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Approved NLST Projects

Here you can browse the complete list of projects for NLST whose requests for data/biospecimens were approved.

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381-400 of 878
Name Principal Investigator Institution Study Date Approved Project ID
Investigation of the dependencies between socio-demographic data and cancer images Miklos Arato Eötvös Loránd University NLST Dec 11, 2019 NLST-613
Classification of Lung Cancer Cells using deep learning algorithms Chris Chatwin University of Sussex NLST Dec 6, 2019 NLST-614
Selection of Eligible People for Lung Cancer Screening using Electronic Primary Care Data (SELECT) Aamir Khakwani University of Nottingham NLST Dec 5, 2019 NLST-611
Lung Cancer Identification Through Artificial Neural Network Marco Domenico Santambrogio Politecnico di Milano NLST Dec 2, 2019 NLST-612
Prediction of Lung cancer and mortality using machine learning and deep learning Thu Cao Columbia University Medical Center NLST Nov 26, 2019 NLST-609
Evaluating the Impact of Preoperative Characteristics on Outcomes of Patients Undergoing Curative-intent Treatment in the NLST Chi-Fu Yang Stanford NLST Nov 25, 2019 NLST-607
Deep learning models to predict lung cancer malignancy Yufeng Deng Infervision US Inc. NLST Nov 25, 2019 NLST-604
Use of Radiomics and AI To Predict Malignancy in Indeterminate Lung Nodules Richard Lee The Royal Marsden Hospital NLST Nov 19, 2019 NLST-593
Optimizing Lung Cancer Early Detection by LDCT via Computer Vision Kingshuk Das AnimanDx Inc NLST Nov 14, 2019 NLST-602
Validating automated algorithms for detection of lung pathology Atilla Kiraly Google NLST Nov 13, 2019 NLST-559
Computer-Aided Diagnostic System for Lung Nodule Detection, Localization, Attribution, and Classification using Deep Learning Devon Bernard Hive Medical NLST Nov 13, 2019 NLST-601
Variability of Observer Interpretations and Lung-RADS Assignment Devon Bernard Hive Medical NLST Nov 13, 2019 NLST-600
Lung cancer CT screening efficacy beyond the NLST screening period Anton Schreuder Radboudumc NLST Nov 13, 2019 NLST-598
Using Region based Convolutional Neural Networks (RCNNs) and its variants to detect, classify, segment and predict lung cancer signatures from NLST data Pratik Shah Massachusetts Institute of Technology NLST Nov 13, 2019 NLST-599
Minibatch Gradient Descent Method for Deep Survival Analysis James Sharpnack University of California-Davis NLST Nov 7, 2019 NLST-597
Extended Cancer Prediction from Lung CT Images Atilla Kiraly Google NLST Nov 1, 2019 NLST-594
Individual lung cancer survival estimation based on radiomics analysis Yujiao Wu University of Technology Sydney NLST Oct 31, 2019 NLST-596
use active learning to do the tumor segmentation. Sijie Wang the university of Tokyo NLST Oct 31, 2019 NLST-595
Interpretable Graph Convolutional Networks with CPC features for Whole Slide Histology Classification Faisal Mahmood Brigham and Women's Hospital NLST Oct 29, 2019 NLST-592
Exascale Deep Learning for Predictive Modeling of Lung Cancer Greeshma Agasthya Oak Ridge National Laboratory NLST Oct 18, 2019 NLST-586