Lung cancer risk estimation on thorax CT scans - DSB2017 JulianDaniel

Logo for Lung cancer risk estimation on thorax CT scans - DSB2017 JulianDaniel

About

Editors:
User Mugshot jacobsc  User Mugshot kiranvaidhya 
Contact email:
colin.jacobs@radboudumc.nl
Image Version:
2cad5396-4633-4006-9620-5761b7ab66d7 — Sept. 30, 2020
Associated publication:
Jacobs C, Setio AAA, Scholten ET, et al.. Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists. Radiology: Artificial Intelligence. 2021;3(6).

Summary

This algorithm analyzes non-contrast CT scans of the thorax and predicts the lung cancer risk. The algorithm was developed by Julian de Wit and Daniel Hammack. The algorithm descriptions and code are publicly available: report Daniel Hammack, code Daniel Hammack, report Julian de Wit, code Julian de Wit.

This algorithm was developed as part of the Kaggle Data Science Bowl in 2017 and won the second place in this challenge.

Mechanism

The algorithm was developed by Julian de Wit and Daniel Hammack. The algorithm descriptions and code are publicly available: report Daniel Hammack, code Daniel Hammack, report Julian de Wit, code Julian de Wit.

Interfaces

This algorithm implements all of the following input-output combinations:

Inputs Outputs
1
    Generic Medical Image
    Results JSON File

Validation and Performance

Uses and Directions

This algorithm was developed for research purposes only.

Warnings

Common Error Messages

Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3