Utilizing Artificial Intelligence for Head and Neck Cancer Outcomes Prediction From Imaging. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Chinnery, Tricia; Arifin, Andrew; Tay, Keng Yeow; Leung, Andrew; Nichols, Anthony C.; Palma, David A.; Mattonen, Sarah A.; Lang, Pencilla
• Other Names: Chong Jaron guest-editor.
• Is Part Of: Canadian Association of Radiologists journal. Volume 72:Issue 1(2021)
• Page Start: 73
• Page End: 85
• Publication Date: 2021-02
• Subjects: head and neck cancer -- artificial intelligence -- machine learning -- radiomics -- predictive modeling; Radiology, Medical -- Periodicals; Radiology, Medical -- Canada -- Periodicals; 616.0757
• DOI: 10.1177/0846537120942134
• Languages: English
• ISSNs: 0846-5371

Abstract

Artificial intelligence (AI)-based models have become a growing area of interest in predictive medicine and have the potential to aid physician decision-making to improve patient outcomes. Imaging and radiomics play an increasingly important role in these models. This review summarizes recent developments in the field of radiomics for AI in head and neck cancer. Prediction models for oncologic outcomes, treatment toxicity, and pathological findings have all been created. Exploratory studies are promising; however, validation studies that demonstrate consistency, reproducibility, and prognostic impact remain uncommon. Prospective clinical trials with standardized procedures are required for clinical translation.