Leveraging automated machine learning to predict colon cancer prognosis from clinical features and risk groups: a retrospective cohort study

초록

Background: Predicting colon cancer recurrence is crucial for determining the need for adjuvant therapy after curative resection. However, clinical decisions often rely on limited features, even when a large amount of data is available. Methods: We assessed the clinical utility of automated machine learning (AutoML) models to predict the prognosis of colon cancer patients from a tertiary hospital using clinical features, pathologic characteristics, and blood markers. We also compared these AutoML models to manually trained and tuned models and evaluated survival predictions. Results: We found comparable performance between linear and ensemble models, and the predicted prognosis was significantly associated with overall survival and disease-free survival outcomes. Interpretable machine learning models identified T and N staging as important features and highlighted the prognostic immune and nutritional index (PINI) as a meaningful biomarker. The XGBoost model predicted prognosis with an AUC of 0.798 in an independent test set from a different hospital, demonstrating the model's interoperability. Additionally, the model was able to distinguish stage IIA patients that would benefit from adjuvant chemotherapy, a complex and difficult decision for clinicians. We also showed that simplified models generally maintained predictive accuracy, and that the automated approach was equally predictive as manually curated models. Conclusion: With extensive validation through multiple test sets and internal cross-validation, this work underscores the potential of AutoML in identifying survival-related signatures in colon cancer from routinely collected data, providing clinicians with valuable insights for personalized treatment strategies.

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