Machine learning in image-based outcome prediction after radiotherapy: A review.

Area of Science:

• Oncology
• Medical Imaging
• Machine Learning

Background:

• Machine learning (ML) integration in radiotherapy is a growing field for predicting patient outcomes.
• Key areas of focus include patient survival, disease recurrence, and treatment-induced toxicity.

Purpose of the Study:

• To review the current applications of ML in radiotherapy for outcome prediction.
• To highlight the use of medical imaging and clinical data in ML models.
• To identify challenges and future directions for ML in radiotherapy.

Main Methods:

• Comprehensive literature review of ML applications in radiotherapy.
• Analysis of studies utilizing medical imaging modalities and clinical data.
• Examination of research trends, particularly in survival analysis.

Main Results:

• ML shows significant potential in predicting radiotherapy outcomes, including survival and toxicity.
• Integration of medical imaging with clinical data presents both opportunities and complexities.
• Survival analysis is a prominent area of ML application in radiotherapy research.

Conclusions:

• ML in radiotherapy holds great promise for advancing precision medicine and enhancing patient care.
• Further research is needed to address challenges such as data collection, privacy, standardization, and interpretability.
• Expanded research on abdominal and pancreatic cancers is advocated.