AUTHOR=Haghshomar Maryam , Rodrigues Darren , Kalyan Aparna , Velichko Yury , Borhani Amir 

TITLE=Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

JOURNAL=Frontiers in Oncology

VOLUME=Volume 14 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1362737

DOI=10.3389/fonc.2024.1362737

ISSN=2234-943X

ABSTRACT=Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. The timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is the crucial part for detection of liver tumors; however, conventional imaging has shortcomings in proper characterization of these tumors which leads to need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype and immunophenotype of tumors, without need for tissue biopsy, hence providing guidance for personalized treatment of such tumors.In this review, we provide an overview of radiomics contributions to diagnosis and staging, assessment of response to treatment, and prognostication. We further aim by exploring how AI and radiomics tools can address current challenges in clinical decision-making. These challenges encompass a broad range of tasks, from prognosticating patient outcomes to anticipating treatment response, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. We outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations.