Supervised Machine Learning in Oncology: A Clinician's Guide.

Area of Science:

• Medical Informatics
• Artificial Intelligence in Medicine
• Oncology

Background:

• Electronic health records generate vast amounts of imaging and clinical data.
• Advanced data processing technologies offer potential for deriving clinical insights from large datasets.
• Supervised machine learning (ML) is a key technique for analyzing labeled data to solve problems.

Purpose of the Study:

• To provide a framework for clinicians to understand and evaluate studies using supervised machine learning methods.
• To review current applications of supervised ML in cancer diagnosis, prognostication, and treatment.
• To focus on the application of supervised ML in gastroenterological cancers and related pathologies.

Main Methods:

• Review of existing literature on supervised machine learning in oncology.
• Description of a framework for critical appraisal of ML studies.
• Categorization of studies based on application in cancer diagnosis, prognostication, and treatment.

Main Results:

• Supervised ML is increasingly applied in oncology for tasks such as cancer diagnosis, staging, and predicting patient outcomes.
• Current research demonstrates the utility of ML in analyzing complex datasets for clinical decision support.
• Specific examples of ML applications in gastroenterological cancers are highlighted.

Conclusions:

• Supervised machine learning holds significant promise for advancing cancer care through data-driven insights.
• Clinicians need structured frameworks to critically assess the validity and applicability of ML-based studies.
• Further research and validation are essential for integrating ML tools into routine oncological practice.