Post-radiochemotherapy PET radiomics in head and neck cancer – The influence of radiomics implementation on the reproducibility of local control tumor models

• Published in: Radiotherapy and oncology Vol. 125; no. 3; pp. 385 - 391
• Main Authors: Bogowicz, Marta, Leijenaar, Ralph T.H., Tanadini-Lang, Stephanie, Riesterer, Oliver, Pruschy, Martin, Studer, Gabriela, Unkelbach, Jan, Guckenberger, Matthias, Konukoglu, Ender, Lambin, Philippe
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01-12-2017
• Subjects: Carcinoma, Squamous Cell - diagnostic imaging; Carcinoma, Squamous Cell - therapy; Chemoradiotherapy; Fluorodeoxyglucose F18; Head and Neck Neoplasms - diagnostic imaging; Head and Neck Neoplasms - therapy; Humans; Local tumor control modeling; Neoplasm Recurrence, Local; Post-radiochemotherapy 18F-FDG PET; Prognosis; Radiomics; Reproducibility; Reproducibility of Results; Software implementation; Squamous Cell Carcinoma of Head and Neck; Post-radiochemotherapy 18F-FDG PET; Reproducibility; Software implementation; Local tumor control modeling; Radiomics
• ISSN: 0167-8140; 1879-0887
• DOI: 10.1016/j.radonc.2017.10.023

This study investigated an association of post-radiochemotherapy (RCT) PET radiomics with local tumor control in head and neck squamous cell carcinoma (HNSCC) and evaluated the models against two radiomics software implementations.
649 features, available in two radiomics implementations and based on the same definitions, were extracted from HNSCC primary tumor region in 18F-FDG PET scans 3 months post definitive RCT (training cohort n = 128, validation cohort n = 50) and compared using the intraclass correlation coefficient (ICC). Local recurrence models were trained, separately for both implementations, using principal component analysis (PCA) and the least absolute shrinkage and selection operator. The reproducibility of the concordance indexes (CI) in univariable Cox regression for features preselected in PCA and the final multivariable models was investigated using respective features from the other implementation.
Only 80 PET radiomic features yielded ICC > 0.8 in the comparison between the implementations. The change of implementation caused high variability of CI in the univariable analysis. However, both final multivariable models performed equally well in the training and validation cohorts (CI > 0.7) independent of radiomics implementation.
The two post-RCT PET radiomic models, based on two different software implementations, were prognostic for local tumor control in HNSCC. However, 88% of the features was not reproducible between the implementations.