Artificial intelligence for lung cancer screening

Background

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.

Aim

In this project, we aim to develop algorithms that will improve the accuracy and cost-effectiveness of lung cancer screening. Much of our work is about extending and improving our current nodule CAD algorithms and automating lung cancer screening. Next to that, we alos have a strong focus on performing clinical research in this area. To help us achieve our goals, we have developed a high-throughput workstation for lung cancer screening (see more info below), which incorporates many of the algorithms we develop. This project is done in close collaboration with MeVis Medical Solutions AG. This project has led to the release of Veolity, an optimized workstation solution for lung cancer screening.

Funding

This project has received and continues to receive funding from several sources: Dutch Research Council (NWO), Radboudumc and MeVis Medical Solutions AG.

People

Colin Jacobs

Colin Jacobs

Assistant Professor

Diagnostic Image Analysis Group

Kiran Vaidhya Venkadesh

Kiran Vaidhya Venkadesh

PhD Candidate

Diagnostic Image Analysis Group

Anton Schreuder

Anton Schreuder

PhD Candidate

Diagnostic Image Analysis Group

Sil van de Leemput

Sil van de Leemput

Research Software Engineer

Diagnostic Image Analysis Group

Ernst Scholten

Ernst Scholten

Radiologist

Diagnostic Image Analysis Group

Bram van Ginneken

Bram van Ginneken

Professor

Diagnostic Image Analysis Group

Cornelia Schaefer-Prokop

Cornelia Schaefer-Prokop

Radiologist

Diagnostic Image Analysis Group

Mathias Prokop

Mathias Prokop

Professor

Radboudumc

Publications

  • H. Kauczor, A. Baird, T. Blum, L. Bonomo, C. Bostantzoglou, O. Burghuber, B. Čepicka, A. Comanescu, S. Courad, A. Devaraj, V. Jespersen, S. Morozov, I. Agmon, N. Peled, P. Powell, H. Prosch, S. Ravara, J. Rawlinson, M. Revel, M. Silca, A. Snoeckx, B. van Ginneken, J. van Meerbeeck, C. Vardavas, O. von Stackelberg, M. Gaga, O. behalf of the of (ESR) and T. (ERS), "ESR/ERS statement paper on lung cancer screening", European Radiology, 2020. Abstract/PDF DOI PMID
  • C. Jacobs and B. van Ginneken, "Google's lung cancer AI: a promising tool that needs further validation", Nature Reviews Clinical Oncology, 2019;16(9):532-533. Abstract/PDF DOI PMID Cited by ~6
  • S. van Riel, C. Jacobs, E. Scholten, R. Wittenberg, M. Winkler Wille, B. de Hoop, R. Sprengers, O. Mets, B. Geurts, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management", European Radiology, 2019;29(2):924-931. Abstract/PDF DOI PMID Cited by ~9
  • A. Schreuder, C. Jacobs, L. Gallardo-Estrella, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Predicting all-cause and lung cancer mortality using emphysema score progression rate between baseline and follow-up chest CT images: A comparison of risk model performances", PLoS One, 2019;14(2):e0212756. Abstract/PDF DOI PMID Cited by ~1
  • G. Aresta, C. Jacobs, T. Araujo, A. Cunha, I. Ramos, B. van Ginneken and A. Campilho, "iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network", Nature Scientific Reports, 2019;9(1). Abstract/PDF DOI PMID Cited by ~8
  • M. Tammemagi, A. Ritchie, S. Atkar-Khattra, B. Dougherty, C. Sanghera, J. Mayo, R. Yuan, D. Manos, A. McWilliams, H. Schmidt, M. Gingras, S. Pasian, L. Stewart, S. Tsai, J. M.Seely, P. Burrowes, R. Bhatia, E. A.Haider, C. Boylan, C. Jacobs, B. van Ginneken, M. Tsao, S. Lam and the Pan-Canadian Early Detection of Lung Cancer Study Group, "Predicting Malignancy Risk of Screen Detected Lung Nodules - Mean Diameter or Volume", Journal of Thoracic Oncology, 2019;14(2):203-211. Abstract/PDF DOI PMID Cited by ~8
  • J. Charbonnier, K. Chung, E. Scholten, E. van Rikxoort, C. Jacobs, N. Sverzellati, M. Silva, U. Pastorino, B. van Ginneken and F. Ciompi, "Automatic segmentation of the solid core and enclosed vessels in subsolid pulmonary nodules", Nature Scientific Reports, 2018;8(1):646. Abstract/PDF DOI PMID Cited by ~9
  • K. Chung, F. Ciompi, J. Scholten E. Th. Goo, M. Prokop, C. Jacobs, B. van Ginneken and C. Schaefer-Prokop, "Visual Discrimination of Screen-detected Persistent from Transient Subsolid Nodules: an Observer Study", PLoS One, 2018;13(2):e0191874. Abstract/PDF DOI PMID Cited by ~4
  • A. Schreuder, C. Schaefer-Prokop, E. Scholten, C. Jacobs, M. Prokop and B. van Ginneken, "Lung cancer risk to personalise annual and biennial follow-up computed tomography screening", Thorax, 2018;73(7):626-633. Abstract/PDF DOI PMID Cited by ~13
  • A. Schreuder, B. van Ginneken, E. Scholten, C. Jacobs, M. Prokop, N. Sverzellati, S. Desai, A. Devaraj and C. Schaefer-Prokop, "Classification of CT Pulmonary Opacities as Perifissural Nodules: Reader Variability", Radiology, 2018;288(3):867-875. Abstract/PDF DOI PMID Cited by ~14
  • M. Silva, M. Prokop, C. Jacobs, G. Capretti, N. Sverzellati, F. Ciompi, B. van Ginneken, C. Schaefer-Prokop, C. Galeone, A. Marchiano and U. Pastorino, "Long-term Active Surveillance of Screening Detected Subsolid Nodules is a Safe Strategy to Reduce Overtreatment", Journal of Thoracic Oncology, 2018;13:1454-1463. Abstract/PDF DOI PMID Cited by ~15
  • M. Silva, C. Schaefer-Prokop, C. Jacobs, G. Capretti, F. Ciompi, B. van Ginneken, U. Pastorino and N. Sverzellati, "Detection of Subsolid Nodules in Lung Cancer Screening: Complementary Sensitivity of Visual Reading and Computer-Aided Diagnosis", Investigative Radiology, 2018;53(8):441-449. Abstract/PDF DOI PMID Cited by ~13
  • K. Chung, C. Jacobs, E. Scholten, J. Goo, H. Prosch, N. Sverzellati, F. Ciompi, O. Mets, P. Gerke, M. Prokop, B. van Ginneken and C. Schaefer-Prokop, "Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?", Radiology, 2017;284(1):264-271. Abstract/PDF DOI PMID Cited by ~32
  • K. Chung, C. Jacobs, E. Scholten, O. Mets, I. Dekker, M. Prokop, B. van Ginneken and C. Schaefer-Prokop, "Malignancy estimation of Lung-RADS criteria for subsolid nodules on CT: accuracy of low and high risk spectrum when using NLST nodules", European Radiology, 2017;27:4672-4679. Abstract/PDF DOI PMID Cited by ~8
  • F. Ciompi, K. Chung, S. van Riel, A. Setio, P. Gerke, C. Jacobs, E. Scholten, C. Schaefer-Prokop, M. Wille, A. Marchiano, U. Pastorino, M. Prokop and B. van Ginneken, "Towards automatic pulmonary nodule management in lung cancer screening with deep learning", Nature Scientific Reports, 2017(46479). Abstract DOI PMID Cited by ~171
  • S. van Riel, F. Ciompi, C. Jacobs, M. Winkler Wille, E. Scholten, M. Naqibullah, S. Lam, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines", European Radiology, 2017;27(10):4019-4029. Abstract/PDF DOI PMID Cited by ~28
  • A. Setio, A. Traverso, T. de Bel, M. Berens, C. Bogaard, P. Cerello, H. Chen, Q. Dou, M. Fantacci, B. Geurts, R. Gugten, P. Heng, B. Jansen, M. de Kaste, V. Kotov, J. Lin, J. Manders, A. Sonora-Mengana, J. Garcia-Naranjo, E. Papavasileiou, M. Prokop, M. Saletta, C. Schaefer-Prokop, E. Scholten, L. Scholten, M. Snoeren, E. Torres, J. Vandemeulebroucke, N. Walasek, G. Zuidhof, B. Ginneken and C. Jacobs, "Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge", Medical Image Analysis, 2017;42:1-13. Abstract/PDF DOI PMID Cited by ~291
  • C. Jacobs, E. van Rikxoort, K. Murphy, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database", European Radiology, 2016;26:2139-2147. Abstract/PDF DOI PMID Cited by ~54
  • A. Ritchie, C. Sanghera, C. Jacobs, W. Zhang, J. Mayo, H. Schmidt, M. Gingras, S. Pasian, L. Stewart, S. Tsai, D. Manos, J. Seely, P. Burrowes, R. Bhatia, S. Atkar-Khattra, B. van Ginneken, M. Tammemagi, M. Tsao, S. Lam and the Pan-Canadian Early Detection of Lung Cancer Study Group, "Computer Vision Tool and Technician as First Reader of Lung Cancer Screening CT Scans", Journal of Thoracic Oncology, 2016;11(5):709-717. Abstract/PDF DOI PMID Cited by ~14
  • C. Jacobs, E. van Rikxoort, E. Scholten, P. de Jong, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Solid, Part-Solid, or Non-solid?: Classification of Pulmonary Nodules in Low-Dose Chest Computed Tomography by a Computer-Aided Diagnosis System", Investigative Radiology, 2015;50(3):168-173. Abstract/PDF DOI PMID Cited by ~40
  • B. Lassen, C. Jacobs, J. Kuhnigk, B. van Ginneken and E. van Rikxoort, "Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans", Physics in Medicine and Biology, 2015;60(3):1307-1323. Abstract/PDF DOI PMID Cited by ~51
  • S. van Riel, C. Sánchez, A. Bankier, D. Naidich, J. Verschakelen, E. Scholten, P. de Jong, C. Jacobs, E. van Rikxoort, L. Peters-Bax, M. Snoeren, M. Prokop, B. van Ginneken and C. Schaefer-Prokop, "Observer Variability for Classification of Pulmonary Nodules on Low-Dose CT Images and Its Effect on Nodule Management", Radiology, 2015;277(3):863-871. Abstract/PDF DOI PMID Cited by ~113
  • C. Jacobs, E. van Rikxoort, T. Twellmann, E. Scholten, P. de Jong, J. Kuhnigk, M. Oudkerk, H. de Koning, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Automatic Detection of Subsolid Pulmonary Nodules in Thoracic Computed Tomography Images", Medical Image Analysis, 2014;18:374-384. Abstract/PDF DOI PMID Cited by ~164