CC BY 4.0 · Yearb Med Inform 2024; 33(01): 099-101
DOI: 10.1055/s-0044-1800727
Section 2: Cancer Informatics
Synopsis

Cancer Informatics: Novel Methods and Applications of Artificial Intelligence in Cancer Care Delivery

Sanjay Aneja
1   Yale Cancer Center, New Haven, CT, USA
2   Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
3   Department of Bioinformatics and Data Science, Yale School of Medicine, New Haven, CT, USA
,
Ravi B. Parikh
4   Abramson Cancer Center, Philadelphia, PA
5   Department of Medicine, Philadelphia, PA
6   Department of Medical Ethics and Health Policy, Philadelphia, PA
› Institutsangaben

Summary

Objectives: To summarize significant research contributions on cancer informatics published in 2023, an extensive search using PubMed/MEDLINE was conducted to identify the scientific contributions published in 2023 that address topics in cancer. The selection process comprised three steps: (i) ten candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of three best papers was conducted by the editorial board of the Yearbook.

Results: The two selected papers demonstrate advances in the clinical implementation of cancer informatics methodologies. Both studies highlight translation of informatics methodologies to improve cancer outcomes.

Conclusions: Cancer informatics is a maturing subfield of bioinformatics. As novel methodologies continue to emerge, further emphasis will be placed on rigorous clinical validation and real-world scalability of such solutions to positively impact patient outcomes.



Publikationsverlauf

Artikel online veröffentlicht:
08. April 2025

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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  • References

  • 1 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers, Methods Inf. Med., vol. 54, no 2, p. 135-144, 2015, doi: 10.3414/ME14-01-0031.
  • 2 Manz CR, et al. Long-term Effect of Machine Learning-Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer: A Randomized Clinical Trial, JAMA Oncol., vol. 9, no 3, p. 414-418, 2023. doi: 10.1001/jamaoncol.2022.6303.
  • 3 Placido D, et al. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat Med 29, 1113–1122 (2023). doi:10.1038/s41591-023-02332-5.
  • 4 Chakrabarty S, et al. Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO), JCO Clin. Cancer Inform., vol. 7, p. e2200177, 2023. doi: 10.1200/CCI.22.00177.
  • 5 Botsis T. et al. Precision Oncology Core Data Model to Support Clinical Genomics Decision Making, JCO Clin. Cancer Inform., vol. 7, p. e2200108, 2023. doi: 10.1200/CCI.22.00108.
  • 6 Flores-Toro JA et al. The Childhood Cancer Data Initiative: Using the Power of Data to Learn From and Improve Outcomes for Every Child and Young Adult With Pediatric Cancer, J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol., vol. 41, no 24, p. 4045-4053, 2023. doi: 10.1200/JCO.22.02208.
  • 7 Ng AY et al. Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer, Nat. Med., vol. 29, no 12, p. 3044-3049, 2023. doi: 10.1038/s41591-023-02625-9.
  • 8 Kann BH et al. Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial, Lancet Digit. Health, vol. 5, no 6, p. e360-e369, 2023. doi: 10.1016/S2589-7500(23)00046-8.
  • 9 Hollon T et al. Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging, Nat. Med., vol. 29, no 4, p. 828-832, 2023. doi: 10.1038/s41591-023-02252-4.
  • 10 Barata C et al. A reinforcement learning model for AI-based decision support in skin cancer, Nat. Med., vol. 29, no 8, p. 1941-1946, 2023. doi: 10.1038/s41591-023-02475-5.
  • 11 Ogier du Terrail J et al. Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer, Nat. Med., vol. 29, no 1, p. 135-146, 2023. doi: 10.1038/s41591-022-02155-w.