Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations

TL;DR

This paper enhances public lung cancer imaging datasets by adding AI-generated annotations, including organ, landmark, and radiomics features, to facilitate research and development of automated analysis tools.

Contribution

It introduces a method to automatically annotate large CT datasets with AI, enriching public data with detailed, harmonized annotations for cancer imaging research.

Findings

Annotations include volumetric organ and landmark data.

Radiomics features are derived and made publicly available.

The approach supports FAIR data principles and reproducibility.

Abstract

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 their downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and thus 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 of computed tomography images of the chest, NSCLC-Radiomics, and 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 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 be used to aid in cancer imaging.