Nodule identification and classification in Lung screening
Principal Investigator
Name
Estanislao OUBEL
Degrees
Ph.D.
Institution
Median Technologies
Position Title
Head Science & Image Processing
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-362
Initial CDAS Request Approval
Oct 18, 2017
Title
Nodule identification and classification in Lung screening
Summary
Finding Lung cancer disease at an early and treatable stage remains the most formidable challenge. Computer-assisted early pulmonary nodule detection by imaging offers an opportunity to catch the tumor before it grows and spreads. In the hand of radiologists, these deep-learning algorithms could significantly reduce the false positive rate that currently afflicts early screening and improve cancer prediction efficiency. Even with the growing interest for CAD in the context of screening and diagnostic, no satisfactory system is available in clinical routine, probably because their performance and robustness are expected to be much higher due to liability concerns for delaying a biopsy recommendation.
Our extensive experience in developing lesion management solution aligns very well with such healthcare need. Therefore, we are planning in developing systems that would work 1- to improve accuracy in nodule detection and 2- to classify the detected nodules according to their likelihood to be malignant. We aim to use the unique NLST dataset to train and develop these computer systems. The CT dataset from NLST with its structured annotated demographics and outcomes will also be an important database to use that has the potential to aid our understanding in disease progression.
Aims
- Develop deep-learning learning algorithms, trained on NLST data, to improve pulmonary nodule detection
- Reduce false positive candidates while maintaining high sensitivity
- Assessment of malignancy for the detected nodules
Collaborators
Michael AUFFRET, Ph. D. Median Technologies
Yuta NAKANO, Ph. D. Median Technologies
Corinne Ramos, Ph.D. Median Technologies