» Articles » PMID: 34853038

Cancer Needs a Robust "Metadata Supply Chain" to Realize the Promise of Artificial Intelligence

Overview
Journal Cancer Res
Specialty Oncology
Date 2021 Dec 2
PMID 34853038
Citations 1
Authors
Affiliations
Soon will be listed here.
Abstract

Profound advances in computational methods, including artificial intelligence (AI), present the opportunity to use the exponentially growing volume and complexity of available cancer measurements toward data-driven personalized care. While exciting, this opportunity has highlighted the disconnect between the promise of compute and the supply of high-quality data. The current paradigm of ad-hoc aggregation and curation of data needs to be replaced with a "metadata supply chain" that provides robust data in context with known provenance, that is, lineage and comprehensive data governance that will allow the promise of AI technology to be realized to its full potential in clinical practice.

Citing Articles

Continuous multimodal data supply chain and expandable clinical decision support for oncology.

Chang J, Kim H, Baek E, Choi J, Lim J, Kim J NPJ Digit Med. 2025; 8(1):128.

PMID: 40016534 PMC: 11868524. DOI: 10.1038/s41746-025-01508-2.


Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO).

Chakrabarty S, Abidi S, Mousa M, Mokkarala M, Hren I, Yadav D JCO Clin Cancer Inform. 2023; 7:e2200177.

PMID: 37146265 PMC: 10281444. DOI: 10.1200/CCI.22.00177.

References
1.
Hickman S, Baxter G, Gilbert F . Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations. Br J Cancer. 2021; 125(1):15-22. PMC: 8257639. DOI: 10.1038/s41416-021-01333-w. View

2.
Lewis P, Amankwaa-Frempong E, Makwani H, Nsingo M, Addison E, Acquah G . Radiotherapy Planning and Peer Review in Sub-Saharan Africa: A Needs Assessment and Feasibility Study of Cloud-Based Technology to Enable Remote Peer Review and Training. JCO Glob Oncol. 2021; 7:10-16. PMC: 8081549. DOI: 10.1200/GO.20.00188. View

3.
Dhruva S, Ross J, Akar J, Caldwell B, Childers K, Chow W . Aggregating multiple real-world data sources using a patient-centered health-data-sharing platform. NPJ Digit Med. 2020; 3:60. PMC: 7170944. DOI: 10.1038/s41746-020-0265-z. View

4.
Chung C, Kalpathy-Cramer J, Knopp M, Jaffray D . In the Era of Deep Learning, Why Reconstruct an Image at All?. J Am Coll Radiol. 2021; 18(1 Pt B):170-173. DOI: 10.1016/j.jacr.2020.09.050. View

5.
Mobadersany P, Yousefi S, Amgad M, Gutman D, Barnholtz-Sloan J, Velazquez Vega J . Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A. 2018; 115(13):E2970-E2979. PMC: 5879673. DOI: 10.1073/pnas.1717139115. View