Are estimations of radiomic image markers dispensable due to recent deep learning findings?. Issue 2 (29th August 2019)

• Record Type: Journal Article
• Authors: Obert, Martin
• Is Part Of: European respiratory journal. Volume 54:Issue 2(2019)
• Publication Date: 2019-08-29
• Subjects: Respiratory organs -- Diseases -- Periodicals; Respiration -- Periodicals; 616.2
• DOI: 10.1183/13993003.01185-2019
• Languages: English
• ISSNs: 0903-1936

Abstract

In light of recent deep learning findings, the question arises whether estimations of radiomic markers may soon be dispensable. In this editorial I would, therefore, like to compare two computed tomography (CT) lung studies to discuss this question: a deep learning (DL) cancer screening analysis, recently published in Nature Medicine, and a sparse data radiomic marker sarcoidosis investigation, published in this issue of the European Respiratory Journal . In this context, I would like to touch on three particular aspects: When are radiomic image markers still helpful? What effect does the volume of available data have on the statistical method of choice? How dogmatic should statistical analysis be in clinical applications? In big data screening investigations, deep learning concepts should be directly applied to images. In sparse data investigations radiomic image feature estimations play a key role. http://bit.ly/2KnjlcZ