Can Artificial Intelligence Be Used to Predict Response in Rectal Cancer? Current Evidence and Future Possibilities

Area of Science:

• Oncology
• Radiology
• Artificial Intelligence

Background:

• Accurately predicting treatment response in rectal cancer remains a significant clinical challenge.
• Artificial intelligence (AI) presents a potential solution for improving treatment response prediction.
• Current AI applications focus on predicting pathologic complete response (pCR) in rectal cancer patients.

Purpose of the Study:

• To review current AI-driven methods for predicting pathologic complete response in rectal cancer.
• To identify key barriers hindering the clinical translation of AI in this field.
• To highlight future research directions and emerging technologies for AI in rectal cancer treatment.

Main Methods:

• This study is a narrative review of existing literature on AI for rectal cancer treatment response prediction.
• The review synthesizes information on AI methodologies, clinical translation challenges, and future prospects.
• Key barriers discussed include lack of standardization, data limitations, and ethical considerations.

Main Results:

• AI demonstrates potential in predicting treatment response for rectal cancer.
• Significant barriers to clinical implementation include data standardization issues, small/biased training datasets, and domain shift.
• Ethical, regulatory, and medicolegal concerns also impede translation.

Conclusions:

• AI-based prediction of treatment response in rectal cancer holds significant clinical value.
• Methodologically rigorous, multi-institutional collaboration is crucial for safe and equitable AI implementation.
• Future directions include federated learning and digital twins for adaptive therapy.