Utilizing Artificial Intelligence for Head and Neck Cancer Outcomes Prediction From Imaging

被引：22

作者：

Chinnery, Tricia ^{[1
]}

Arifin, Andrew ^{[2
]}

Tay, Keng Yeow ^{[3
]}

Leung, Andrew ^{[3
]}

Nichols, Anthony C. ^{[4
]}

Palma, David A. ^{[2
]}

Mattonen, Sarah A. ^{[1
,2
]}

Lang, Pencilla ^{[2
]}

机构：

[1] Western Univ, Dept Med Biophys, London, ON, Canada

[2] Western Univ, Dept Oncol, London, ON, Canada

[3] Western Univ, Dept Med Imaging, London, ON, Canada

[4] Western Univ, Dept Otolaryngol Head & Neck Surg, London, ON, Canada

来源：

CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES | 2021年 / 72卷 / 01期

关键词：

head and neck cancer; artificial intelligence; machine learning; radiomics; predictive modeling; MACHINE LEARNING-METHODS; LOCALLY ADVANCED HEAD; EXTRANODAL EXTENSION; PROGNOSTIC VALUE; OROPHARYNGEAL CANCER; RADIATION-THERAPY; DE-ESCALATION; RADIOMICS; RADIOTHERAPY; TOXICITIES;

D O I：

10.1177/0846537120942134

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

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.

引用

页码：73 / 85

页数：13

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