Back to search

Article

Uncertainty Quantification in Radiogenomics: EGFR Amplification in Glioblastoma

2020-05-26

Abstract excerpt

<h4>ABSTRACT</h4> <h4>BACKGROUND</h4> Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
5535f44a-6192-5d18-9e0c-4de96838e2c6
DOI
10.1101/2020.05.22.20110288
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Uncertainty Quantification in Radiogenomics: EGFR Amplification in GlioblastomaDOI 10.1101/2020.05.22.20110288
Select a neighboring publication to make it the new centre.