| Name
|
Principal Investigator
|
Institution
|
Study
|
Date Approved
|
Project ID
|
| Intelligent personable treatment recommendation |
Xiaoshui Huang |
The University of Sydney |
NLST |
Nov 15, 2019 |
NLST-584 |
| A Comparison Between Multi Classification Algorithms on PLCO Colorectal Cancer Dataset |
Bawer Berwary |
Van Yuzuncu Yil University |
PLCO |
Nov 14, 2019 |
PLCO-533 |
| Predicting Cancer diagnosis based on preliminary screenings |
Jonathan Mendoza |
Independent |
PLCO |
Nov 14, 2019 |
PLCO-532 |
| Link-based survival additive models with mixed types of censoring. |
Giampiero Marra |
University College London |
PLCO |
Nov 14, 2019 |
PLCO-538 |
| Data mining study on clinical and imaging data for assessing cancer risk and estimating outcome |
Shandong Wu |
University of Pittsburgh, Department of Radiology |
PLCO |
Nov 14, 2019 |
PLCOI-519 |
| Predicting lung cancer recurrence with machine learning |
Flavio Calmon |
Harvard University |
PLCO |
Nov 13, 2019 |
PLCOI-522 |
| 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 |
| Bias-correction of relative risks by robustly incorporating validation studies that include multiple methods of physical activity assessment and related biomarkers |
Xin Zhou |
Yale University |
IDATA |
Nov 7, 2019 |
IDATA-32 |
| Minibatch Gradient Descent Method for Deep Survival Analysis |
James Sharpnack |
University of California-Davis |
NLST |
Nov 7, 2019 |
NLST-597 |
| Machine Learning to select patients benefiting from prostate cancer screening |
Jean-Emmanuel Bibault |
Laboratory of Artificial Intelligence in Medicine and Biomedical Physics, Stanford University |
PLCO |
Nov 6, 2019 |
PLCO-541 |
| Exploration of deep learning-based model's lung cancer screening capabilities. |
Manwoo Lee |
Samsung Electronics |
PLCO |
Nov 4, 2019 |
PLCOI-537 |
| Quantitative analysis of tissue morphology and biomarkers with convolutional neural networks for improve prognostics of prostate cancer |
Geert Litjens |
Radboud University Medical Center |
PLCO |
Nov 4, 2019 |
PLCOI-540 |
| “Development of a probabilistic network for clinical pathways based on patient cohort characteristics, outcome-indicators and clinical workflow analysis to create artificial patients with prostatic adenocarcinoma” |
Andrea Riedel |
Institute for general practitioners |
PLCO |
Nov 4, 2019 |
PLCO-535 |
| Evaluation of Patients Data using AI techniques and It's Application in Pancreatic Cancer Differential Diagnosis |
Wanessa Sena |
Federal Institute of Pernambuco |
PLCO |
Nov 4, 2019 |
PLCO-539 |
| Inflammatory potential of diet and risk of ovarian cancer by menopausal status in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial |
Zhuyue Li |
West China Hospital |
PLCO |
Nov 4, 2019 |
PLCO-536 |
| Dietary Inflammatory Index and risk of differentiated thyroid cancer in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial |
Li Chen |
Department of the Endocrine and Breast Surgery, The First Affiliated hospital of Chongqing Medical university |
PLCO |
Nov 4, 2019 |
PLCO-543 |
| Integrative analysis to predict lung cancer patient outcome using NLST dataset |
Guanghua Xiao |
UT Southwestern Medical Center |
NLST |
Nov 4, 2019 |
NLST-562 |
| Chest CT image registration |
Yufa Xia |
Shenzhen institutes of advanced technology, Chinese Academy of Sciences |
NLST |
Nov 4, 2019 |
NLST-578 |
| Predicting lung cancer recurrence with machine learning |
Flavio Calmon |
Harvard University |
NLST |
Nov 4, 2019 |
NLST-567 |
| Individual lung cancer survival estimation based on radiomics analysis |
Yujiao Wu |
University of Technology Sydney |
NLST |
Oct 31, 2019 |
NLST-596 |
