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 2.09 million cases of lung cancer and 1.76 million deaths due to lung cancer in 2018. 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. Click on the cards below to learn about the various projects in lung cancer image analysis.

Projects

Artificial intelligence for lung cancer screening

We aim to improve the efficiency of lung cancer screening by using artificial intelligence.

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AI-driven incidental lung nodule analysis

AI solutions to optimize the detection and characterization of incidentally detected lung nodules.

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Optimal treatment prediction for early stage lung cancer

We aim to combine advanced CT and FDG-PET image analysis with computational pathology to predict the most optimal treatment for each individual patient.

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People

Colin Jacobs

Colin Jacobs

Assistant Professor

Ward Hendrix

Ward Hendrix

PhD Candidate

Ernst Scholten

Ernst Scholten

Radiologist

Anton Schreuder

Anton Schreuder

PhD Candidate

Marco Marra

Marco Marra

Research Software Engineer

DIAG Research Software Engineering

Sil van de Leemput

Sil van de Leemput

Research Software Engineer

RTC Deep Learning