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

Jairo A Socarrás Fernández; David Mönnich; Sara Leibfarth; Stefan Welz; Alex Zwanenburg; Stefan Leger; Steffen Löck; Christina Pfannenberg; Christian La Fougère; Gerald Reischl; Michael Baumann; Daniel Zips; Daniela Thorwarth

doi:10.1016/j.phro.2020.07.003

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

Phys Imaging Radiat Oncol. 2020 Jul:15:52-59. doi: 10.1016/j.phro.2020.07.003.

Authors

Jairo A Socarrás Fernández¹, David Mönnich¹, Sara Leibfarth¹, Stefan Welz², Alex Zwanenburg^{3

4}, Stefan Leger^{3

4}, Steffen Löck³, Christina Pfannenberg⁵, Christian La Fougère⁶, Gerald Reischl⁷, Michael Baumann^{3

8}, Daniel Zips^{2

9}, Daniela Thorwarth^{1

9}

Affiliations

¹ Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany.
² Department of Radiation Oncology, University of Tübingen, Germany.
³ OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.
⁴ National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁵ Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany.
⁶ Department of Nuclear Medicine, University of Tübingen, Germany.
⁷ Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Germany.
⁸ German Cancer Research Center DKFZ, Heidelberg, Germany.
⁹ German Cancer Consortium (DKTK), partner Site Tübingen, Tübingen, Germany.

Abstract

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 [¹⁸F]-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 [¹⁸F]FMISO-PET tumour-to-muscle-ratio (TMR_peak ≥ 1.6) was used for LRC prediction. Comparison between risk groups identified by CT-radiomics or [¹⁸F]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, [¹⁸F]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 [¹⁸F]FMISO-PET significant differences were observed (p = 0.02).

Conclusions: No direct correlation of patient stratification using [¹⁸F]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.

Keywords: CT-Imaging; Imaging biomarkers; Machine Learning; PET-Imaging; Quantitative Imaging; Radiomics.