rate of patients is significantly reduced (7,8). These factors not only influence treatment decisions but also complicate survival prediction in clinical practice. Despite this, prognostic models tailored specifically to elderly patients remain scarce.

Several prognostic models and nomograms have been proposed for predicting survival in colorectal cancer. These models typically incorporate demographic factors, tumor characteristics [e.g., tumor-node-metastasis (TNM) stage, histologic grade, tumor location], and treatment information. However, most of them are derived from mixed-age or early-stage populations, and do not account for the distinct biological behavior, treatment tolerance, or metastasis patterns in elderly patients with advanced disease (9,10). Moreover, the lack of stratification by specific metastatic sites (e.g., bone, liver, lung, brain) and omission of elderly-specific risk modifiers limits their applicability in this group.

Given the increasing aging population and the rising burden of metastatic CC in the elderly, there is an urgent clinical need for an accurate, individualized survival prediction tool that can support informed treatment decisions and risk stratification in this subgroup. Addressing this gap, our study aimed to develop and validate a prognostic nomogram based on a large, population-based dataset from the Surveillance, Epidemiology, and End Results (SEER) program. The nomogram integrates demographic, pathological, treatment, and metastasis-specific factors to estimate overall survival (OS) in elderly patients and offers a practical tool to guide personalized clinical management. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-708/rc).

Methods

Patient selection

Data were retrieved from the SEER database (https://seer.cancer.gov/) using SEER*Stat software version 8.4.0.1. The SEER program provides population-based cancer data covering approximately 28% of the U.S. population and is widely considered representative of national trends (11). We included patients diagnosed with CC (ICD-O-3 codes C18.0–C18.9) between 2010 and 2016. Eligible patients were those aged 60 years or older with pathologically confirmed metastatic CC [American Joint Committee on Cancer (AJCC) M1 stage]. Only patients with complete information on demographics, tumor characteristics, treatment, metastasis status, and survival outcome were included. Patients were excluded if they had a survival duration of less than one month or missing critical data, such as age, sex, race, grade, TNM stage, tumor size, carcinoembryonic antigen (CEA) status, treatment details, or distant metastasis information. Ultimately, a total of 6,851 patients met the inclusion criteria. These patients were randomly divided into a training cohort (n=3,411) and a validation cohort (n=3,440) in a 1:1 ratio using simple random sampling to ensure internal comparability and robust model construction.

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Definition and screening of variables for training and validation cohort

Seventeen clinicopathological variables were included in the analysis, namely: age, sex, race, marital status, tumor site, histological grade, tumor (T) stage, node (N) stage, tumor size, CEA level, surgery, radiotherapy, chemotherapy, and presence of bone, brain, liver, and lung metastasis. Tumor sites were categorized as right colon, left colon, and transverse colon. Tumor size was stratified into three groups (<49, 49–72, and >72 mm) using X-tile software, which allows for outcome-based optimal cut-point determination (12). Marital status was categorized as married, single, or other (divorced, widowed, or separated). These variables were used to examine metastasis risk factors and construct a survival prediction model.

Follow-up and outcome measurement

OS was defined as the time from initial diagnosis to death from any cause or the date of last follow-up. Patients alive at the end of follow-up were censored (13). The data cut-off date for follow-up was December 31, 2019, ensuring adequate observation time for survival outcomes. Patients with a survival duration of less than 1 month were excluded to avoid immortal time bias. The primary outcome of interest was OS, which served as the endpoint for model development and validation.

Statistical analysis

All statistical analyses were performed using R software version 4.2.3. Categorical variables were summarized as frequencies and percentages, with comparisons between training and validation cohorts conducted using chi-square tests. To identify factors associated with specific sites of distant metastasis (bone, brain, liver, and lung), univariate and multivariate logistic regression analyses were performed. The results were expressed as odds ratios (ORs) with 95% confidence intervals (CIs). In the training cohort, univariate Cox regression analysis was conducted to identify prognostic variables for OS. Variables with P<0.05 in univariate analysis were entered into a multivariate Cox regression model to determine independent prognostic factors. Hazard ratios (HRs) and 95% CIs were calculated. A prognostic nomogram was then developed based on the significant variables from the multivariate model to estimate 1-, 3-, and 5-year OS probabilities.

To evaluate the predictive performance of the nomogram, three approaches were used. Discrimination was assessed by calculating the area under the time-dependent receiver operating characteristic (ROC) curve (AUC). An AUC greater than 0.70 was considered acceptable, and greater than 0.80 indicated strong predictive ability (14). Calibration plots were generated to compare predicted survival probabilities with actual outcomes. Clinical usefulness was evaluated through decision curve analysis (DCA), which quantified the net clinical benefit across different threshold probabilities (15). All statistical tests were two-sided, and a P value less than 0.05 was considered statistically significant.

Results

Risk prediction of distant metastasis in elderly patients with metastatic CC

Among the 6,851 elderly patients included in the SEER database, there were 228 cases of bone metastasis, 73 cases of brain metastasis, 4,840 cases of liver metastasis, and 1,273 cases of lung metastasis. Through univariate and multivariate logistic regression analysis, N stage, surgery, radiotherapy, and carcinoembryonic antigen were identified as risk factors for bone metastasis in elderly patients with CC. Surgery, radiotherapy, and chemotherapy were identified as risk factors for brain metastasis in elderly patients with CC. Gender, race, tumor site, grade, T stage, N stage, surgery, chemotherapy, radiotherapy, and carcinoembryonic antigen were identified as risk factors for liver metastasis in elderly patients with CC. Race, tumor site, grade, N stage, surgery, radiotherapy, carcinoembryonic antigen, and tumor size were identified as risk factors for lung metastasis in elderly patients with CC (see Figure 1).

Figure 1 Forest plots showing risk factors for distant metastasis in elderly patients with metastatic colon cancer. (A) Risk factors for bone metastasis; (B) risk factors for brain metastasis; (C) risk factors for liver metastasis; (D) risk factors for lung metastasis. AJCC, American Joint Committee on Cancer; CEA, carcinoembryonic antigen; CI, confidence interval; N, node; OR, odds ratio; T, tumor.

Baseline characteristics of patients

A total of 6,851 eligible elderly patients from the SEER database were randomly allocated to a training cohort (n=3,411) and a validation cohort (n=3,440). In both the training and validation cohorts, the majority of patients were aged between 60 and 74 years. There were similar numbers of male and female patients. A significant proportion of the patients were identified as White (78%), with grade II being the predominant histological grading (55%). Concerning treatment, 5,341 patients (78%) underwent surgical procedures, 4,548 patients (66%) received chemotherapy, while a small percentage received radiotherapy (3%). Detailed information is provided in Table 1.

Independent prognostic factors of elderly patients in the training cohort

Univariate Cox regression analysis identified age, tumor site, grade, T stage, N stage, surgery, chemotherapy, CEA, bone metastasis, brain metastasis, liver metastasis, lung metastasis, tumor size, and marital status as prognostic factors for elderly patients. Additionally, multivariate Cox analysis revealed age, tumor site, grade, T stage, N stage, surgery, chemotherapy, CEA, bone metastasis, liver metastasis, lung metastasis, and tumor size as independent risk factors for elderly patients. Refer to Table 2 for details.

Establishment of nomogram for elderly patients with metastatic CC

Based on the results of the multivariate Cox regression analysis in the training cohort, a nomogram was developed to forecast the 1-, 3-, and 5-year prognosis in elderly patients (refer to Figure 2). The nomogram primarily comprises the following independent risk factors: age, tumor site, grade, T stage, N stage, surgery, chemotherapy, CEA, bone metastasis, liver metastasis, lung metastasis, and tumor size. In the training and validation cohorts, the consistency index (C-index) of the nomogram for predicting OS was 0. 713 (95% CI: 0.708–0.718) and 0. 716 (95% CI: 0.711–0.721), respectively.

Figure 2 Prognostic nomogram of metastatic CC in the elderly patient. Each level of these variables was assigned a specific point on the scale. The total score is obtained by adding the scores of each risk factor. AJCC, American Joint Committee on Cancer; CC, colon cancer; CEA, carcinoembryonic antigen; N, node; T, tumor.

Evaluation and validation of nomogram

As widely recognized, in a time-dependent curve, an AUC value of 0.5 implies that the nomogram lacks predictive efficacy, while an AUC value of 1 suggests the nomogram can completely differentiate patients with varying survival rates. A larger value between 0.5 and 1 indicates a stronger discriminative ability of the nomogram. In the current study, the ROC curves demonstrated that the AUC values for the training cohort at 1, 3, and 5 years were 0.785, 0.786, and 0.795, respectively, whereas the AUC values for the validation cohort at 1, 3, and 5 years were 0.792, 0.784, and 0.793. Please refer to Figure 3. Additionally, calibration curves exhibited a high level of consistency between the predicted and observed survival probabilities, as depicted in Figure 4. The decision curves further indicated a strong clinical applicability in predicting the OS of elderly patients, as shown in Figure 5.

Figure 3 Time-dependent ROC curves of the prognostic nomogram. (A) ROC curves for 1-, 3-, and 5-year overall survival in the training cohort; (B) ROC curves for 1-, 3-, and 5-year overall survival in the validation cohort. AUC, area under the curve; FPR, false positive rate; ROC, receiver operating characteristic; TPR, true positive rate.

Figure 4 Calibration curves for the prognostic nomogram. (A) Calibration curves for 1-, 3-, and 5-year overall survival in the training cohort; (B) calibration curves for 1-, 3-, and 5-year overall survival in the validation cohort. In these curves, the gray line serves as an ideal reference line. The closer the solid line of the color (blue/red/green) is to the gray line, the more accurate the model’s predicted survival rate.

Figure 5 DCA for the prognostic nomogram. (A-C) DCA for 1-, 3-, and 5-year overall survival in the training cohort; (D-F) DCA for 1-, 3-, and 5-year overall survival in the validation cohort. AJCC, American Joint Committee on Cancer; DCA, decision curve analysis; N, node; T, tumor.

Survival analyses

Based on the total score from the nomogram, all patients in the training cohort were stratified into low-risk (total score <255), medium-risk (total score between 255 and 394), and high-risk (total score ≥394) groups. The Kaplan-Meier curve demonstrates a distinct differentiation between the different risk groups in both the training and validation cohorts, indicating the effective identification of high-risk patients by the nomogram. Please refer to Figure 6 for details.

Figure 6 Kaplan-Meier survival curves for patients stratified by the nomogram risk score. (A) Survival curves for the low-, medium-, and high-risk groups in the training cohort; (B) Survival curves for the low-, medium-, and high-risk groups in the validation cohort.

Discussion

CC ranks as the fifth leading cause of cancer-related deaths globally. Despite the availability of various treatment methods, the rates of metastasis, recurrence, and mortality associated with CC remain high (16). While the prognosis evaluation system for CC is well-established, it still exhibits shortcomings in clinical application as it often overlooks several crucial risk factors, including age, gender, treatment, and others. In various cancer types, the nomogram has been demonstrated to be more accurate than traditional staging systems (17-20). Elderly patients, as highlighted by research, represent a vulnerable group, demonstrating heightened susceptibility to distant metastasis and presenting with a poorer survival prognosis (21,22). This trend may be associated with their diminished tolerance to surgery, radiotherapy, and chemotherapy in comparison to younger patients. Consequently, early prediction of the risk of CC metastasis and survival prognosis in the elderly holds paramount significance.

In relation to treatment, this study underscores the critical influence of treatment on the survival and prognosis of elderly patients. The findings indicate a notably higher survival rate among CC patients who underwent surgical treatment compared to those who did not. Consequently, for CC patients with the possibility of surgical resection, proactive consideration of surgical treatment can lead to increased survival rates (23,24). The study encompassed patients who received both chemotherapy and radiotherapy, including preoperative and postoperative interventions. The research deduced that receiving both chemotherapy and radiotherapy serves as an independent prognostic factor for elderly patients, corroborating findings from earlier studies (25). In a large randomized controlled trial, it was observed that patients undergoing preoperative neoadjuvant chemotherapy encountered fewer severe postoperative complications compared to the control group. Preoperative neoadjuvant chemotherapy led to notable downstaging of T and N, along with tumor regression, resulting in a lower recurrence rate over a 16-year period in contrast to patients not receiving neoadjuvant chemotherapy (26). Adjuvant radiotherapy has been shown to enhance the local control rate, particularly in patients with T4b stage CC (27). In a retrospective study, it was noted that patients deemed to be at high risk of postoperative recurrence experienced a reduction in the recurrence rate and an improvement in the survival rate with the administration of postoperative radiotherapy (28). These findings have been further supported by other studies as well (29,30). Furthermore, the current study validates that elderly patients with CC who undergo surgery, radiotherapy, and chemotherapy witness a reduction in the risk of metastasis and achieve a more favorable prognosis. Such conclusions serve as a valuable guide for both patients and clinicians in determining clinical treatment strategies.

This study’s findings indicate that tumor site, T stage, and N stage serve as independent risk factors for both metastasis and prognosis in elderly patients, aligning with previous research results (31). Serum CEA, a routine biomarker for diagnosing and monitoring CC (32,33), was identified as a risk factor for distant metastasis and survival prognosis, despite varying outcomes in different research findings (31,34,35). Therefore, regular monitoring of serum CEA levels in CC patients is still recommended. Distant metastases, including brain, lung, bone, and liver metastases, along with tumor site and histological grade, can be utilized as predictive factors, mirroring the traditional TNM stage system (36). Additionally, the specific location of distant metastasis holds crucial importance. Patients with bone, brain, lung, and liver metastases have a higher risk of mortality compared to those without distant metastasis (37). Distinct tumor sites of CC demonstrate significantly different prognoses, consistent with previous research (38). Histological grade also plays a pivotal role in determining the prognosis of CC patients, providing a reference basis for clinical treatment and prognosis assessment. A higher grade signifies a more malignant tumor and a poorer prognosis (5).

The study possesses several notable strengths. Firstly, the utilization of the SEER database provides a substantial volume of information on CC patients, ensuring a sufficiently large sample size that enhances the reliability of the results. Secondly, this study marks the first development of a nomogram dedicated to predicting the prognosis of elderly patients, thereby filling a critical gap in existing research. Thirdly, the model was rigorously validated, with the validation outcomes affirming the stability and dependability of the constructed model.

Despite the strengths of our study, several limitations must be acknowledged. First, this study is based on retrospective data extracted from the SEER database, which may inherently introduce selection bias. Second, the SEER database lacks crucial clinical and biological variables, including comorbidities, immune checkpoint inhibitor use, targeted therapies, and laboratory inflammatory markers such as C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), or lactate dehydrogenase (LDH) levels, all of which may significantly influence patient prognosis and treatment selection. The absence of these variables may affect the accuracy and comprehensiveness of our model. Third, although our nomogram was internally validated using a large, randomly divided validation cohort, it was not tested on external or multicenter datasets. The lack of external validation may limit its generalizability to broader patient populations with diverse demographic and clinical characteristics. Future studies should aim to validate the nomogram using prospective data or independent real-world cohorts. Fourth, molecular and genetic information—such as microsatellite instability (MSI) status, KRAS/NRAS mutations, and BRAF alterations—was not available in SEER and thus not incorporated in our model. These biomarkers are increasingly recognized as critical prognostic and predictive indicators in metastatic CC and may affect response to therapy and survival. Their integration into future prognostic models may enhance accuracy and clinical relevance.

In summary, our study developed and validated a clinically applicable nomogram for elderly patients with metastatic CC, providing individualized survival estimates and supporting treatment decision-making. Nonetheless, to further improve the clinical utility of the model, future research should focus on incorporating molecular and treatment-related biomarkers, ensuring prospective external validation, and exploring dynamic prediction algorithms that can adapt to evolving clinical data.

Conclusions

In conclusion, this study developed and validated a novel prognostic nomogram tailored for elderly patients with metastatic CC, based on a large-scale SEER database analysis. By identifying independent risk factors for distant metastasis and OS—including tumor site, TNM stage, treatment strategies, serum CEA levels, and metastatic patterns—our model demonstrated robust predictive performance and clinical applicability. The nomogram effectively stratifies patients into different risk categories, thereby aiding clinicians in making individualized treatment decisions and optimizing management strategies. Despite certain limitations such as the lack of external validation and missing data on molecular or targeted therapies, this model provides valuable insight into the prognosis of elderly metastatic CC patients and offers a practical tool for improving personalized care and clinical outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-708/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-708/prf

Funding: This work was supported by the Natural Science Foundation of Hunan Province (Nos. 2023JJ60458, 2023JJ30367, 2023JJ30364, 2021JJ30418, 2023JJ30361), the Academic inheritance and communication project of China Academy of Chinese Medical Sciences (No. CI2022E014XB), the Clinical Medical Technology Innovation Guidance Project of Hunan Province (No. 2021SK51010), the Scientific Research Project of Hunan Provincial Health Commission (No. D202319019427), and the Key Project of Hunan Administration of Traditional Chinese Medicine (No. A2023042).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-708/coif). All authors report that this work was supported by the Natural Science Foundation of Hunan Province (Nos. 2023JJ60458, 2023JJ30367, 2023JJ30364, 2021JJ30418, 2023JJ30361), the Academic inheritance and communication project of China Academy of Chinese Medical Sciences (No. CI2022E014XB), the Clinical Medical Technology Innovation Guidance Project of Hunan Province (No. 2021SK51010), the Scientific Research Project of Hunan Provincial Health Commission (No. D202319019427), and the Key Project of Hunan Administration of Traditional Chinese Medicine (No. A2023042). The authors have no other 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. The 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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