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| Zdroj: |
Clinical Oncology Week; 6/17/2024, p1056-1056, 1p |
| Abstrakt: |
A recent report from the Department of Precision Medicine discusses research on colon cancer recurrence. The study aimed to develop prognostic models for predicting colorectal cancer recurrence using machine learning models and a limited number of Carcinoembryonic Antigen (CEA) measurements. The gradient boosting classifier model demonstrated superior performance, achieving an Area Under the Curve (AUC) score of 0.81. The study highlights the potential of machine learning in recurrence prediction for colorectal cancer and the importance of early identification of individuals at higher risk of recurrence. Further validation and development of risk-based follow-up strategies are needed to improve patient outcomes and healthcare efficiency. [Extracted from the article] |
| Databáze: |
Complementary Index |
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