Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures Journal Article

  

Authors:	Sanduleanu, S.; Jochems, A.; Upadhaya, T.; Even, A. J. G.; Leijenaar, R. T. H.; Dankers, F. J. W. M.; Klaassen, R.; Woodruff, H. C.; Hatt, M.; Kaanders, H. J. A. M.; Hamming-Vrieze, O.; van Laarhoven, H. W. M.; Subramiam, R. M.; Huang, S. H.; O'Sullivan, B.; Bratman, S. V.; Dubois, L. J.; Miclea, R. L.; Di Perri, D.; Geets, X.; Crispin-Ortuzar, M.; Apte, A.; Deasy, J. O.; Oh, J. H.; Lee, N. Y.; Humm, J. L.; Schöder, H.; De Ruysscher, D.; Hoebers, F.; Lambin, P.
Article Title:	Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures
Abstract:	Background: Tumor hypoxia increases resistance to radiotherapy and systemic therapy. Our aim was to develop and validate a disease-agnostic and disease-specific CT (+FDG-PET) based radiomics hypoxia classification signature. Material and methods: A total of 808 patients with imaging data were included: N = 100 training/N = 183 external validation cases for a disease-agnostic CT hypoxia classification signature, N = 76 training/N = 39 validation cases for the H&N CT signature and N = 62 training/N = 36 validation cases for the Lung CT signature. The primary gross tumor volumes (GTV) were manually defined by experts on CT. In order to dichotomize between hypoxic/well-oxygenated tumors a threshold of 20% was used for the [18F]-HX4-derived hypoxic fractions (HF). A random forest (RF)-based machine-learning classifier/regressor was trained to classify patients as hypoxia-positive/ negative based on radiomic features. Results: A 11 feature “disease-agnostic CT model” reached AUC's of respectively 0.78 (95% confidence interval [CI], 0.62–0.94), 0.82 (95% CI, 0.67–0.96) and 0.78 (95% CI, 0.67–0.89) in three external validation datasets. A “disease-agnostic FDG-PET model” reached an AUC of 0.73 (0.95% CI, 0.49–0.97) in validation by combining 5 features. The highest “lung-specific CT model” reached an AUC of 0.80 (0.95% CI, 0.65–0.95) in validation with 4 CT features, while the “H&N-specific CT model” reached an AUC of 0.84 (0.95% CI, 0.64–1.00) in validation with 15 CT features. A tumor volume-alone model was unable to significantly classify patients as hypoxia-positive/ negative. A significant survival split (P = 0.037) was found between CT-classified hypoxia strata in an external H&N cohort (n = 517), while 117 significant hypoxia gene-CT signature feature associations were found in an external lung cohort (n = 80). Conclusion: The disease-specific radiomics signatures perform better than the disease agnostic ones. By identifying hypoxic patients our signatures have the potential to enrich interventional hypoxia-targeting trials. © 2020 The Author(s)
Keywords:	tumor hypoxia; radiomics
Journal Title:	Radiotherapy and Oncology
Volume:	153
ISSN:	0167-8140
Publisher:	Elsevier Inc.
Date Published:	2020-12-01
Start Page:	97
End Page:	105
Language:	English
DOI:	10.1016/j.radonc.2020.10.016
PUBMED:	33137396
PROVIDER:	scopus
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097729232&doi=10.1016%2fj.radonc.2020.10.016&partnerID=40&md5=8acbccdf4a75e9a977c2355b987bba1a
Notes:	Article -- Export Date: 4 January 2021 -- Source: Scopus

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