Published online Dec 15, 2024. doi: 10.4251/wjgo.v16.i12.4597
Revised: September 7, 2024
Accepted: September 14, 2024
Published online: December 15, 2024
Processing time: 237 Days and 23.7 Hours
Colorectal cancer (CRC) is characterized by high heterogeneity, aggressiveness, and high morbidity and mortality rates. With machine learning (ML) algorithms, patient, tumor, and treatment features can be used to develop and validate models for predicting survival. In addition, important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.
To construct prognostic prediction models and screen important variables for patients with stage I to III CRC.
More than 1000 postoperative CRC patients were grouped according to survival time (with cutoff values of 3 years and 5 years) and assigned to training and testing cohorts (7:3). For each 3-category survival time, predictions were made by 4 ML algorithms (all-variable and important variable-only datasets), each of which was validated via 5-fold cross-validation and bootstrap validation. Important variables were screened with multivariable regression methods. Model performance was evaluated and compared before and after variable screening with the area under the curve (AUC). SHapley Additive exPlanations (SHAP) further demonstrated the impact of important variables on model decision-making. Nomograms were constructed for practical model application.
Our ML models performed well; the model performance before and after important parameter identification was consistent, and variable screening was effective. The highest pre- and postscreening model AUCs 95% confidence intervals in the testing set were 0.87 (0.81-0.92) and 0.89 (0.84-0.93) for overall survival, 0.75 (0.69-0.82) and 0.73 (0.64-0.81) for disease-free survival, 0.95 (0.88-1.00) and 0.88 (0.75-0.97) for recurrence-free survival, and 0.76 (0.47-0.95) and 0.80 (0.53-0.94) for distant metastasis-free survival. Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets. The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors. The nomograms were created.
We constructed a comprehensive, high-accuracy, important variable-based ML architecture for predicting the 3-category survival times. This architecture could serve as a vital reference for managing CRC patients.
Core Tip: We developed and validated a promising machine learning architecture for predicting the 3-category survival times (cutoff values of 3 years and 5 years) for four survival times (overall, disease-free, recurrence-free, and distant metastasis-free survival) and screened corresponding important variables. Fivefold cross validation and bootstrap validation were conducted. The models were evaluated with the area under the curve (AUC); moreover, the effectiveness of our variable screening methods was evaluated by comparing the models’ pre- and post-screening AUCs. SHapley Additive exPlanations were used to explain the decision-making process. Nomograms were drawn for various applications.