ResearcherUniversity of RochesterActive in the last 2 years
Dandan Zheng
Matched on ORCID iD 0000-0003-2259-1633; OpenAlex author ID A5100705796. Sources: OpenAlex.
Their work
- Papers
- 7 (first author 0, last author 3)
- Trials
- 0
- Grants
- 0
- Labs led
- 1
- Collaborators
- 29
2017: 1 paper · 2018: none · 2019: none · 2020: 1 paper · 2021: 1 paper · 2022: 1 paper · 2023: 1 paper · 2024: 1 paper · 2025: none · 2026: 1 paper
Papers 7
- Radiation Therapy for Pancreatic Cancer: An ASTRO Clinical Practice Guideline. Co-author DOI 10.1016/j.prro.2026.07.002↗
- radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. Co-author DOI 10.1038/s41598-024-62741-5↗
- 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for P… Co-author DOI 10.3390/cancers15235496↗
- Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Last author DOI 10.3390/cancers14071654↗
- Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer. Last author DOI 10.1038/s41598-021-95152-x↗
- Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy. Last author DOI 10.3390/cancers12041051↗
- Dosimetric parameters correlate with duodenal histopathologic damage after stereotactic body radiotherapy for pancreatic cancer: Secondary analysis of a prospe… Co-author DOI 10.1016/j.radonc.2016.12.030↗
Closest collaborators
The 25 strongest of 29 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Michael Anthony Hollingsworth · 5 shared papers, 2020–2024 · strength 0.75
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Paper Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Appl… DOI 10.3390/cancers14071654↗
- Michael J. Baine · 5 shared papers, 2020–2024 · strength 0.75
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Paper Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Appl… DOI 10.3390/cancers14071654↗
- Hongfeng Yu · 3 shared papers, 2022–2024 · strength 0.52
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Paper Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Appl… DOI 10.3390/cancers14071654↗
- Chi H. Lin · 4 shared papers, 2017–2022 · strength 0.45
- Paper Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Appl… DOI 10.3390/cancers14071654↗
- Paper Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer. DOI 10.1038/s41598-021-95152-x↗
- Paper Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy. DOI 10.3390/cancers12041051↗
- Chi Zhang · 2 shared papers, 2023–2024 · strength 0.37
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Kiersten Preuss · 2 shared papers, 2022–2024 · strength 0.33
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Paper Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Appl… DOI 10.3390/cancers14071654↗
- Jeffrey Wong · 2 shared papers, 2021–2024 · strength 0.31
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Paper Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer. DOI 10.1038/s41598-021-95152-x↗
- Rachna T. Shroff · 1 shared paper, 2026 · strength 0.22
- Michael Frederick Bassetti · 1 shared paper, 2026 · strength 0.22
- Nina Niu Sanford · 1 shared paper, 2026 · strength 0.22
- Michael David Chuong · 1 shared paper, 2026 · strength 0.22
- Marsha Reyngold · 1 shared paper, 2026 · strength 0.22
- Jessica M. Frakes · 1 shared paper, 2026 · strength 0.22
- Karyn A. Goodman · 1 shared paper, 2026 · strength 0.22
- Thomas Baptist Brunner · 1 shared paper, 2026 · strength 0.22
- Diego A.S. Toesca · 1 shared paper, 2026 · strength 0.22
- Daniel Tandel Chang · 1 shared paper, 2026 · strength 0.22
- Krishan R. Jethwa · 1 shared paper, 2026 · strength 0.22
- Ethan Bernard Ludmir · 1 shared paper, 2026 · strength 0.22
- Susan Tsai · 1 shared paper, 2026 · strength 0.22
- Amol Kumar Narang · 1 shared paper, 2026 · strength 0.22
- Yu Shi · 1 shared paper, 2023 · strength 0.19
- Yu Shi · 1 shared paper, 2024 · strength 0.18
- Paul M. Grandgenett · 1 shared paper, 2024 · strength 0.18
- Justin A. Chen · 1 shared paper, 2022 · strength 0.15
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.