TY - JOUR
T1 - Non-invasive imaging prediction of tumor hypoxia
T2 - A novel developed and externally validated CT and FDG-PET-based radiomic signatures
AU - Sanduleanu, Sebastian
AU - Jochems, Arthur
AU - Upadhaya, Taman
AU - Even, Aniek J. G.
AU - Leijenaar, Ralph T. H.
AU - Dankers, Frank J. W. M.
AU - Klaassen, Remy
AU - Woodruff, Henry C.
AU - Hatt, Mathieu
AU - Kaanders, Hans J. A. M.
AU - Hamming-Vrieze, Olga
AU - van Laarhoven, Hanneke W. M.
AU - Subramiam, Rathan M.
AU - Huang, Shao Hui
AU - O'Sullivan, Brian
AU - Bratman, Scott
AU - Dubois, Ludwig J.
AU - Miclea, Razvan L.
AU - Di Perri, Dario
AU - Geets, Xavier
AU - Crispin-Ortuzar, Mireia
AU - Apte, Aditya
AU - Deasy, Joseph O.
AU - Oh, Jung Hun
AU - Lee, Nancy Y.
AU - Humm, John L.
AU - Schoder, Heiko
AU - De Ruysscher, Dirk
AU - Hoebers, Frank
AU - Lambin, Philippe
N1 - Funding Information:
Authors acknowledge financial support from ERC advanced grant (ERC-ADG-2015, n° 694812 - Hypoximmuno), ERC-PoC n°SEP-210494895, ERC-2020-PoC: 957565-AUTO.DISTINCT, Authors also acknowledge financial support from SME Phase 2 (RAIL - n°673780), EUROSTARS (DART, DECIDE, COMPACT-12053), the European Program H2020-2015-17 (ImmunoSABR - n° 733008, PREDICT - ITN - n° 766276, FETOPEN- SCANnTREAT - n° 899549, CHAIMELEON - n° 952172, EuCanImage – n° 952103), TRANSCAN Joint Transnational Call 2016 (JTC2016 “CLEARLY”- n° UM 2017-8295) and Interreg V-A Euregio Meuse-Rhine (“Euradiomics” - n° EMR4). This research is also supported by the Dutch technology Foundation STW/NWO (grant n° 10696 DuCAT & n° P14-19 Radiomics STRaTegy), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs. Dr Philippe Lambin reports, within and outside the submitted work, grants/sponsored research agreements from Varian medical, Oncoradiomics, ptTheragnostic/DNAmito, Health Innovation Ventures. He received an advisor/presenter fee and/or reimbursement of travel costs/external grant writing fee and/or in kind manpower contribution from Oncoradiomics, BHV, Merck, Varian, Elekta, ptTheragnostic and Convert pharmaceuticals. Dr Lambin has shares in the company Oncoradiomics, Convert pharmaceuticals, MedC2 and LivingMed Biotech, he is co-inventor of two issued patents with royalties on radiomics (PCT/NL2014/050248, PCT/NL2014/050728) licensed to Oncoradiomics and one issue patent on mtDNA (PCT/EP2014/059089) licensed to ptTheragnostic/DNAmito, three non-patented invention (softwares) licensed to ptTheragnostic/DNAmito, Oncoradiomics and Health Innovation Ventures and three non-issues, non licensed patents on Deep Learning-Radiomics and LSRT (N2024482, N2024889, N2024889). He confirms that none of the above entities or funding was involved in the preparation of this paper. Dr. Woodruff and Dr. Jochems have (minority) shares in the company Oncoradiomics. Dr. Ralph Leijenaar has shares in, and is Chief Technology Officer of, the company Oncoradiomics. He is co-inventor of an issued patent with royalties related to radiomics (PCT/NL2014/050728) licensed to Oncoradiomics. No further foreseen conflicts of interest.
Funding Information:
Authors acknowledge financial support from ERC advanced grant (ERC-ADG-2015, n° 694812 - Hypoximmuno), ERC-PoC n°SEP-210494895, ERC-2020-PoC: 957565-AUTO.DISTINCT, Authors also acknowledge financial support from SME Phase 2 (RAIL - n°673780), EUROSTARS (DART, DECIDE, COMPACT-12053), the European Program H2020-2015-17 (ImmunoSABR - n° 733008, PREDICT - ITN - n° 766276, FETOPEN- SCANnTREAT - n° 899549, CHAIMELEON - n° 952172, EuCanImage – n° 952103), TRANSCAN Joint Transnational Call 2016 (JTC2016 “CLEARLY”- n° UM 2017-8295) and Interreg V-A Euregio Meuse-Rhine (“Euradiomics” - n° EMR4). This research is also supported by the Dutch technology Foundation STW/NWO (grant n° 10696 DuCAT & n° P14-19 Radiomics STRaTegy), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs.
Publisher Copyright:
© 2020 The Author(s)
PY - 2020/12
Y1 - 2020/12
N2 - 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 [F-18]-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. (C) 2020 The Author(s). Published by Elsevier B.V.
AB - 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 [F-18]-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. (C) 2020 The Author(s). Published by Elsevier B.V.
KW - Radiomics
KW - Tumor hypoxia
KW - UPTAKE DISTRIBUTIONS
KW - CANCER PATIENTS
KW - LUNG-TUMORS
KW - RADIOTHERAPY
KW - HEAD
KW - DISCRETIZATION
KW - REPEATABILITY
KW - ANGIOGENESIS
KW - METHODOLOGY
KW - INFORMATION
U2 - 10.1016/j.radonc.2020.10.016
DO - 10.1016/j.radonc.2020.10.016
M3 - Article
C2 - 33137396
SN - 0167-8140
VL - 153
SP - 97
EP - 105
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
ER -