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. 2024 Jan-Dec:31:10732748241303650.
doi: 10.1177/10732748241303650.

Evaluation of Risk Factors, and Development and Validation of Prognostic Prediction Models for Distant Metastasis in Patients With Rectal Cancer: A Study Based on the SEER Database and a Chinese Population

Huiru Zhang  1 , Haojun Wang  1 , Yan Yao  2 , Lijuan Liu  3 , Fubin Feng  3 , Huayao Li  4 , Changgang Sun  1   3   4
Affiliations

Evaluation of Risk Factors, and Development and Validation of Prognostic Prediction Models for Distant Metastasis in Patients With Rectal Cancer: A Study Based on the SEER Database and a Chinese Population

Huiru Zhang et al. Cancer Control. 2024 Jan-Dec.

Abstract

Purpose: This study aimed to construct a prediction model regarding risk factors and prognostic factors for distant metastasis of T1-T3 stage rectal cancer. For this purpose, a population-based retrospective cohort study was conducted.

Methods: Data on 7872 patients diagnosed with rectal cancer between 2004 and 2020 were obtained from the Surveillance, Epidemiology, and End Results database, of whom 746 had distant metastases at diagnosis. Independent risk factors for distant metastasis of rectal cancer were determined using univariate and multivariate logistic regression analyses. Cox proportional hazards regression analyses clarified the independent prognostic factors for distant metastases of rectal cancer. A 7:3 randomization process was used to place all patients into the training and internal validation groups. Furthermore, we retrospectively collected clinical data from 226 patients who had both rectal cancer and distant metastases between 2012 and 2024 at the Weifang Hospital of Traditional Chinese Medicine. We used the calibration curve, DCA curve, C-index, and area under the curve (AUC) to assess the discriminatory and pre-precision qualities of the models.

Results: The multivariate logistic regression analysis identified race, tumor grade, T stage, N stage, radiotherapy, chemotherapy, surgery, tumor size, and histological subtype as risk factors for distant metastases in rectal cancer, with AUC values for both training and validation sets exceeding 0.8. Using Cox regression analysis, we determined that the age, sex, tumor size, surgery, chemotherapy, and radiotherapy were independent predictors of distant metastasis of rectal cancer. In the prognostic model, the C-index of the training cohort was 0.687 (95% CI: 0.6615-0.7125), that of the internal validation cohort was 0.692 (95% CI: 0.6508-0.7332), and that of the external validation cohort was 0.704 (0.6785-0.7295).

Conclusion: Our nomogram can predict risk factors and analyze the 1-, 2-, and 3 year prognosis of distant metastases in patients with rectal cancer, providing valuable guidance for future clinical work.

Keywords: distant metastasis; factual database; logistic models; nomogram; prognosis; rectal cancer; risk factors.

Plain language summary

Purpose: This study aimed to construct a prediction model regarding risk factors and prognostic factors for distant metastasis of rectal cancer with T1-T3 stage.

Methods: Data of patients diagnosed with rectal cancer between 2004 and 2020 were obtained from the Surveillance, Epidemiology, and End Results database. Independent risk factors for distant metastasis of rectal cancer were determined using univariate and multivariate logistic regression analyses. Cox proportional risk regression analyses clarified the independent prognostic factors for distant metastases of rectal cancer. A 7:3 randomization process was used to place all patients into the training and internal validation groups. Furthermore, as part of the validation cohort, we retrospectively collected clinical data from 226 patients who had both rectal cancer and distant metastases between 2012 and 2024 at the Weifang Hospital of Traditional Chinese Medicine. We used the calibration curve, DCA curve, C-index, and area under the curve (AUC) to assess the discriminatory and pre-precision qualities of the models.

Results: The multivariate logistic regression analysis revealed that the race, tumor grade, T, N, radiotherapy, chemotherapy, surgery, tumor size, and histological subtype were among the risk factors for distant metastases in rectal cancer, and the AUC values for both the training and validation sets in the risk model were greater than 0.8. Using Cox regression analysis, we determined that the age, sex, tumor size, surgery, chemotherapy, and radiotherapy were independent predictors of distant metastasis of rectal cancer. In the prognostic model, the C-index of the training cohort was 0.687 (95% CI: 0.6615-0.7125), that of the internal validation cohort was 0.692 (95% CI: 0.6508-0.7332), and that of the external validation cohort was 0.704 (0.6785-0.7295).

Conclusion: Our nomogram can predict risk factors and analyze the 1-, 2-, and 3 year prognosis of patients with metastatic rectal cancer, providing valuable guidance for future clinical work.

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Conflict of interest statement

Declaration of Conflicting InterestThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
A flowchart showing patient screening details.
Figure 2.
Figure 2.
Construction and validation of a diagnostic nomogram (A) Development and validation of a diagnostic risk model for assessing the distant metastasis of rectal cancer. Rectal cancer distant metastasis risk model. (B) Area under the ROC curve for the training set. (C) The training set nomogram correction plot. (D) Decision curve analysis of training set. (E) Area under the ROC curve for the validation set. (F) Validation of nomogram correction plot. (G) Decision curve analysis of the validation set.
Figure 3.
Figure 3.
Nomogram for predicting 1, 2 and 3 year overall survival in patients with metastatic rectal cancer.
See this image and copyright information in PMC

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