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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Precision medicine in oncology - machine learning recommendations.

¹Department of Pharmacotherapy and Pharmaceutical Care, Faculty of Pharmacy, Medical University of Warsaw Warsaw, Poland.

American Journal of Cancer Research

|May 11, 2023

Summary

This article provides recommendations for using machine learning methods in cancer research. It aims to guide the application of artificial intelligence in oncology for improved outcomes.

Keywords:

Oncology machine learning methods precision medicine

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Area of Science:

Oncology
Machine Learning
Artificial Intelligence

Background:

The integration of machine learning (ML) in oncology is rapidly advancing.
There is a growing need for standardized recommendations to guide ML implementation in cancer care.

Discussion:

Discusses the potential of ML algorithms in cancer diagnosis, prognosis, and treatment selection.
Highlights the importance of data quality, validation, and ethical considerations in ML applications for oncology.

Key Insights:

Provides actionable recommendations for developing and deploying ML models in oncology.
Emphasizes the need for collaboration between oncologists, data scientists, and regulatory bodies.

Outlook:

Predicts a future where ML is an integral part of precision oncology.
Suggests ongoing research and development are crucial for realizing the full potential of ML in cancer treatment.