Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.

Area of Science:

• Oncology
• Biomedical Data Science
• Machine Learning

Background:

• Gynecological cancers (breast, cervical, ovarian) are detected late due to non-specific symptoms and lack of reliable screening.
• Early prediction methods are crucial for improving survival rates, guiding personalized treatment, and reducing healthcare burdens.

Purpose of the Study:

• To review recent advancements in machine learning (ML) models for oncologic prediction in gynecologic oncology.
• To highlight the potential of AI-driven ML in improving cancer screening, risk classification, and survival modeling.

Main Methods:

• Review of current literature on ML applications in gynecologic oncology.
• Discussion of standard ML algorithms (SVM, Random Forests) and deep learning (DL) models (CNNs).
• Exploration of emerging techniques like explainable AI, federated learning (FL), and multi-omics fusion.

Main Results:

• ML models show high potential in cancer type identification, progression monitoring, and treatment design.
• AI models can integrate diverse datasets (clinical, genomic, imaging) to identify subtle patterns for accurate risk prediction.
• Challenges include data inconsistency, model interpretability, and clinical integration.

Conclusions:

• ML is revolutionizing precision oncology for gynecological cancers, enabling better patient-centered outcomes.
• Explainable AI, FL, and multi-omics fusion are key to developing reliable and clinically applicable ML models.
• The transformative role of ML promises improved care for women affected by gynecological cancers.