Pulmonary Nodule Malignancy Prediction

This is a deep learning (DL) algorithm based on convolutional neural networks (CNNs) that takes in a low-dose chest CT and the coordinates of a pulmonary nodule as input to predict the malignancy risk of the nodule. This algorithm was developed with pulmonary nodules from the National Lung Screening Trial (NLST) and validated externally with pulmonary nodules from the Danish Lung Cancer Screening Trial (DLCST).

The study describing the development and validation of this algorithm has been published in Radiology.

Intended use

For research use only. The algorithm is intended to be used only on pulmonary nodules detected at low-dose chest CT examinations of high-risk patients eligible for lung cancer screening programs.

📝 Model Facts Label

The following table describes the Model Facts label for clinical end-users and researchers interested in exploring the utility of this algorithm.

📧 Contact

For inquiries and additional information, please contact Kiran Vaidhya Venkadesh and Colin Jacobs.

📖 Citation

If you use this algorithm in your work, please cite our work:

Venkadesh KV, Setio AAA, Schreuder A, Scholten ET, Chung K, W Wille MM, Saghir Z, van Ginneken B, Prokop M, Jacobs C. Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT. Radiology. 2021 May 18:204433. doi: 10.1148/radiol.2021204433. Epub ahead of print. PMID: 34003056.