Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures. (December 2020)

• Record Type: Journal Article
• Authors: Sanduleanu, Sebastian; Jochems, Arthur; Upadhaya, Taman; Even, Aniek J.G.; Leijenaar, Ralph T.H.; Dankers, Frank J.W.M.; Klaassen, Remy; Woodruff, Henry C.; Hatt, Mathieu; Kaanders, Hans J.A.M.; Hamming-Vrieze, Olga; van Laarhoven, Hanneke W.M.; Subramiam, Rathan M.; Huang, Shao Hui; O'Sullivan, Brian; Bratman, Scott V.; Dubois, Ludwig J.; Miclea, Razvan L.; Di Perri, Dario; Geets, Xavier; Crispin-Ortuzar, Mireia; Apte, Aditya; Deasy, Joseph O.; Oh, Jung Hun; Lee, Nancy Y.; Humm, John L.; Schöder, Heiko; De Ruysscher, Dirk; Hoebers, Frank; Lambin, Philippe
• Is Part Of: Radiotherapy and oncology. Volume 153(2020)
• Page Start: 97
• Page End: 105
• Publication Date: 2020-12
• Subjects: Radiomics -- Tumor hypoxia; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.10.016
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: A CT ± FDG-PET radiomics signature accurately discerned normoxic from hypoxic tumors. A significant survival split was found between CTAgnostic, -classified hypoxia strata. There were 117 significant yet low hypoxia gene-CTAgnostic feature associations. By identifying hypoxic patients we can potentially "enrich" hypoxia targeting trials. The disease-specific radiomics signatures perform better than disease-agnostic ones. The performance of the CT signature was lower than the CT-FDG 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 [ 18 F]-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.