Lung cancer risk estimation on thorax CT scans - DSB2017 JulianDaniel

About

Creators:

kiranvaidhya

jacobsc

Contact email:

colin.jacobs@radboudumc.nl

Version:

2cad5396-4633-4006-9620-5761b7ab66d7

Last updated:

Sept. 30, 2020, 5:41 p.m.

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).

Inputs:

Generic Medical Image

Outputs:

Results JSON File (A collection of results of unknown type. Legacy, if possible please use alternative interfaces.)

Model Facts

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.

Validation and Performance

Left empty by the Algorithm Editors

Uses and Directions

This algorithm was developed for research purposes only.

Warnings

Left empty by the Algorithm Editors

Common Error Messages

Left empty by the Algorithm Editors

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