Machine Learning for Endometrial Cancer Prediction and Prognostication.

Area of Science:

• Oncology
• Computational Biology
• Bioinformatics

Background:

• Endometrial cancer (EC) is a common uterine malignancy with significant mortality, especially when diagnosed at advanced stages.
• Current EC diagnostic methods are expensive, time-consuming, and not universally accessible.
• Computational biology and machine learning (ML) present opportunities for rapid, cost-effective cancer surveillance.

Purpose of the Study:

• To provide a comprehensive review of ML applications in endometrial cancer.
• To analyze the potential of ML modalities for EC prevention, screening, detection, and prognosis.
• To encourage collaborative research between oncologists, data scientists, and bioinformaticians in EC.

Main Methods:

• Literature review of existing research on endometrial cancer.
• Analysis of machine learning techniques applied to cancer diagnosis and prognosis.
• Discussion of ML's role in various stages of EC patient management.

Main Results:

• Machine learning holds significant potential for improving EC diagnosis and treatment.
• ML can enhance early detection, risk assessment, and personalized treatment strategies for EC.
• The integration of ML can lead to more efficient and accessible EC surveillance systems.

Conclusions:

• Machine learning offers a promising avenue for advancing endometrial cancer research and clinical practice.
• Further investigation and collaboration are needed to fully leverage ML for combating EC.
• ML can aid in developing customized treatment plans and improving patient outcomes in endometrial cancer.