Timely and relevant thoracic oncology news brought to you by the only global association dedicated to the multidisciplinary study of lung cancer.

Timely and relevant thoracic oncology news brought to you by the only global association dedicated to the multidisciplinary study of lung cancer.

Self-Learning AI as a Diagnostic Tool in Malignant Mesothelioma

Prof. John Le Quesne will show how AI can be used to subtype mesothelioma into risk categories and prognosis.

By

Erin Jungmeyer

Estimated Read Time:

1 minute

Meeting News, WCLC News

Presenter Profile: John Le Quesne, MA, PhD, MBBS, FRCPath

Associate Group Leader, CRUK Scotland Research Institute; Consultant Histopathologist (Hon.), National Heath Service; and Mazumdar-Shaw Chair in Molecular Pathology, University of Glasgow

Glasgow, UK

ILCN: What is your presentation about?

The application of a highly novel type of self-learning artificial intelligence to pathology images for the diagnosis and prognosis of malignant mesothelioma. We show that our algorithm is best-in-class in subtyping mesothelioma into risk categories and prognosis, and reveal the recurrent underlying morphological makeup of the disease.

ILCN: Why is this topic timely or important in 2024?

AI applied to digital pathology is set to change and improve diagnostics and patient stratification in major ways. Many approaches are being tried, but this one has several special unique properties.

ILCN: How did you become involved with this area of lung cancer research, care, or advocacy?

I have been working in lung diagnostics for over a decade, and applying AI methods for over 7 years.

ILCN: What are you most looking forward to during the 50th Anniversary World Conference on Lung Cancer?

Hearing the exciting new science, and meeting with friends and colleagues.


About the Authors

Erin Jungmeyer

Erin Jungmeyer

Managing Editor, ILCN