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A Deep Learning Model for Improved Cancer Risk Prediction in Lung Screening Low-Dose Chest Computed Tomography

Principal Investigator
Name
Florian Fintelmann
Degrees
MD
Institution
Massachusetts General Hospital
Position Title
Assistant Professor of Radiology, Harvard Medical School
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-564
Initial CDAS Request Approval
Sep 12, 2019
Title
A Deep Learning Model for Improved Cancer Risk Prediction in Lung Screening Low-Dose Chest Computed Tomography
Summary
Together with collaborators at the Massachusetts Institute of Technology we will analyze the low-dose computed tomography (LDCT) examinations of the chest obtained in participants in the National Lung Screening Trial prior to the tissue diagnosis of lung cancer. The LDCT examinations of patients diagnosed with lung cancer will be compared with LDCT examinations of patients who did not develop lung cancer during the trial matched for age, gender and smoking exposure. We postulate that a deep learning algorithm can be trained to estimate the risk for developing a clinically active lung cancer within the next 12 months (1-year risk) and within the next 24 months (2-year risk). Additionally, we will explore the degree to which non-lung, smoking-related cancers may be predicted from LDCT using deep learning.
Aims

- Identify imaging features on low-dose computed tomography examinations of the chest obtained for screening that predict lung cancer
- Predict risk of all smoking-related cancers from LDCT

Collaborators

Lecia V. Sequist, MD (Massachusetts General Hospital)
Regina Barzilay, PhD (Massachusetts Institute of Technology)
Adam Yala, PhD candidate (Massachusetts Institute of Technology)

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