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ZENODO
Dataset . 2025
License: CC BY NC
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY NC
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC
Data sources: Datacite
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The LUNA25 Challenge: Public Training and Development set - Annotation Data

Authors: Peeters, Dré; Obreja, Bogdan; Antonissen, Noa; Jacobs, Colin;

The LUNA25 Challenge: Public Training and Development set - Annotation Data

Abstract

This dataset represents the annotation part of LUNA25: Public Training and Development dataset. In total, it contains a csv with the annotations of 555 malignant nodules and 5608 benign nodules, acquired in 2120 patients and 4069 low-dose chest CT scans. The dataset was acquired in participants who enrolled in the National Lung Cancer Screening Trial (NLST) between 2002 and 2004 in one of the 33 centers in the United States. The LUNA25 challenge is new grand challenge designed to evaluate the diagnostic performance of AI algorithms and radiologists in lung nodule malignancy risk estimation in screening CT. LUNA25 aims to establish: 1) state-of-the-art AI performance for lung nodule malignancy risk estimation, 2) performance of radiologists at lung nodule malignancy risk estimation through a large scale international reader study, 3) a comparison between performance of AI algorithms and radiologists with a variety of experience levels. This study hypothesizes that state-of-the-art AI is non-inferior to radiologists at lung nodule malignancy risk estimation at screening CT. 

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Keywords

Computed Tomography, Artificial Intelligence, Lung Cancer, Radiologists, Computer-Aided Diagnosis

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Related to Research communities
Cancer Research