AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics

TL;DR

This paper reviews AI techniques for medical imaging, focusing on tumor detection, classification, and prognosis, emphasizing radiomics and the potential for clinical translation in oncology imaging.

Contribution

It provides a comprehensive overview of AI-based methods in radiomics, highlighting recent advances and challenges in applying these techniques to clinical oncology imaging.

Findings

AI enables effective tumor detection and classification.

Radiomics offers noninvasive tumor characterization.

Future directions include integrating NLP and neuro-symbolic AI.

Abstract

Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of radiomics and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be utilized as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.