Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers

Andrew Hope, Maikel Verduin, Thomas J. Dilling, Ananya Choudhury, Rianne Fijten, Leonard Wee, Hugo J. W. L. Aerts, Issam El Naqa, Ross Mitchell, Marc Vooijs, Andre Dekker, Dirk de Ruysscher, Alberto Traverso*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Citations (Web of Science)

Abstract

Simple Summary

The management of locally advanced (stages II-III) non-small cell lung cancer patients is very challenging because of poor survival rates and patient/tumor heterogeneity. In this review, we identify the critical points that can be addressed by artificial intelligence (AI) algorithms to improve care of these patients and to present a roadmap for AI applications that will support better treatments.

Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.

Original languageEnglish
Article number2382
Number of pages17
JournalCancers
Volume13
Issue number10
DOIs
Publication statusPublished - May 2021

Keywords

  • lung cancers
  • artificial intelligence
  • radiomics
  • deep learning
  • clinical decision aids
  • TUMOR HYPOXIA
  • FDG-PET
  • RADIOMICS
  • RADIATION
  • CT
  • CLASSIFICATION
  • SCIENCE
  • IMMUNOTHERAPY
  • RADIOTHERAPY
  • SEGMENTATION

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