Here you can browse the complete list of projects for NLST whose requests for data/biospecimens were approved.
Filter results of approved projects table below
| Name | Principal Investigator | Institution | Study | Date Approved | Project ID |
|---|---|---|---|---|---|
| Nodule identification and classification in Lung screening | Estanislao OUBEL | Median Technologies | NLST | Oct 18, 2017 | NLST-362 |
| Assessing Racial Disparities of Lung Cancer Risk Prediction Model | Hee-Soon Juon | Thomas Jefferson University | NLST | Oct 16, 2017 | NLST-361 |
| Racial disparities in outcomes in patients undergoing screening and treatment for Lung Cancer | Gurudatta Naik | University of Alabama at Birmingham | NLST | Oct 13, 2017 | NLST-360 |
| A simulation modeling study examining the downstream effects of follow-up schedules for screen-detected nodules | Chung Yin (Joey) Kong | Massachusetts General Hospital / Harvard Medical School | NLST | Sep 28, 2017 | NLST-357 |
| Deep Learning for Lung Cancer Detection | Ryan Sherman | Deep Analytics | NLST | Sep 27, 2017 | NLST-341 |
| Detection and prediction analysis of lung cancer based on deep learning with low-dose CT | Seyoun Park | Johns Hopkins University | NLST | Sep 26, 2017 | NLST-356 |
| Computer-Aided Treatment Effectiveness Assessment Based on Lung Cancer CT Screening | Hsiao-Dong Chiang | Cornell University | NLST | Sep 26, 2017 | NLST-346 |
| Automatic Detection of Cancerous Lung Tissue | Maribeth Cogan | The University of Texas at Dallas | NLST | Sep 22, 2017 | NLST-355 |
| Nodule Identification for Lung Screening | Osama Masoud | Vital Images | NLST | Sep 22, 2017 | NLST-353 |
| LDCT Pulmonary Nodule Assessment Model Based on Multi-Omics Approach | Rayjean Hung | Lunenfeld-Tanenbaum Research Institute, Sinai Health System | NLST | Sep 20, 2017 | NLST-349 |
| Investigation of Deep Learning based methods to Detection, Segmentation and Classification of Lung Nodules | Saeed Seyyedi | Independent | NLST | Sep 20, 2017 | NLST-354 |
| NLP and Machine Vision for Development of Predictive Models to Determine Lung Cancer Risk on Basis of CT Images and Social History. | Joey Bargo | MedMyne | NLST | Sep 19, 2017 | NLST-350 |
| Deep Machine Learning for Detection of Lung Cancer from NLST Images | Eugene Demidenko | Geisel School of Medicine at Dartmouth | NLST | Sep 18, 2017 | NLST-347 |
| Computer assisted detection of abnormalities in chest CT scans | Joel Pinto | Nuance Communications | NLST | Sep 18, 2017 | NLST-348 |
| Selecting the risk cut off for the LLP model. | Kevin ten Haaf | Erasmus MC | NLST | Aug 27, 2017 | NLST-343 |
| Machine learning to identify, track, and monitor disease | John MacLean | doclink.io | NLST | Aug 16, 2017 | NLST-338 |
| Predict histological grade by CT image auto-reviewing | Edwin Wang | University of Calgary | NLST | Aug 16, 2017 | NLST-340 |
| Predicting lung cancer from chest radiography features | Ashwini Suriyaprakash | Independent | NLST | Aug 7, 2017 | NLST-334 |
| Ultra-Low-Dose Lung Nodule CT Surveillance Using Prior-Image-Based Reconstruction | Hao Zhang | Johns Hopkins University | NLST | Aug 3, 2017 | NLST-329 |
| Interpretable machine learning models for lung cancer screening | Cynthia Rudin | Duke | NLST | Aug 2, 2017 | NLST-332 |