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Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy()

BACKGROUND AND PURPOSE: Hypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomic...

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Detalles Bibliográficos
Autores principales: Socarrás Fernández, Jairo A, Mönnich, David, Leibfarth, Sara, Welz, Stefan, Zwanenburg, Alex, Leger, Stefan, Löck, Steffen, Pfannenberg, Christina, La Fougère, Christian, Reischl, Gerald, Baumann, Michael, Zips, Daniel, Thorwarth, Daniela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536307/
https://www.ncbi.nlm.nih.gov/pubmed/33043157
http://dx.doi.org/10.1016/j.phro.2020.07.003
Descripción
Sumario:BACKGROUND AND PURPOSE: Hypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomics is a surrogate predictor for hypoxia as identified by PET. MATERIALS AND METHODS: Two independent cohorts of HNC patients were used for model development and validation, HN1 (n = 149) and HN2 (n = 47). The training set HN1 consisted of native planning CT data whereas for the validation cohort HN2 also hypoxia PET/CT data was acquired using [(18)F]-Fluoromisonidazole (FMISO). Machine learning algorithms including feature engineering and classifier selection were trained for two-year loco-regional control (LRC) to create optimal CT-radiomics signatures. Secondly, a pre-defined [(18)F]FMISO-PET tumour-to-muscle-ratio (TMR(peak) ≥ 1.6) was used for LRC prediction. Comparison between risk groups identified by CT-radiomics or [(18)F]FMISO-PET was performed using area-under–the-curve (AUC) and Kaplan-Meier analysis including log-rank test. RESULTS: The best performing CT-radiomics signature included two features with nearest-neighbour classification (AUC = 0.76 ± 0.09), whereas AUC was 0.59 for external validation. In contrast, [(18)F]FMISO TMR(peak) reached an AUC of 0.66 in HN2. Kaplan-Meier analysis of the independent validation cohort HN2 did not confirm the prognostic value of CT-radiomics (p = 0.18), whereas for [(18)F]FMISO-PET significant differences were observed (p = 0.02). CONCLUSIONS: No direct correlation of patient stratification using [(18)F]FMISO-PET or CT-radiomics was found in this study. Risk groups identified by CT-radiomics or hypoxia PET showed only poor overlap. Direct assessment of tumour hypoxia using PET seems to be more powerful to stratify HNC patients.