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Machine learning based lung cancer prediction models have been proposed to assist clinicians in managing incidental or screen detected indeterminate pulmonary nodules. Such systems may be able to reduce variability in nodule classification, improve decision making and ultimately reduce the number of benign nodules that are needlessly followed or worked-up. In this article, we provide an overview of the main lung cancer prediction approaches proposed to date and highlight some of their relative strengths and weaknesses. We discuss some of the challenges in the development and validation of such techniques and outline the path to clinical adoption.

Original publication

DOI

10.21037/tlcr.2018.05.15

Type

Journal article

Journal

Transl Lung Cancer Res

Publication Date

06/2018

Volume

7

Pages

304 - 312

Keywords

Pulmonary nodules, decision making, lung, lung neoplasms, machine learning