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


Bogowicz, Marta; Leijenaar, Ralph T H; Tanadini-Lang, Stephanie; Riesterer, Oliver; Pruschy, Martin; Studer, Gabriela; Unkelbach, Jan; Guckenberger, Matthias; Konukoglu, Ender; Lambin, Philippe (2017). Post-radiochemotherapy PET radiomics in head and neck cancer - The influence of radiomics implementation on the reproducibility of local control tumor models. Radiotherapy and Oncology, 125(3):385-391.

Abstract

Purpose 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.
Materials and methods 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.
Results 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.
Conclusion 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.

Abstract

Purpose 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.
Materials and methods 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.
Results 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.
Conclusion 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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Radiation Oncology
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Oncology, Radiology Nuclear Medicine and imaging, Hematology
Language:English
Date:6 November 2017
Deposited On:16 Nov 2017 14:29
Last Modified:05 Sep 2018 12:02
Publisher:Elsevier
ISSN:0167-8140
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.radonc.2017.10.023
PubMed ID:29122358

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