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Journal of International Oncology ›› 2026, Vol. 53 ›› Issue (8): 476-484.doi: 10.3760/cma.j.cn371439-20250823-00077

• Original Article • Previous Articles     Next Articles

Construction of a postoperative survival prediction model for elderly patients with colorectal cancer based on machine learning algorithms

Shen Cailu, Wu Ruirong, Wu You, Ge Xiaosong()   

  1. Department of Medical Oncology， Affiliated Hospital of Jiangnan University， Wuxi 214100， China
  • Received:2025-08-23 Online:2026-08-08 Published:2026-07-21
  • Contact: Ge Xiaosong， Email： gexiaosong@qq.com
  • Supported by:
    National Natural Science Foundation of China(81502042);Natural Science Foundation of Jiangsu Province of China(BK20140171);Guiding Project of Jiangsu Provincial Health Commission(Z2023020)

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Abstract:

Objective To construct a postoperative survival prediction model suitable for elderly patients with colorectal cancer （CRC） based on machine learning algorithms， as well as to identify key factors influencing the prognosis of patients. Methods Clinical data of 3 576 elderly CRC patients who underwent surgical treatment between 2000 and 2019 were obtained from the United States Surveillance， Epidemiology， and End Results （SEER） database. The dataset was randomly divided into the training cohort （n=2 503） and the validation cohort （n=1 073） at a 7∶3 ratio. Least absolute shrinkage and selection operator （LASSO） regression was applied to identify key predictors of cancer-specific survival （CSS）， followed by the construction of LASSO-Cox and DeepSurv deep learning models. Model performance was evaluated using the concordance index （C-index）， receiver operator characteristic （ROC） curves， and Brier score. To analyze the interpretability of the DeepSurv model， the Shapley additive explanation （SHAP） method was applied. The Kaplan-Meier survival curve was plotted and the log-rank test was performed to evaluate the risk stratification ability of the model. The decision curve analysis （DCA） was used to verify the clinical practicability. Results There were no statistically significant differences in baseline characteristics between the training cohort and validation cohort （all P>0.05）. Eight key prognostic factors influencing CSS in elderly CRC patients were screened out using LASSO regression： age， T stage， N stage， histological grade， perineural invasion， positive lymph node ratio， preoperative serum carcinoembryonic antigen （CEA）， and postoperative adjuvant chemotherapy. Multivariate analysis confirmed that N stage （N1： HR=2.43， 95%CI： 1.86-3.17， P<0.001； N2： HR=3.49， 95%CI： 2.32-5.27， P<0.001）， absence of postoperative adjuvant chemotherapy （HR=1.94， 95%CI： 1.51-2.50， P<0.001） ， age≥85 years （HR=1.49， 95%CI： 1.22-1.82， P<0.001）， positive perineural invasion （HR=1.61， 95%CI： 1.26-2.04， P<0.001）， positive preoperative serum CEA （HR=1.44， 95%CI： 1.19-1.75， P<0.001）， histological grade Ⅲ-Ⅳ （HR=1.49， 95%CI： 1.20-1.85， P<0.001）， positive lymph node ratio≥0.09 （HR=1.48， 95%CI： 1.09-2.01， P=0.012）， and T3-4 stage （HR=1.47， 95%CI： 1.09-1.99， P=0.012） were independent risk factors for CSS in elderly patients with CRC. In the training cohort， the DeepSurv model yielded areas under the curve （AUCs） of 0.727 （95%CI： 0.681-0.769）， 0.753 （95%CI： 0.720-0.791）， and 0.744 （95%CI： 0.717-0.773） for predicting 1-， 2-， and 3-year CSS rate， respectively. The LASSO-Cox model yielded corresponding AUCs of 0.720 （95%CI： 0.675-0.764）， 0.748 （95%CI： 0.714-0.782）， and 0.741 （95%CI： 0.712-0.771）. In the validation cohort， the DeepSurv model achieved AUCs of 0.758 （95%CI： 0.699-0.818）， 0.786 （95%CI： 0.743-0.830）， and 0.778 （95%CI： 0.737-0.819） for predicting 1-， 2-， and 3-year CSS rate， respectively. The LASSO-Cox model demonstrated similar performance， with AUCs of 0.757 （95%CI： 0.701-0.812）， 0.783 （95%CI： 0.736-0.829）， and 0.768 （95%CI： 0.724-0.810）. The LASSO-Cox model achieved Brier scores of 0.064， 0.105， and 0.143 for predicting 1-， 2-， and 3-year CSS rate in the training cohort， and 0.058， 0.097， and 0.135 in the validation cohort. The DeepSurv model achieved Brier scores of 0.060， 0.093， and 0.127 in the training cohort， and 0.056， 0.087， and 0.120 in the validation cohort. SHAP analysis identified N stage， age， and postoperative adjuvant chemotherapy as the three most influential variables affecting the predictive performance of the DeepSurv model. Survival analysis revealed that the 3-year CSS rates for the low- and high-risk groups predicted by the LASSO-Cox model were 92.49% and 76.73% in the training cohort （χ2=132.21， P<0.001）， and 94.31% and 76.88% in the validation cohort （χ2=69.07， P<0.001）. The corresponding rates predicted by the DeepSurv model were 92.60% and 76.40% in the training cohort （χ2=142.34， P<0.001）， and 94.60% and 76.20% in the validation cohort （χ2=76.70， P<0.001）. The DCA curve showed that both models had good clinical practicability. Conclusions N stage， age， postoperative adjuvant chemotherapy， positive lymph node ratio， preoperative serum CEA， histological grade， perineural invasion， and T stage are identified as independent prognostic factors in elderly patients with CRC. The LASSO-Cox model and the DeepSurv deep learning model developed based on these variables demonstrate favorable predictive performance and robust risk stratification ability.

Key words: Machine learning, Colorectal neoplasms, Elderly patients, Drug therapy, Prognosis