ResearcherSunnybrook Health Science CentreActive in the last 2 years
Paul Jack Karanicolas
Matched on ORCID iD 0000-0003-2906-7682; OpenAlex author ID A5074604408.
Their work
- Papers
- 8 (first author 0, last author 1)
- Trials
- 1 (principal investigator on 1)
- Grants
- 0
- Collaborators
- 15
2017: 2 papers · 2018: 1 trial · 2019: 1 paper · 2020: 2 papers · 2021: 2 papers · 2022: none · 2023: none · 2024: 1 paper
Papers 8
- Focused ultrasound and concurrent chemotherapy for the treatment of advanced pancreatic cancer: A systematic review. Last author DOI 10.1002/jso.27832↗
- Clinical Trials of Systemic Chemotherapy for Resectable Pancreatic Cancer: A Review. Co-author DOI 10.1001/jamasurg.2021.0149↗
- Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images. Co-author DOI 10.1038/s41598-021-80998-y↗
- CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging. Co-author DOI 10.1186/s12880-020-0418-1↗
- Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.3389/frai.2020.550890↗
- Prognostic Value of CT Radiomic Features in Resectable Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.1038/s41598-019-41728-7↗
- Optimistic honesty: understanding surgeon and patient perspectives on hopeful communication in pancreatic cancer care. Co-author DOI 10.1016/j.hpb.2017.04.001↗
- CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis. Co-author DOI 10.1186/s12880-017-0209-5↗
Trials 1
Closest collaborators
The 15 strongest of 15 ranked by shared papers, trials and grants, with recent and small-team work weighing most.
- Farzad Khalvati · 5 shared papers, 2017–2021
- Paper Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning featu… DOI 10.1038/s41598-021-80998-y↗
- Paper CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging. DOI 10.1186/s12880-020-0418-1↗
- Paper Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma. DOI 10.3389/frai.2020.550890↗
- Masoom A. Haider · 5 shared papers, 2017–2021
- Paper Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning featu… DOI 10.1038/s41598-021-80998-y↗
- Paper CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging. DOI 10.1186/s12880-020-0418-1↗
- Paper Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma. DOI 10.3389/frai.2020.550890↗
- Steve Gallinger · 5 shared papers, 2017–2021
- Paper Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning featu… DOI 10.1038/s41598-021-80998-y↗
- Paper CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging. DOI 10.1186/s12880-020-0418-1↗
- Paper Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma. DOI 10.3389/frai.2020.550890↗
- Yucheng Zhang · 4 shared papers, 2019–2021
- Paper Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning featu… DOI 10.1038/s41598-021-80998-y↗
- Paper CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging. DOI 10.1186/s12880-020-0418-1↗
- Paper Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma. DOI 10.3389/frai.2020.550890↗
- Michael Jonathon Raphael · 1 shared paper, 2024
- Sean Bennett · 1 shared paper, 2024
- Matthew H. G. Katz · 1 shared paper, 2021
- Dimitrios P Moris · 1 shared paper, 2021
- Eileen Mary O'Reilly · 1 shared paper, 2021
- Michail N. Mavros · 1 shared paper, 2021
- Timothy Michael Pawlik · 1 shared paper, 2021
- Kim M. Blakely · 1 shared paper, 2017
- Frances Catriona Wright · 1 shared paper, 2017
- Lesley Gotlib Conn · 1 shared paper, 2017
- Armin Eilaghi · 1 shared paper, 2017
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (the fewest links first, then the links with the most shared work). Built from public records only; no contact details are kept.