Approved NLST Projects
Here you can browse the complete list of projects for NLST whose requests for data/biospecimens were approved.
| Name | Principal Investigator | Institution | Study | Date Approved | Project ID |
|---|---|---|---|---|---|
| Machine Learning Methods for Nodule Detection and Classification | Xiaohui Xie | University of California, Irvine | NLST | May 11, 2017 | NLST-304 |
| Outcomes associated with significant incidental findings in lung cancer screening | Ilana Gareen | Brown University | NLST | May 10, 2017 | NLST-308 |
| Automatic Detection and Classification System for Lung Lesion | Takashi Shirahata | Hitachi, Ltd. | NLST | Apr 27, 2017 | NLST-302 |
| Using Deep Learning for Cancer Nodule Detection | Ashish Gupta | Auburn University | NLST | Apr 25, 2017 | NLST-300 |
| Clinical and cost effectiveness of lung cancer screening by low-dose CT | Chris Hyde | University of Exeter | NLST | Apr 20, 2017 | NLST-301 |
| Use of neural networks in tissue abnormality detection | Rafal Grzeszczuk | AGH University of Science and Technology | NLST | Apr 11, 2017 | NLST-299 |
| Application of Deep Learning to Combine Clinical and Imaging Data to Localize, Characterize, and Prognosticate Lung Cancer Patients | Jae Ho Sohn | UCSF | NLST | Apr 4, 2017 | NLST-297 |
| Determining the prevalence and misclassification of perifissural nodules in the NLST. | Anton Schreuder | Radboudumc | NLST | Mar 22, 2017 | NLST-296 |
| Detection and Diagnosis of Lung Cancer with Deep Learning | Shan Li | Zephex Technology | NLST | Mar 21, 2017 | NLST-295 |
| Automated Lung Nodule Detection using Deep Neural Networks | Kun-Hsing Yu | President and Fellows of Harvard College | NLST | Feb 22, 2017 | NLST-286 |
| Building a Common Data Model for Cancer Research | Guoqian Jiang | Mayo Clinic | NLST | Feb 17, 2017 | NLST-285 |
| Pulmonary Nodule Detection and Classification using large scale CT data | feifei zhou | Independent Researcher, not applicable | NLST | Feb 13, 2017 | NLST-283 |
| Radiomics using Deep Learning with High Performance Computing | Eduardo Ulises Moya Sánchez | Barcelona Supercomputing Center | NLST | Feb 9, 2017 | NLST-275 |
| Improvement of Early Lung Cancer Detection Rate for Patients Undergoing 18F-FDG PET/CT scans | Tsung-Ying Ho | Chang Gung Memorial Hospital | NLST | Feb 9, 2017 | NLST-284 |
| Surgical approach and outcomes in NLST patients with positive screening CT | Brendon Stiles | Weill Cornell Medicine | NLST | Feb 7, 2017 | NLST-281 |
| Medical applications of example-based super-resolution | Ramin Zabih | Joan & Sanford I. Weill Medical College of Cornell University | NLST | Jan 31, 2017 | NLST-279 |
| Deep learning of lung cancer images for segmentation and outcome prediction | Olivier Gevaert | Stanford University | NLST | Jan 31, 2017 | NLST-276 |
| Lung lesion screening and tracking | Carla Leibowitz | Arterys | NLST | Jan 30, 2017 | NLST-278 |
| Lung Cancer Detection using Data Analytics and Machine Learning | Apoorva Mahale | Vivekanand Education Society's Institute of Technology | NLST | Jan 30, 2017 | NLST-277 |
| Lung cancer prediction by deep learning approach | Xiaokang Wang | University of California,Davis | NLST | Jan 26, 2017 | NLST-273 |