Approved Projects
Here you can browse the complete list of projects on CDAS whose requests for data/biospecimens were approved.
| Name | Principal Investigator | Institution | Study | Date Approved | Project ID |
|---|---|---|---|---|---|
| A Deep Learning Model for Improved Cancer Risk Prediction in Sequential Lung Screening X-Rays | Regina Barzilay | Massachusetts Institute of Technology | PLCO | Feb 17, 2022 | PLCOI-919 |
| Self-supervised Training in chest X-rays domain. | Song Luo | Northwestern University | PLCO | Feb 16, 2022 | PLCOI-920 |
| Computerized image analysis of morphology, texture on H&E slides of colon cancer patients | Anant Madabhushi | Emory University | PLCO | Feb 16, 2022 | PLCOI-917 |
| PLCO data integration to PD research as control | Andrew Singleton | National Institute of Aging | PLCO | Feb 16, 2022 | PLCO-914 |
| Identification and Characterization of Genetic Variants Predisposing Cancer | Jeffrey Smith | Vanderbilt University Medical Center | PLCO | Feb 16, 2022 | PLCO-918 |
| Bachelor's thesis project: Investigating how machine learning can be applied to predict pancreatic cancer | Arvid Eriksson | Royal Institute of Technology (KTH) | PLCO | Feb 16, 2022 | PLCO-916 |
| Risk of Prostate Cancer Death in the PLCO Cohort Based on a Four Kallekrein Panel | Jonathan Shoag | Case Western Reserve University | PLCO | Feb 16, 2022 | PLCO-912 |
| Machine Learning for Breast Cancer Risk Prediction | Sigrid Sandström | Stockholm University | PLCO | Feb 16, 2022 | PLCO-910 |
| Validating the performance of Deep Neural Network models for lung nodule detection | Naglis Ramanauskas | Oxipit, UAB | NLST | Feb 16, 2022 | NLST-886 |
| A Deep Learning Model for Improved Cancer Risk Prediction in Sequential Lung Screening X-Rays | Regina Barzilay | Massachusetts Institute of Technology | NLST | Feb 16, 2022 | NLST-885 |
| Building integrative multi-modal and interpretable AI systems for personalized lung cancer therapy | Ruijiang Li | Stanford University | NLST | Feb 16, 2022 | NLST-884 |
| Optimization of lung cancer screening interval by deep learning-based risk prediction | Adam Alessio | Michigan State University | NLST | Feb 16, 2022 | NLST-883 |
| Personalised lung cancer treatment through outcome predictions and patient stratification | Charles-Antoine Collins Fekete | University College London | NLST | Feb 16, 2022 | NLST-881 |
| An evaluation of the association between aspirin and NSAID use and changes in body mass index on gastrointestinal cancer incidence and mortality | Holli Loomans-Kropp | The Ohio State University | PLCO | Feb 15, 2022 | PLCO-907 |
| The Lung EArly Proteins (LEAP) Project | Hilary Robbins | International Agency for Research on Cancer | PLCO | Feb 3, 2022 | 2021-1022 |
| Progression prediction of lung cancer using deep learning model | Sunghoon Kwon | Seoul National University | NLST | Feb 2, 2022 | NLST-879 |
| Competing risks analysis of multiple cancers with application to vitamin D in PLCO | Yei Eun Shin | Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute | PLCO | Feb 2, 2022 | PLCO-904 |
| Development of a ML algorithm to predict ovarian cancer risk | Nicolas Martelin | Prostperia SAS | PLCO | Jan 27, 2022 | PLCO-903 |
| Lung Cancer Patient's survival analysis | Sreekanth Settur | Swiss School of Business and Management | PLCO | Jan 27, 2022 | PLCO-902 |
| Assessing the Generalizability of Findings from the NLST | Louise Henderson | University of North Carolina at Chapel Hill | NLST | Jan 26, 2022 | NLST-876 |