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Open AccessArticle

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

by 1,2,3,*, 1,2,3, 1,2,3,4, 1,2,3,4, 1,2,3,4, 5, 6,7, 6,7, 8,9, 8,9, 10,11, 10,11, 12,13,14,15, 12,13,14, 12,16,17, 12,16,17, 18,19, 18,19, 1,2,4,20, 1,2,3,4,20, 1,2,3,4,20,21, 1,2,3,4,20,† and 1,2,3,†
1
OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, 01307 Dresden, Germany
2
German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 01307 Dresden, Germany
3
National Center for Tumor Diseases (NCT), Partner Site Dresden of the German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus and Technische Universität Dresden, 01307 Dresden, Germany
4
Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
5
Department of Radiotherapy, Hospital Dresden-Friedrichstadt, 01067 Dresden, Germany
6
German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 10117 Berlin, Germany
7
Department of Radiooncology and Radiotherapy, Charité University Hospital, 10117 Berlin, Germany
8
German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 45147 Essen, Germany
9
Department of Radiotherapy, University Hospital Essen, Medical Faculty, University of Duisburg-Essen, 45147 Essen, Germany
10
German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 60596 Frankfurt, Germany
11
Department of Radiotherapy and Oncology, Goethe-University Frankfurt, 60596 Frankfurt, Germany
12
German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 81377 Munich, Germany
13
Department of Radiation Oncology, Ludwig-Maximilians-Universität, 81377 Munich, Germany
14
Clinical Cooperation Group, Personalized Radiotherapy in Head and Neck Cancer, Helmholtz Zentrum, 81377 Munich, Germany
15
Department of Radiation Oncology, Medical University of Innsbruck, Anichstraße 35, A-6020 Innsbruck, Austria
16
Department of Radiation Oncology, Technische Universität München, 81675 Munich, Germany
17
Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
18
German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site, 72076 Tübingen, Germany
19
Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, 72076 Tübingen, Germany
20
Institute of Radiooncology—OncoRay, Helmholtz-Zentrum Dresden—Rossendorf, 01328 Dresden, Germany
21
German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
*
Author to whom correspondence should be addressed.
These authors share senior authorship.
Cancers 2020, 12(10), 3047; https://doi.org/10.3390/cancers12103047
Received: 28 August 2020 / Revised: 7 October 2020 / Accepted: 13 October 2020 / Published: 19 October 2020
(This article belongs to the Special Issue Advances in Head and Neck Squamous Cell Carcinoma (HNSCC))
Radiomic risk models are usually based on imaging features, which are extracted from the entire gross tumour volume (GTV entire ). This approach does not explicitly consider the complex biological structure of the tumours. Therefore, in this retrospective study, we investigated the prognostic value of radiomic analyses based on different tumour sub-volumes using computed tomography imaging of patients with locally advanced head and neck squamous cell carcinoma who were treated with primary radio-chemotherapy. The GTV entire was cropped by different margins to define the rim and corresponding core sub-volumes of the tumour. Furthermore, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. As a result, the models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed an improved performance compared to models based on the corresponding tumour core. This indicates that the consideration of tumour sub-volumes may help to improve radiomic risk models.
Imaging features for radiomic analyses are commonly calculated from the entire gross tumour volume (GTVentire). However, tumours are biologically complex and the consideration of different tumour regions in radiomic models may lead to an improved outcome prediction. Therefore, we investigated the prognostic value of radiomic analyses based on different tumour sub-volumes using computed tomography imaging of patients with locally advanced head and neck squamous cell carcinoma. The GTVentire was cropped by different margins to define the rim and the corresponding core sub-volumes of the tumour. Subsequently, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. Radiomic risk models were developed and validated using a retrospective cohort consisting of 291 patients in one of the six Partner Sites of the German Cancer Consortium Radiation Oncology Group treated between 2005 and 2013. The validation concordance index (C-index) averaged over all applied learning algorithms and feature selection methods using the GTVentire achieved a moderate prognostic performance for loco-regional tumour control (C-index: 0.61 ± 0.04 (mean ± std)). The models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed higher median performances (C-index: 0.65 ± 0.02 and 0.64 ± 0.05, respectively), while models based on the corresponding tumour core volumes performed less (C-index: 0.59 ± 0.01). The difference in C-index between the 5 mm tumour rim and the corresponding core volume showed a statistical trend (p = 0.10). After additional prospective validation, the consideration of tumour sub-volumes may be a promising way to improve prognostic radiomic risk models. View Full-Text
Keywords: radiomic; image-based risk modelling; machine learning; personalised therapy; radiation oncology radiomic; image-based risk modelling; machine learning; personalised therapy; radiation oncology
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Leger, S.; Zwanenburg, A.; Leger, K.; Lohaus, F.; Linge, A.; Schreiber, A.; Kalinauskaite, G.; Tinhofer, I.; Guberina, N.; Guberina, M.; Balermpas, P.; von der Grün, J.; Ganswindt, U.; Belka, C.; Peeken, J.C.; Combs, S.E.; Boeke, S.; Zips, D.; Richter, C.; Krause, M.; Baumann, M.; Troost, E.G.; Löck, S. Comprehensive Analysis of Tumour Sub-Volumes for Radiomic Risk Modelling in Locally Advanced HNSCC. Cancers 2020, 12, 3047.

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