Machine learning in the prediction of cancer therapy

Area of Science:

• Oncology
• Bioinformatics
• Artificial Intelligence in Medicine

Background:

• Therapeutic resistance is a primary driver of cancer treatment failure and mortality.
• Current cancer treatment strategies rely on subtypes and genetic mutations, which do not always predict response.
• There is a critical need for predictive models to guide personalized drug selection in oncology.

Purpose of the Study:

• To review advancements in predicting therapeutic response using machine learning (ML).
• To outline the fundamental principles of ML algorithms applied to cancer therapy.
• To identify current challenges in developing clinically applicable ML models for treatment prediction.

Main Methods:

• Review of recent literature on machine learning applications in predicting cancer treatment response.
• Explanation of basic machine learning concepts and their relevance to pharmacogenomics.
• Discussion of challenges in translating preclinical ML models to clinical practice.

Main Results:

• Artificial intelligence, particularly ML, shows significant potential in preclinical cancer therapy prediction.
• The development of clinically useful predictive models is hindered by a lack of comprehensive pharmacogenomic data.
• Machine learning algorithms are increasingly being explored for personalized cancer treatment strategies.

Conclusions:

• Machine learning holds promise for improving cancer treatment outcomes by predicting patient response.
• Overcoming data limitations is essential for the clinical implementation of ML-based predictive models.
• Further research is needed to bridge the gap between ML advancements and clinical application in oncology.