Lung cancer risk estimation on thorax CT scans - DSB2017 grt123

About

Creators:

kiranvaidhya

jacobsc

Contact email:

colin.jacobs@radboudumc.nl

Version:

ffdd0ac4-315e-4879-84ce-8cda68e03989

Last updated:

April 13, 2021, 7:17 a.m.

Associated publications:

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).

Liao F, Liang M, Li Z, Hu X, Song S. Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network. IEEE Trans Neural Netw Learning Syst. 2019;30(11):3484-3495.

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 is described in this publication by Fangzhou Liao, Ming Liang, Zhe Li, and Xiaolin Hu and the code is publicly available on Github.

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

Image courtesy of Fangzhou Liao et al. in the previously mentioned paper.

Mechanism

The algorithm is described in this publication by Fangzhou Liao, Ming Liang, Zhe Li, and Xiaolin Hu.

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