Artificial intelligence for lung cancer screening


To be able to detect lung cancer in an early stage, screening of high-risk subjects using low-dose CT has been proposed. In 2011, the National Lung Screening Trial (NLST) was the first multicenter randomized controlled trial (RCT) to demonstrate that three rounds of annual screening of a high-risk population using low-dose chest computed tomography (CT) lead to 20% fewer lung cancer deaths after seven years of follow-up, compared to annual screening with chest radiography. Over 53,000 participants were included in this landmark study. The Dutch-Belgian NELSON trial – the second largest RCT with 15,789 participants – recently published their results and showed a 24% mortality reduction in a high-risk population of men compared to no screening. Based on the results of these trials, several countries have started the implementation of lung cancer screening, and other countries are conducting pilot trials.

AI holds great potential to assist in many of the detection and characterization tasks that have to be performed by a radiologist, and may be able to play an important role in reducing costs and improving the efficiency of screening.


In this project, we developed algorithms that aimed to improve the accuracy and cost-effectiveness of lung cancer screening.


The results of this project are described in the PhD thesis of Kiran Vaidhya Venkadesh


Kiran Vaidhya Venkadesh

Kiran Vaidhya Venkadesh

Postdoctoral Researcher

Colin Jacobs

Colin Jacobs

Assistant Professor

Bram van Ginneken

Bram van Ginneken

Professor, Scientific Co-Director


  • K. Venkadesh, T. Aleef, E. Scholten, Z. Saghir, M. Silva, N. Sverzellati, U. Pastorino, B. van Ginneken, M. Prokop and C. Jacobs, "Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules", Radiology, 2023;308(2):e223308.
  • K. Venkadesh, T. Aleef, A. Schreuder, E. Scholten, B. van Ginneken, M. Prokop and C. Jacobs, "Deep learning for estimating pulmonary nodule malignancy risk using prior CT examinations in lung cancer screening", European Congress of Radiology, 2022.
  • K. Venkadesh, A. Schreuder, E. Scholten, S. Atkar-Khattra, J. Mayo, Z. Saghir, M. Wille, B. van Ginneken, S. Lam, M. Prokop and C. Jacobs, "Integration Of A Deep Learning Algorithm Into The Clinically Established PanCan Model For Malignancy Risk Estimation Of Screen-detected Pulmonary Nodules In First Screening CT", Annual Meeting of the Radiological Society of North America, 2021.
  • K. Venkadesh, A. Setio, A. Schreuder, E. Scholten, K. Chung, M. W Wille, Z. Saghir, B. van Ginneken, M. Prokop and C. Jacobs, "Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.", Radiology, 2021;300(2):438-447.
  • K. Venkadesh, A. Setio, Z. Saghir, B. van Ginneken and C. Jacobs, "Deep Learning for Lung Nodule Malignancy Prediction: Comparison With Clinicians and the Brock Model on an Independent Dataset From a Large Lung Screening Trial", Annual Meeting of the Radiological Society of North America, 2020.