| Multimodal Deep Learning Model for Lung Cancer Detection: Optimal Fusion of Radiological and Clinical Data for Precision Medicine |
Tushar Mehta |
Parkland High School |
NLST |
Dec 16, 2024 |
NLST-1369 |
| Evaluating the Impact of Radiologist Experience and AI Integration on Lung Cancer Screening Performance: A Comparative Analysis of a Leading Dutch Radiology Center Using NLST Data |
Alex Puiu |
PRMSE-SCREENING |
NLST |
Dec 16, 2024 |
NLST-1368 |
| Multimodal and interpretable deep learning algorithm to classify lung nodules |
Luis Montuenga |
University of Navarra |
NLST |
Dec 16, 2024 |
NLST-1367 |
| A nested case-control study from NLST to evaluate a multiplex protein biomarker-based immunoassay for the early detection of bladder cancer |
Charles Rosser |
Cedars-Sinai Medical Center |
NLST |
Nov 21, 2024 |
NLST-1360 |
| Chest CT Radiomics and Outcomes |
Sadeer Al-Kindi |
Houston Methodist Research Institute |
NLST |
Nov 21, 2024 |
NLST-1359 |
| Improving risk stratification for lung cancer screening using peripheral blood leukocyte DNA methylation: an investigation in the National Lung Screening Trial (NLST) |
Dominique Michaud |
Tufts University |
NLST |
Nov 19, 2024 |
NLST-1358 |
| Improved Methods for Comparing Human Radiologists to AI tools |
Ashesh Rambachan |
Massachusetts Institute of Technology |
NLST |
Nov 12, 2024 |
NLST-1357 |
| Machine Learning approach for the analysis of lung cancer data to aid the radiologists at global level |
Tiratharaj Singh |
Jaypee University of Information Technology (JUIT) |
NLST |
Nov 12, 2024 |
NLST-1355 |
| Predicting Lung Nodule Growth Using Deep Learning Methods |
gao dong chen |
Shan Dong University |
NLST |
Nov 5, 2024 |
NLST-1354 |
| Using deep learning models to infer spatial transcriptomics from H&E slides |
Eytan Ruppin |
Cancer Data Science Laboratory, NCI |
NLST |
Nov 5, 2024 |
NLST-1353 |
| Comparing the accuracy of machine learning diagnosis of lung cancer from different data modalities |
Polina Golland |
Massachusetts Institute of Technology |
NLST |
Nov 5, 2024 |
NLST-1349 |
| Radiomics-Driven Predictive Lung Cancer Models |
Patrice Essien |
George Washington University |
NLST |
Nov 5, 2024 |
NLST-1347 |
| Surrogate endpoints for cancer screening trials |
Adam Brentnall |
Queen Mary University of London |
NLST |
Nov 5, 2024 |
NLST-1345 |
| Analysis of Chest X-rays Based on Deep Learning for Health Risk Assessment |
Yu Fei Huang |
China Medical University |
NLST |
Nov 5, 2024 |
NLST-1351 |
| Quantification of Interstitial Lung Abnormalities Using Data from the National Lung Screening Trial |
Hyungjin Kim |
Seoul National University Hospital |
NLST |
Nov 5, 2024 |
NLST-1350 |
| Combined model integrating pericardial fat and tumor radiomics with clinical data to classify pulmonary nodules: A multicentric analysis |
Wenjuan Huang |
Harbin medical university cancer hospital |
NLST |
Nov 5, 2024 |
NLST-1346 |
| Framework for quantifying the effects of screening interventions using intermediate trial outcomes |
Vichithranie Madurasinghe |
University of Warwick |
NLST |
Oct 21, 2024 |
NLST-1344 |
| Deep Learning-Based Coronary Calcification Risk Prediction: A Clinical Application Study Using the NLST Dataset |
Qian Tan |
South China University of Technology |
NLST |
Oct 21, 2024 |
NLST-1343 |
| Correlation of clinical information with imaging values gained from chest CTs analyzed with deep learning algorithm |
Johannes Hofmanninger |
contextflow |
NLST |
Oct 16, 2024 |
NLST-1339 |
| Real-Time Medical Device Performance Monitoring with CUSUM Control Chart |
Constantine Gatsonis |
Brown University |
NLST |
Oct 11, 2024 |
NLST-1331 |
