Artificial Intelligence for Radiation Oncology Applications Using Public Datasets

Overview

Journal Semin Radiat Oncol

Specialties Oncology
Radiology

Date 2022 Oct 6

PMID 36202442

Authors

Kareem A Wahid

Enrico Glerean

Jaakko Sahlsten

Joel Jaskari

Kimmo Kaski

Mohamed A Naser

Renjie He

Abdallah S R Mohamed

Clifton D Fuller

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain.

Citing Articles

Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention.

Dafni M, Shih M, Manoel A, Yousif M, Spathi S, Harshal C Cancer Causes Control. 2024; .

PMID: 39672997 DOI: 10.1007/s10552-024-01942-9.

Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge.

Wahid K, Dede C, El-Habashy D, Kamel S, Rooney M, Khamis Y ArXiv. 2024; .

PMID: 39650598 PMC: 11623708.

Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.

Wahid K, Kaffey Z, Farris D, Humbert-Vidan L, Moreno A, Rasmussen M Radiother Oncol. 2024; 201:110542.

PMID: 39299574 PMC: 11648575. DOI: 10.1016/j.radonc.2024.110542.

Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning.

Sahlsten J, Jaskari J, Wahid K, Ahmed S, Glerean E, He R Commun Med (Lond). 2024; 4(1):110.

PMID: 38851837 PMC: 11162474. DOI: 10.1038/s43856-024-00528-5.

Towards equitable AI in oncology.

Viswanathan V, Parmar V, Madabhushi A Nat Rev Clin Oncol. 2024; 21(8):628-637.

PMID: 38849530 DOI: 10.1038/s41571-024-00909-8.

References

Fedorov A, R Longabaugh W, Pot D, Clunie D, Pieper S, Aerts H . NCI Imaging Data Commons. Cancer Res. 2021; 81(16):4188-4193. PMC: 8373794. DOI: 10.1158/0008-5472.CAN-21-0950. View

Peloquin D, DiMaio M, Bierer B, Barnes M . Disruptive and avoidable: GDPR challenges to secondary research uses of data. Eur J Hum Genet. 2020; 28(6):697-705. PMC: 7411058. DOI: 10.1038/s41431-020-0596-x. View

Hunter Chapman C, Gabeau D, Pinnix C, Deville Jr C, Gibbs I, Winkfield K . Why Racial Justice Matters in Radiation Oncology. Adv Radiat Oncol. 2020; 5(5):783-790. PMC: 7340406. DOI: 10.1016/j.adro.2020.06.013. View

Elhalawani H, Lin T, Volpe S, Mohamed A, White A, Zafereo J . Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges. Front Oncol. 2018; 8:294. PMC: 6107800. DOI: 10.3389/fonc.2018.00294. View

Aryanto K, Oudkerk M, van Ooijen P . Free DICOM de-identification tools in clinical research: functioning and safety of patient privacy. Eur Radiol. 2015; 25(12):3685-95. PMC: 4636522. DOI: 10.1007/s00330-015-3794-0. View

Oreiller V, Andrearczyk V, Jreige M, Boughdad S, Elhalawani H, Castelli J . Head and neck tumor segmentation in PET/CT: The HECKTOR challenge. Med Image Anal. 2022; 77:102336. DOI: 10.1016/j.media.2021.102336. View

Mayo C, Kessler M, Eisbruch A, Weyburne G, Feng M, Hayman J . The big data effort in radiation oncology: Data mining or data farming?. Adv Radiat Oncol. 2017; 1(4):260-271. PMC: 5514231. DOI: 10.1016/j.adro.2016.10.001. View

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P . The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013; 26(6):1045-57. PMC: 3824915. DOI: 10.1007/s10278-013-9622-7. View

Diaz D, Suneja G, Jagsi R, Barry P, Thomas Jr C, Deville Jr C . Mitigating Implicit Bias in Radiation Oncology. Adv Radiat Oncol. 2021; 6(5):100738. PMC: 8339323. DOI: 10.1016/j.adro.2021.100738. View

10.

Kalpathy-Cramer J, Freymann J, Kirby J, Kinahan P, Prior F . Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive. Transl Oncol. 2014; 7(1):147-52. PMC: 3998686. DOI: 10.1593/tlo.13862. View

11.

Fuller C, van Dijk L, Thompson R, Scott J, Ludmir E, Thomas Jr C . Meeting the Challenge of Scientific Dissemination in the Era of COVID-19: Toward a Modular Approach to Knowledge-Sharing for Radiation Oncology. Int J Radiat Oncol Biol Phys. 2020; 108(2):496-505. PMC: 7462881. DOI: 10.1016/j.ijrobp.2020.06.066. View

12.

Cardenas C, Mohamed A, Yang J, Gooding M, Veeraraghavan H, Kalpathy-Cramer J . Head and neck cancer patient images for determining auto-segmentation accuracy in T2-weighted magnetic resonance imaging through expert manual segmentations. Med Phys. 2020; 47(5):2317-2322. PMC: 7322982. DOI: 10.1002/mp.13942. View

13.

Willemink M, Koszek W, Hardell C, Wu J, Fleischmann D, Harvey H . Preparing Medical Imaging Data for Machine Learning. Radiology. 2020; 295(1):4-15. PMC: 7104701. DOI: 10.1148/radiol.2020192224. View

14.

Schwarz C, Kremers W, Therneau T, Sharp R, Gunter J, Vemuri P . Identification of Anonymous MRI Research Participants with Face-Recognition Software. N Engl J Med. 2019; 381(17):1684-1686. PMC: 7091256. DOI: 10.1056/NEJMc1908881. View

15.

Li X, Morgan P, Ashburner J, Smith J, Rorden C . The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods. 2016; 264:47-56. DOI: 10.1016/j.jneumeth.2016.03.001. View

16.

Molnar-Gabor F, Korbel J . Genomic data sharing in Europe is stumbling-Could a code of conduct prevent its fall?. EMBO Mol Med. 2020; 12(3):e11421. PMC: 7059003. DOI: 10.15252/emmm.201911421. View

17.

Richardson J, Bouquin D, Tmanova L, Wright D . Information and informatics literacies of first-year medical students. J Med Libr Assoc. 2015; 103(4):198-202. PMC: 4613382. DOI: 10.3163/1536-5050.103.4.008. View

18.

Bennett W, Smith K, Jarosz Q, Nolan T, Bosch W . Reengineering Workflow for Curation of DICOM Datasets. J Digit Imaging. 2018; 31(6):783-791. PMC: 6261183. DOI: 10.1007/s10278-018-0097-4. View

19.

Tenopir C, Allard S, Douglass K, Aydinoglu A, Wu L, Read E . Data sharing by scientists: practices and perceptions. PLoS One. 2011; 6(6):e21101. PMC: 3126798. DOI: 10.1371/journal.pone.0021101. View

20.

Morid M, Borjali A, Del Fiol G . A scoping review of transfer learning research on medical image analysis using ImageNet. Comput Biol Med. 2020; 128:104115. DOI: 10.1016/j.compbiomed.2020.104115. View