Comprehensive Analysis of Tumour Sub-Volumes for Radiomic Risk Modelling in Locally Advanced HNSCC

Imaging features for radiomic analyses are commonly calculated from the entire gross tumour volume (GTV <sub>entire</sub><inline-formula>)</inline-formula>. However, tumours are biologically complex and the consideration of different tumour regions in radiomic models may lead...

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Bibliografische gegevens
Hoofdauteurs: Stefan Leger, Alex Zwanenburg, Karoline Leger, Fabian Lohaus, Annett Linge, Andreas Schreiber, Goda Kalinauskaite, Inge Tinhofer, Nika Guberina, Maja Guberina, Panagiotis Balermpas, Jens von der Grün, Ute Ganswindt, Claus Belka, Jan C. Peeken, Stephanie E. Combs, Simon Boeke, Daniel Zips, Christian Richter, Mechthild Krause, Michael Baumann, Esther G.C. Troost, Steffen Löck
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: MDPI AG 2020-10-01
Reeks:Cancers
Onderwerpen:
radiomic
image-based risk modelling
machine learning
personalised therapy
radiation oncology
Online toegang:https://www.mdpi.com/2072-6694/12/10/3047
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