BMC medical informatics and decision making · 2025
Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis.
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Li Bing, Xiao Ming, Zeng Rong, Zhang Le
Аннотация
BACKGROUND: Colorectal cancer is the fourth most deadly cancer, with a high mortality rate and a high probability of recurrence and metastasis. Since continuous examinations and disease monitoring for patients after surgery are currently difficult to perform, it is necessary for us to develop a predictive model for colorectal cancer metastasis and recurrence to improve the survival rate of patients.
RESULTS: Previous studies mostly used only clinical or radiological data, which are not sufficient to explain the in-depth mechanism of colorectal cancer recurrence and metastasis. Therefore, this study proposes such a multiomics data-based predictive model for the recurrence and metastasis of colorectal cancer. LR, SVM, Naïve-bayes and ensemble learning models are used to build this predictive model.
CONCLUSIONS: The experimental results indicate that our proposed multiomics data-based ensemble learning model effectively predicts the recurrence and metastasis of colorectal cancer.
RESULTS: Previous studies mostly used only clinical or radiological data, which are not sufficient to explain the in-depth mechanism of colorectal cancer recurrence and metastasis. Therefore, this study proposes such a multiomics data-based predictive model for the recurrence and metastasis of colorectal cancer. LR, SVM, Naïve-bayes and ensemble learning models are used to build this predictive model.
CONCLUSIONS: The experimental results indicate that our proposed multiomics data-based ensemble learning model effectively predicts the recurrence and metastasis of colorectal cancer.