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 |
|---|---|---|---|---|---|
| Advanced Deep Learning for Early Detection and Prognostication in Lung Cancer | Zeng Zeng | Institute for Infocomm Research | NLST | Jul 5, 2017 | NLST-323 |
| Predicting lung cancer prognosis with deep learning | Francesco Ciompi | Radboud University Medical Center | NLST | Jun 30, 2017 | NLST-325 |
| Targeting low-dose CT to reduce mortality from lung cancer: a machine learning-based analysis of heterogeneous treatment effects in the NLST | Aaron Baum | Icahn School of Medicine at Mount Sinai | NLST | Jun 30, 2017 | NLST-324 |
| Using Big Data for Computer-Aided Diagnosis of Chest CTs | Ronald Summers | National Institutes of Health Clinical Center | NLST | Jun 30, 2017 | NLST-319 |
| Machine learning to differentiate between malignant and benign nodules | Parag Chaudhari | Mon Health Medical Center | NLST | Jun 22, 2017 | NLST-315 |
| Deep learning for lung nodule detection and cancer prediction | Quan Chen | University of Kentucky | NLST | Jun 22, 2017 | NLST-320 |
| Robust Radiomics Feature Extraction for Lung Cancer | Suthirth Vaidya | Predible Health | NLST | Jun 20, 2017 | NLST-318 |
| Lung Cancer Early Detection Challenge: Concept to Clinic | Isaac Slavitt | DrivenData | NLST | Jun 20, 2017 | NLST-317 |
| Deep Learning to Detect Lung Cancer and Predict Mortality | George Washko | Brigham and Women's Hospital | NLST | Jun 14, 2017 | NLST-313 |
| Unsupervised feature extraction for benign and malignant pulmonary nodules | Matthew Stephens | University of Cincinnati | NLST | Jun 13, 2017 | NLST-314 |
| Lung Cancer Risk in Patients with Interstitial Lung Disease | Stacey-Ann Whittaker Brown | Icahn School of Medicine at Mount Sinai | NLST | Jun 9, 2017 | NLST-310 |
| Bayesian probabilistic techniques for cancer growth prediction/ prognosis based on informative features containing significant medical details within the historical CT scans | Haroon Rasheed | Bahria University,Karachi - 75620 Pakistan | NLST | Jun 8, 2017 | NLST-307 |
| High Performance Computing Enabled Deep Learning for Lung Cancer Classification | Derek Ni | F. Hoffmann-La Roche AG | NLST | Jun 8, 2017 | NLST-312 |
| Predicting medical outcomes using deep learning with CT chest images | Lyle Palmer | University of Adelaide | NLST | Jun 8, 2017 | NLST-311 |
| Evaluating comorbidities and life expectancy in patients undergoing LDCT screening in the real world setting | Jonathan Iaccarino | Boston University Medical Campus | NLST | May 19, 2017 | NLST-309 |
| Computer Vision AI to Diagnose Lung Cancer from CT Images | Peter Szoldan | MedInnoScan Kft. | NLST | May 12, 2017 | NLST-306 |
| AI-Assisted CT Image Analysis for Accurate Lung Cancer Screening and Early Detection | Yizhou Yu | The University of Hong Kong | NLST | May 11, 2017 | NLST-305 |
| 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 |
| Lung cancer diagnosis by using deep learning | Qinming He | College of Computer Science and Technology, Zhejiang University | NLST | May 4, 2017 | NLST-298 |