Artificial Intelligence in Colon Cancer: Advances, Challenges, and Future Perspectives

Area of Science:

• Oncology
• Medical Informatics
• Artificial Intelligence

Background:

• Colorectal cancer (CRC) presents a significant global health burden with increasing incidence in younger demographics.
• Current diagnostic and therapeutic approaches face challenges in accuracy and efficiency, necessitating innovative solutions.
• Artificial intelligence (AI) offers transformative potential across the entire CRC care continuum.

Purpose of the Study:

• To review the current applications and impact of AI in colorectal cancer management.
• To highlight AI's role in enhancing diagnostic accuracy, treatment planning, and patient follow-up.
• To discuss challenges and future directions for AI adoption in precision oncology for CRC.

Main Methods:

• Review of AI applications in endoscopy (computer-aided detection/diagnosis), pathology (whole-slide imaging analysis), radiology (lesion detection, radiomics), and surgical robotics.
• Analysis of AI's role in treatment planning, prognostic modeling, and survivorship care using multimodal data.
• Discussion of challenges including data heterogeneity, validation, interpretability, and regulatory hurdles.

Main Results:

• AI significantly improves adenoma detection rates in endoscopy (25-50%) and supports real-time lesion characterization.
• In pathology, AI enables automated triage, in silico prediction of microsatellite instability, and prognostic stratification.
• Radiology applications include AI-driven lesion detection, staging, and radiomic virtual biopsies for molecular profiling.
• AI facilitates personalized treatment planning, predicts neoadjuvant response, and identifies therapeutic targets.
• AI enhances surgical precision through robotic platforms and improves intraoperative safety.
• Multimodal AI datasets provide superior survival and recurrence predictions, optimizing survivorship care.

Conclusions:

• AI is a cornerstone technology poised to enhance precision oncology in colorectal cancer.
• Widespread adoption requires addressing challenges in data, validation, interpretability, and regulation.
• Advances in multimodal AI and federated learning are expected to overcome current barriers, optimizing CRC care delivery and patient outcomes.