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Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging 13/2020

12.05.2020 | Original Article

PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma

verfasst von: Stefan P. Haider, Amit Mahajan, Tal Zeevi, Philipp Baumeister, Christoph Reichel, Kariem Sharaf, Reza Forghani, Ahmet S. Kucukkaya, Benjamin H. Kann, Benjamin L. Judson, Manju L. Prasad, Barbara Burtness, Seyedmehdi Payabvash

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 13/2020

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Abstract

Purpose

To devise, validate, and externally test PET/CT radiomics signatures for human papillomavirus (HPV) association in primary tumors and metastatic cervical lymph nodes of oropharyngeal squamous cell carcinoma (OPSCC).

Methods

We analyzed 435 primary tumors (326 for training, 109 for validation) and 741 metastatic cervical lymph nodes (518 for training, 223 for validation) using FDG-PET and non-contrast CT from a multi-institutional and multi-national cohort. Utilizing 1037 radiomics features per imaging modality and per lesion, we trained, optimized, and independently validated machine-learning classifiers for prediction of HPV association in primary tumors, lymph nodes, and combined “virtual” volumes of interest (VOI). PET-based models were additionally validated in an external cohort.

Results

Single-modality PET and CT final models yielded similar classification performance without significant difference in independent validation; however, models combining PET and CT features outperformed single-modality PET- or CT-based models, with receiver operating characteristic area under the curve (AUC) of 0.78, and 0.77 for prediction of HPV association using primary tumor lesion features, in cross-validation and independent validation, respectively. In the external PET-only validation dataset, final models achieved an AUC of 0.83 for a virtual VOI combining primary tumor and lymph nodes, and an AUC of 0.73 for a virtual VOI combining all lymph nodes.

Conclusion

We found that PET-based radiomics signatures yielded similar classification performance to CT-based models, with potential added value from combining PET- and CT-based radiomics for prediction of HPV status. While our results are promising, radiomics signatures may not yet substitute tissue sampling for clinical decision-making.
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Literatur
4.
Zurück zum Zitat AJCC Cancer staging manual (8th edition): Springer International Publishing; 2017. AJCC Cancer staging manual (8th edition): Springer International Publishing; 2017.
5.
Zurück zum Zitat TNM Classification of Malignant Tumours, 8th Edition. Wiley-Blackwell: Union for International Cancer Control; 2016. TNM Classification of Malignant Tumours, 8th Edition. Wiley-Blackwell: Union for International Cancer Control; 2016.
11.
Zurück zum Zitat Zhu Y, Mohamed ASR, Lai SY, Yang S, Kanwar A, Wei L, et al. Imaging-genomic study of head and neck squamous cell carcinoma: associations between radiomic phenotypes and genomic mechanisms via integration of the Cancer Genome Atlas and the Cancer Imaging Archive. JCO Clin Cancer Inform. 2019;3:1–9. https://doi.org/10.1200/CCI.18.00073.CrossRefPubMed Zhu Y, Mohamed ASR, Lai SY, Yang S, Kanwar A, Wei L, et al. Imaging-genomic study of head and neck squamous cell carcinoma: associations between radiomic phenotypes and genomic mechanisms via integration of the Cancer Genome Atlas and the Cancer Imaging Archive. JCO Clin Cancer Inform. 2019;3:1–9. https://​doi.​org/​10.​1200/​CCI.​18.​00073.CrossRefPubMed
12.
Zurück zum Zitat Elhalawani H, Mackin D, Ger RB, Lin T, Mohamed AS, Rock C, et al. FDG-PET imaging-derived radiomics correlates of human papillomavirus status: connecting the dots in the oropharyngeal cancer biology, metabolism, and imaging interplay. International Journal of Radiation Oncology • Biology • Physics. 2018;102:e262. doi:https://doi.org/10.1016/j.ijrobp.2018.07.856. Elhalawani H, Mackin D, Ger RB, Lin T, Mohamed AS, Rock C, et al. FDG-PET imaging-derived radiomics correlates of human papillomavirus status: connecting the dots in the oropharyngeal cancer biology, metabolism, and imaging interplay. International Journal of Radiation Oncology • Biology • Physics. 2018;102:e262. doi:https://​doi.​org/​10.​1016/​j.​ijrobp.​2018.​07.​856.
16.
Zurück zum Zitat Vallières M, Kay-Rivest E, Perrin LJ, Liem X, Furstoss C, Khaouam N, et al. Data from head-neck-PET-CT. The Cancer Imaging Archive; 2017. Vallières M, Kay-Rivest E, Perrin LJ, Liem X, Furstoss C, Khaouam N, et al. Data from head-neck-PET-CT. The Cancer Imaging Archive; 2017.
17.
Zurück zum Zitat Grossberg A, Mohamed A, Elhalawani H, Bennett W, Smith K, Nolan T, et al. Data from Head and Neck Cancer CT Atlas. The Cancer Imaging Archive; 2017. Grossberg A, Mohamed A, Elhalawani H, Bennett W, Smith K, Nolan T, et al. Data from Head and Neck Cancer CT Atlas. The Cancer Imaging Archive; 2017.
19.
Zurück zum Zitat Zuley ML, Jarosz R, Kirk S, Lee Y, Colen R, Garcia K, et al. Radiology data from the Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma [TCGA-HNSC] collection. The Cancer Imaging Archive; 2016. Zuley ML, Jarosz R, Kirk S, Lee Y, Colen R, Garcia K, et al. Radiology data from the Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma [TCGA-HNSC] collection. The Cancer Imaging Archive; 2016.
20.
Zurück zum Zitat Wee L, Dekker A. Data from head-neck-Radiomics-HN1 [data set]. The Cancer Imaging Archive; 2019. Wee L, Dekker A. Data from head-neck-Radiomics-HN1 [data set]. The Cancer Imaging Archive; 2019.
27.
Zurück zum Zitat Zwanenburg A, Leger S, Vallières M, Löck S. Image biomarker standardisation initiative. arXiv e-prints; 2016. Zwanenburg A, Leger S, Vallières M, Löck S. Image biomarker standardisation initiative. arXiv e-prints; 2016.
28.
Zurück zum Zitat Pyradiomics-community. Pyradiomics Documentation Release 2.1.2. 2018. Pyradiomics-community. Pyradiomics Documentation Release 2.1.2. 2018.
34.
Zurück zum Zitat R Development Core team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019. R Development Core team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019.
35.
Zurück zum Zitat Revelle W. psych: procedures for psychological, psychometric, and personality research. Version 1.8.12 ed. Northwestern University, Evanston, Illinois, USA; 2018. Revelle W. psych: procedures for psychological, psychometric, and personality research. Version 1.8.12 ed. Northwestern University, Evanston, Illinois, USA; 2018.
40.
Zurück zum Zitat Snoek J, Larochelle H, Adams RP. Practical Bayesian optimization of machine learning algorithms. Adv Neural Inf Proces Syst. 2012;25:2960–8. Snoek J, Larochelle H, Adams RP. Practical Bayesian optimization of machine learning algorithms. Adv Neural Inf Proces Syst. 2012;25:2960–8.
41.
Zurück zum Zitat Yan Y. rBayesian optimization: Bayesian optimization of hyperparameters. Version 1.1.0 ed; 2016. Yan Y. rBayesian optimization: Bayesian optimization of hyperparameters. Version 1.1.0 ed; 2016.
42.
Zurück zum Zitat DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.CrossRefPubMed DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.CrossRefPubMed
48.
Zurück zum Zitat Chen T, Guestrin C. XGBoost: a scalable tree boosting system. arXiv e-prints; 2016. Chen T, Guestrin C. XGBoost: a scalable tree boosting system. arXiv e-prints; 2016.
49.
Zurück zum Zitat Jin Y. Tree boosting with XGBoost—why does XGBoost win “every” machine learning competition? ; 2017. Jin Y. Tree boosting with XGBoost—why does XGBoost win “every” machine learning competition? ; 2017.
Metadaten
Titel
PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma
verfasst von
Stefan P. Haider
Amit Mahajan
Tal Zeevi
Philipp Baumeister
Christoph Reichel
Kariem Sharaf
Reza Forghani
Ahmet S. Kucukkaya
Benjamin H. Kann
Benjamin L. Judson
Manju L. Prasad
Barbara Burtness
Seyedmehdi Payabvash
Publikationsdatum
12.05.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 13/2020
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-020-04839-2

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