AMARA: Accurate MAlignancy Risk estimation of incidentally and screen-detected pulmonary nodules using Artificial intelligence

Problem description:

The Dutch-Belgian NELSON trial recently showed that CT screening leads to a 24% lung cancer mortality reduction in male long-term smokers. This has sparked discussions on how to implement lung cancer screening in the Netherlands. Screening using low-dose CT is highly sensitive for pulmonary nodule detection. The vast majority of these nodules are benign, but may result in extra investigations. Pulmonary nodules are also an increasingly common finding in routine medical care, with an incidence that is much greater than recognized previously. Thus, there is a critical need for improved nodule stratification and management.

Project summary

Deep learning, a methodology where computers can learn high dimensional features from large amounts of data, has led to a revolution in the field of artificial intelligence (AI) because its use resulted in major improvements in the performance of AI systems. Radboudumc has developed a deep learning-based algorithm for nodule malignancy prediction and results have shown that the algorithm performs comparable to thoracic radiologists for risk estimation of screen-detected nodules on low-dose chest CT.

To make the deep learning algorithm also applicable in routine clinical practice, the training data for the algorithm needs to be extended with CT data from routine clinical care. Therefore, an available large retrospective dataset of routine CT scan at Radboudumc will be curated and annotated. In addition, a more extensive external validation of the algorithm is needed. Therefore, we will perform an external validation of the algorithm on data from three European lung cancer screening trials: the Dutch-Belgian NELSON screening trial, the Danish Lung Cancer Screening Trial (DLCST), and the Multicentric Italian Lung Detection (MILD) trial, and on a large multi-center retrospective dataset collected at the five participating Dutch institutes.

We hypothesize that the final AI algorithm for nodule malignancy prediction and its integration in nodule management protocols will 1) accelerate the time to diagnosis of malignant nodules, and 2) limit unnecessary nodule work-up

Funding

This research project is funded by the Dutch Cancer Society. The project is led by Colin Jacobs and is a collaboration with investigators from Radboudumc, University Medical Center Groningen, Erasmus MC, UMC Utrecht and MUMC+.

People

Renate Dinnessen

Renate Dinnessen

PhD Candidate

Dré Peeters

Dré Peeters

PhD Candidate

Cornelia Schaefer-Prokop

Cornelia Schaefer-Prokop

Senior Researcher

Erik van der Heijden

Erik van der Heijden

Pulmonologist

Radboudumc

Firdaus Mohamed Hoesein

Firdaus Mohamed Hoesein

Radiologist

UMC Utrecht

Pim de Jong

Pim de Jong

Radiologist

UMC Utrecht

Rozemarijn Vliegenthart

Rozemarijn Vliegenthart

Radiologist

University Medical Center Groningen

Hester Gietema

Hester Gietema

Radiologist

Maastricht UMC+

Robin Cornelissen

Robin Cornelissen

Pulmonologist

Erasmus Medical Center

Colin Jacobs

Colin Jacobs

Assistant Professor

Publications

  • D. Peeters, N. Alves, K. Venkadesh, R. Dinnessen, Z. Saghir, E. Scholten, C. Schaefer-Prokop, R. Vliegenthart, M. Prokop and C. Jacobs, "Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation", European Radiology, 2024.
  • R. Dinnessen, K. Venkadesh, D. Peeters, H. Gietema, E. Scholten, C. Schaefer-Prokop and C. Jacobs, "External validation of an AI algorithm for pulmonary nodule malignancy risk estimation on a dataset of incidentally detected pulmonary nodules", European Congress of Radiology, 2024.
  • 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.