TY  - JOUR
A1  - Leger, Stefan
A1  - Zwanenburg, Alex
A1  - Leger, Karoline
A1  - Lohaus, Fabian
A1  - Linge, Annett
A1  - Schreiber, Andreas
A1  - Kalinauskaite, Goda
A1  - Tinhofer, Inge
A1  - Guberina, Nika
A1  - Guberina, Maja
A1  - Balermpas, Panagiotis
A1  - Müller-von der Grün, Jens
A1  - Ganswindt, Ute
A1  - Belka, Claus
A1  - Peeken, Jan Caspar
A1  - Combs, Stephanie E.
A1  - Boeke, Simon
A1  - Zips, Daniel
A1  - Richter, Christian
A1  - Krause, Mechthild
A1  - Baumann, Michael
A1  - Troost, Esther Gera Cornelia
A1  - Löck, Steffen
T1  - Comprehensive analysis of tumour sub-volumes for radiomic risk modelling in locally advanced HNSCC
T2  - Cancers
N2  - Simple Summary: 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.

Abstract: 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.
KW  - radiomic
KW  - image-based risk modelling
KW  - machine learning
KW  - personalised therapy
KW  - radiation oncology
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/56537
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-565371
SN  - 2072-6694
VL  - 12
IS  - 10, 3047
PB  - MDPI
CY  - Basel
ER  -