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

• Published in: Cancers 2020-10, Vol.12 (10), p.3047
• Main Authors: Leger, Stefan, Zwanenburg, Alex, Leger, Karoline, Lohaus, Fabian, Linge, Annett, Schreiber, Andreas, Kalinauskaite, Goda, Tinhofer, Inge, Guberina, Nika, Guberina, Maja, Balermpas, Panagiotis, von der Grün, Jens, Ganswindt, Ute, Belka, Claus, Peeken, Jan C., Combs, Stephanie E., Boeke, Simon, Zips, Daniel, Richter, Christian, Krause, Mechthild, Baumann, Michael, Troost, Esther G.C., Löck, Steffen
• Format: Article
• Language: English
• Subjects: Algorithms; Cancer therapies; Cell growth; Chemotherapy; Computed tomography; Diagnosis; Diagnostic imaging; Feature selection; Gene expression; Head & neck cancer; Head and neck cancer; Hypoxia; Learning algorithms; Lung cancer; Machine learning; Magnetic resonance imaging; Medical imaging; Patients; Prostate; Radiation; Radiomics; Squamous cell carcinoma; Technology application; Tumors
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers12103047

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.