Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Colorectal cancer (CRC) metastasis significantly impacts mortality and patient survival.
• Current diagnostic methods for CRC metastasis lack optimal sensitivity and are prone to variability.
• Machine learning (ML) and deep learning (DL) offer advanced capabilities for analyzing complex patient data.

Purpose of the Study:

• To provide a comprehensive review of ML and DL approaches for early CRC metastasis prediction.
• To highlight existing gaps in comparative studies of these predictive models.
• To examine the application of these technologies in predicting metastasis to specific sites.

Main Methods:

• Exploration of DL techniques, including convolutional neural networks (CNNs) like GoogleNet, VGGNet, ResNet, and U-Net.
• Integration of multi-modal data (imaging, clinical, histological) and transfer learning for enhanced prediction.
• Review of traditional ML algorithms (logistic regression, random forests) and their comparison with DL models.

Main Results:

• DL models, particularly CNNs, demonstrate effectiveness in identifying complex data patterns for metastasis prediction.
• Integration of multi-modal data and transfer learning improves individualized treatment strategies and patient outcomes.
• DL models incorporating radiomics and transfer learning often outperform traditional ML algorithms.

Conclusions:

• ML and DL show significant promise for improving the early prediction of colorectal cancer metastasis.
• Addressing challenges like data quality, interpretability, ethical concerns, and computational costs is crucial for clinical integration.
• Future research should focus on explainable AI, resource optimization, ethical frameworks, and validation across diverse populations.