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Enrichment of lung cancer computed tomography collections with AI-derived annotations.
Pubmed ID
38177130 (View this publication on the PubMed website)
Digital Object Identifier
Sci Data. 2024 Jan 4; Volume 11 (Issue 1): Pages 25
Krishnaswamy D, Bontempi D, Thiriveedhi VK, Punzo D, Clunie D, Bridge CP, Aerts HJWL, Kikinis R, Fedorov A
  • Brigham and Women's Hospital, Boston, MA, USA.
  • Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
  • Brigham and Women's Hospital, Boston, MA, USA.
  • Radical Imaging, Boston, MA, USA.
  • PixelMed Publishing, Bangor, PA, USA.
  • Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections containing computed tomography images of the chest, NSCLC-Radiomics, and a subset of the National Lung Screening Trial. Using publicly available AI algorithms, we derived volumetric annotations of thoracic organs-at-risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR (Findable, Accessible, Interoperable, Reusable) data principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can aid in cancer imaging.

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