Radiomics-based artificial intelligence (AI) models in colorectal cancer (CRC) diagnosis, metastasis detection, prognosis, and treatment response

TL;DR

This review discusses how radiomics and AI models analyze medical images to improve diagnosis, staging, prognosis, and treatment prediction in colorectal cancer, highlighting recent advances and future challenges.

Contribution

It provides a comprehensive overview of radiomics applications in CRC, emphasizing the integration of deep learning and machine learning for personalized patient management.

Findings

Radiomics can distinguish benign from malignant lesions.

AI models predict metastasis and treatment response.

Challenges include data standardization and clinical integration.

Abstract

With a high rate of morbidity and mortality, colorectal cancer (CRC) ranks third in mortality among cancers. By analyzing the texture properties of images and quantifying the heterogeneity of tumors, radiomics and radiogenomics are non-invasive methods to determine the biological properties of CRC. Recently, several articles have discussed the application of radiomics in different aspects of CRC. Therefore, given the large amount of data published, this review aims to discuss how radiomics can be used for distinguishing benign and malignant colorectal lesions, diagnosing, staging, predicting prognosis and treatment response, and predicting lymph node and hepatic metastasis of CRC, based on radiomic features extracted from magnetic resonance imaging (MRI), computed tomography (CT), esophageal ultrasonography (EUS), and positron emission tomography-CT (PET-CT). Challenges in bringing radiomics to clinical application and future solutions have also been discussed. With the progress made in radiomics and the application of deep and machine learning in this area, radiomics can become one of the main tools for the personalized management of CRC patients shortly.