Digestive Disease Interventions 2020; 04(01): 073-081
DOI: 10.1055/s-0040-1705097
Review Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Supervised Machine Learning in Oncology: A Clinician's Guide

Nikitha Murali
1  Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
,
Ahmet Kucukkaya
1  Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
,
Alexandra Petukhova
1  Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
,
John Onofrey
1  Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
2  Department of Urology, Yale University School of Medicine, New Haven, Connecticut
,
Julius Chapiro
1  Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
› Author Affiliations
Funding This review was partially supported by the National Institutes of Health/National Cancer Institute Grant # R01CA206180.
Further Information

Publication History

09 December 2019

25 December 2019

Publication Date:
02 March 2020 (online)

Abstract

The widespread adoption of electronic health records has resulted in an abundance of imaging and clinical information. New data-processing technologies have the potential to revolutionize the practice of medicine by deriving clinically meaningful insights from large-volume data. Among those techniques is supervised machine learning, the study of computer algorithms that use self-improving models that learn from labeled data to solve problems. One clinical area of application for supervised machine learning is within oncology, where machine learning has been used for cancer diagnosis, staging, and prognostication. This review describes a framework to aid clinicians in understanding and critically evaluating studies applying supervised machine learning methods. Additionally, we describe current studies applying supervised machine learning techniques to the diagnosis, prognostication, and treatment of cancer, with a focus on gastroenterological cancers and other related pathologies.