Lung cancer image analysis

Lung cancer is the leading cause of cancer-related death worldwide, for which the five-year survival rates have yet to surpass 20%. The World Health Organization (WHO) has estimated that there were 2.21 million cases of lung cancer and 1.80 million deaths due to lung cancer in 2020. Tobacco smoking remains the main risk factor for lung cancer. Imaging is crucial for early detection, diagnosis, treatment planning and monitoring of lung cancer. It plays an important role in the multidisciplinary management of lung cancer patients.

In this research line, we aim to develop, validate and deploy algorithms that assist in the interpretation of radiological imaging for lung cancer. This research line is led by Colin Jacobs.

Lung AI group

Click on the cards below to learn about the various projects in lung cancer image analysis.

Projects

NELSON-POP: Personalized outcome prediction in lung cancer screening

Accurate estimation of the probability of lung cancer of screen-detected pulmonary nodules using artificial intelligence

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SOLACE: Strengthening the screening Of Lung cAnCer in Europe

Strengthening the screening of lung cancer in Europe

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MERAI Lab: MeVis and Radboudumc ICAI Lab

MERAI Lab is a collaboration between Radboudumc and MeVis Medical Solutions AG with the aim to create AI solutions in the lung oncology field.

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AMARA: Accurate malignancy risk estimation of pulmonary nodules using AI

Accurate malignancy risk estimation of pulmonary nodules using artificial intelligence

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IMAGIO: Imaging and Advanced Guidance for Workflow Optimization in Interventional Oncology

IMAGIO will leverage Interventional Oncology in the clinical setting to improve the cancer survival outcomes, through minimally invasive, efficient, and affordable care pathways for three disease states: liver cancer, lung cancer and sarcoma.

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Algorithms

Several algorithms that this group has developed can be tried on the grand-challenge.org platform:

Lung nodule detection for routine clinical CT scans

Deep learning for the detection of pulmonary nodules in chest CT scans

Go to Grand Challenge →
Pulmonary Nodule Malignancy Prediction

Deep Learning for Malignancy Risk Estimation of Low-Dose Screening CT Detected Pulmonary Nodules

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Deep learning to estimate pulmonary nodule malignancy risk using a current and a prior CT image

Deep learning to estimate pulmonary nodule malignancy risk using a prior CT image

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Pulmonary Lobe Segmentation

Automatic segmentation of pulmonary lobes on CT scans for patients with COPD or COVID-19.

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People

Colin Jacobs

Colin Jacobs

Associate Professor

Cornelia Schaefer-Prokop

Cornelia Schaefer-Prokop

Senior Researcher

Ernst Scholten

Ernst Scholten

Senior Researcher

Noa Antonissen

Noa Antonissen

PhD Candidate

Dré Peeters

Dré Peeters

PhD Candidate

Renate Dinnessen

Renate Dinnessen

PhD Candidate

Lars Leijten

Lars Leijten

PhD Candidate

Bogdan Obreja

Bogdan Obreja

PhD Candidate

Fennie van der Graaf

Fennie van der Graaf

PhD Candidate

Michel Vitale

Michel Vitale

PhD Candidate

Lisa Klok

Lisa Klok

PhD Candidate

Alonso Cerrato Nieto

Alonso Cerrato Nieto

PhD Candidate

Shaurya Gaur

Shaurya Gaur

Master Student

Kristina Avramova

Kristina Avramova

Bachelor Student

Sarah van Rooden

Sarah van Rooden

Student assistant

Publications

  • W. Hendrix, N. Hendrix, E. Scholten, M. Mourits, J. Trap-de Jong, S. Schalekamp, M. Korst, M. van Leuken, B. van Ginneken, M. Prokop, M. Rutten and C. Jacobs, "Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans", Communications Medicine, 2023;3(1):156.
  • 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.
  • C. Jacobs, "Challenges and outlook in the management of pulmonary nodules detected on CT", European Radiology, 2023;34:247-249.
  • W. Hendrix, M. Rutten, N. Hendrix, B. van Ginneken, C. Schaefer-Prokop, E. Scholten, M. Prokop and C. Jacobs, "Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals", European Radiology, 2023;33:8279-8288.
  • N. Antonissen, K. Venkadesh, H. Gietema, R. Vliegenthart, Z. Saghir, E. Scholten, M. Prokop, C. Schaefer-Prokop and C. Jacobs, "Retrospective validation of nodule management based on deep learning-based malignancy thresholds in lung cancer screening", European Congress of Radiology, 2023.
  • D. Peeters, N. Alves, K. Venkadesh, R. Dinnessen, Z. Saghir, E. Scholten, H. Huisman, C. Schaefer-Prokop, R. Vliegenthart, M. Prokop and C. Jacobs, "The effect of applying an uncertainty estimation method on the performance of a deep learning model for nodule malignancy risk estimation", European Congress of Radiology, 2023.
  • G. Sidorenkov, R. Stadhouders, C. Jacobs, F. Mohamed Hoesein, H. Gietema, K. Nackaerts, Z. Saghir, M. Heuvelmans, H. Donker, J. Aerts, R. Vermeulen, A. Uitterlinden, V. Lenters, J. van Rooij, C. Schaefer-Prokop, H. Groen, P. de Jong, R. Cornelissen, M. Prokop, G. de Bock and R. Vliegenthart, "Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial.", European journal of epidemiology, 2023;38(4):445-454.
  • W. Hendrix, M. Rutten, N. Hendrix, B. van Ginneken, C. Schaefer-Prokop, E. Scholten, M. Prokop and C. Jacobs, "Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals", European Radiology, 2023;33:8279-8288.
  • 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.
  • W. Hendrix, N. Hendrix, M. Prokop, E. Scholten, B. Van Ginneken, M. Rutten and C. Jacobs, "Trends in the Incidence of Pulmonary Nodules in Chest Computed Tomography: 10-Year Results from Two Dutch Hospitals", European Congress of Radiology, 2021.
  • C. Jacobs, A. Setio, E. Scholten, P. Gerke, H. Bhattacharya, F. M. Hoesein, M. Brink, E. Ranschaert, P. de Jong, M. Silva, B. Geurts, K. Chung, S. Schalekamp, J. Meersschaert, A. Devaraj, P. Pinsky, S. Lam, B. van Ginneken and K. Farahani, "Deep Learning for Lung Cancer Detection in Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists", Radiology: Artificial Intelligence, 2021;3(6):e210027.
  • C. Jacobs, A. Schreuder, S. van Riel, 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, "Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement", Radiology: Imaging Cancer, 2021;3(5):e200160.
  • A. Schreuder and C. Schaefer-Prokop, "Beyond the AJR: "Association of the Intensity of Diagnostic Evaluation With Outcomes in Incidentally Detected Lung Nodules"", American Journal of Roentgenology, 2021;217:1011-1011.
  • A. Schreuder, O. Mets, C. Schaefer-Prokop, C. Jacobs and M. Prokop, "Microsimulation modeling of extended annual CT screening among lung cancer cases in the National Lung Screening Trial", Lung Cancer, 2021;156:5-11.
  • A. Schreuder, C. Jacobs, N. Lessmann, M. Broeders, M. Silva, I. Isgum, P. de Jong, N. Sverzellati, M. Prokop, U. Pastorino, C. Schaefer-Prokop and B. van Ginneken, "Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening", European Respiratory Journal, 2021;58(3):2003386.
  • A. Schreuder, E. Scholten, B. van Ginneken and C. Jacobs, "Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?", Translational Lung Cancer Research, 2021;10(5):2378-2388.
  • 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.
  • M. Silva, G. Milanese, S. Sestini, F. Sabia, C. Jacobs, B. van Ginneken, M. Prokop, C. Schaefer-Prokop, A. Marchiano, N. Sverzellati and U. Pastorino, "Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval", European Radiology, 2020;31(4):1956-1968.
  • 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;30:3277-3294.
  • 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.
  • 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.
  • 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.
  • 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", Scientific Reports, 2019;9(1):11591.
  • 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.
  • 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", Scientific Reports, 2018;8(1):646.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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", Scientific Reports, 2017(46479).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.