• Stefan Leger, Alex Zwanenburg, Karoline Pilz, Sebastian Zschaeck, Klaus Zöphel, Jörg Kotzerke, Andreas Schreiber, Daniel Zips, Mechthild Krause, Michael Baumann, Troost Esther G C EGC OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dr, Christian Richter, and Steffen Löck.
• OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany. Electronic address: Stefan.Leger@oncoray.de.
• Radiother Oncol. 2019 Jan 1; 130: 10-17.

Background And PurposeThe development of radiomic risk models to predict clinical outcome is usually based on pre-treatment imaging, such as computed tomography (CT) scans used for radiation treatment planning. Imaging data acquired during the course of treatment may improve their prognostic performance. We compared the performance of radiomic risk models based on the pre-treatment CT and CT scans acquired in the second week of therapy.Material And MethodsTreatment planning and second week CT scans of 78 head and neck squamous cell carcinoma patients treated with primary radiochemotherapy were collected. 1538 image features were extracted from each image. Prognostic models for loco-regional tumour control (LRC) and overall survival (OS) were built using 6 feature selection methods and 6 machine learning algorithms. Prognostic performance was assessed using the concordance index (C-Index). Furthermore, patients were stratified into risk groups and differences in LRC and OS were evaluated by log-rank tests.ResultsThe performance of radiomic risk model in predicting LRC was improved using the second week CT scans (C-Index: 0.79), in comparison to the pre-treatment CT scans (C-Index: 0.65). This was confirmed by Kaplan-Meier analyses, in which risk stratification based on the second week CT could be improved for LRC (p = 0.002) compared to pre-treatment CT (p = 0.063).ConclusionIncorporation of imaging during treatment may be a promising way to improve radiomic risk models for clinical treatment adaption, i.e., to select patients that may benefit from dose modification.Copyright © 2018 Elsevier B.V. All rights reserved.

28 keywords...

hide…