Validating automated algorithms for detection of lung pathology
Principal Investigator
Name
Atilla Kiraly
Degrees
Ph.D.
Institution
Google
Position Title
Staff Software Engineer
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-559
Initial CDAS Request Approval
Nov 13, 2019
Title
Validating automated algorithms for detection of lung pathology
Summary
Since 2013, the USPSTF recommends annual screening for lung cancer with low-dose computed tomography (LDCT) in adults aged 55 to 80 years who have a 30 pack-year smoking history and currently smoke or have quit within the past 15 years. However, access to health care and affordability issues still appear to be barriers to screening for lung cancer (Delmerico et al, 2014). Automating detection has the potential of increasing efficiency and reducing costs. Rapid advances in computer vision and large scale machine learning have made it possible to train computer algorithms to identify high-level concepts at an accuracy exceeding that of humans (Ioffe et al, 2015; Szegedy et al, 2015).
In our previous project (NLST-204) we developed an algorithm that could detect lung cancer with state-of-the-art performance, outperforming radiologists in a reader study (Ardila et al, 2019). We’d like to validate this model on additional NLST images, not included in the initial project (NLST-204) to see if performance is matched on data which was not made available in the original project.
In our previous project (NLST-204) we developed an algorithm that could detect lung cancer with state-of-the-art performance, outperforming radiologists in a reader study (Ardila et al, 2019). We’d like to validate this model on additional NLST images, not included in the initial project (NLST-204) to see if performance is matched on data which was not made available in the original project.
Aims
Specific Aim 1: Analyse additional images, not included as part of original research project with existing lung cancer detection algorithm and determine the accuracy of algorithm in detecting malignant cases
Specific Aim 2: Investigate how the algorithm may be used in conjunction with human readers to provide an assisted read
Specific Aim 3: Use deep learning techniques to augment the performance of the existing lung screening algorithm with the additional data
Specific Aim 4: Model additional targets, such as mortality outcomes or nodule growth
Collaborators
Daniel Tse, Google
Diego Ardila, Google
Atilla Kiraly, Google
Wenxing Ye, Google
Shravya Shetty, Google
Jie Yang, Google