Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Radiotherapy and oncology, 2020-12, Vol.153, p.97-105

Ort / Verlag

Ireland: Elsevier B.V

Erscheinungsjahr

2020

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• •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. 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. 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. 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). 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.