ResearcherEnte Ospedaliero CantonaleNo recent work listed
Filippo Del Grande
Matched on ORCID iD 0000-0003-1106-5830; OpenAlex author ID A5039211624. Sources: OpenAlex.
Their work
- Papers
- 2 (first author 0, last author 1)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 6
2022: 1 paper · 2023: 1 paper
Papers 2
- Is Computed-Tomography-Based Body Composition a Reliable Predictor of Chemotherapy-Related Toxicity in Pancreatic Cancer Patients? Co-author DOI 10.3390/cancers15174398↗
- Body composition as a predictor of chemotherapy-related toxicity in pancreatic cancer patients: A systematic review. Last author DOI 10.3389/fonc.2022.974116↗
Closest collaborators
The 6 strongest of 6 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Sara De Dosso · 2 shared papers, 2022–2023 · strength 0.32
- Paper Body composition as a predictor of chemotherapy-related toxicity in pancreatic cancer patients: A systematic review. DOI 10.3389/fonc.2022.974116↗
- Paper Is Computed-Tomography-Based Body Composition a Reliable Predictor of Chemotherapy-Related Toxicity in Pancreatic Cance… DOI 10.3390/cancers15174398↗
- Stefania Rizzo · 2 shared papers, 2022–2023 · strength 0.32
- Paper Body composition as a predictor of chemotherapy-related toxicity in pancreatic cancer patients: A systematic review. DOI 10.3389/fonc.2022.974116↗
- Paper Is Computed-Tomography-Based Body Composition a Reliable Predictor of Chemotherapy-Related Toxicity in Pancreatic Cance… DOI 10.3390/cancers15174398↗
- Marco Cefalì · 2 shared papers, 2022–2023 · strength 0.32
- Paper Body composition as a predictor of chemotherapy-related toxicity in pancreatic cancer patients: A systematic review. DOI 10.3389/fonc.2022.974116↗
- Paper Is Computed-Tomography-Based Body Composition a Reliable Predictor of Chemotherapy-Related Toxicity in Pancreatic Cance… DOI 10.3390/cancers15174398↗
- Saskia Hussung · 1 shared paper, 2023 · strength 0.16
- Dieter Koeberle · 1 shared paper, 2023 · strength 0.16
- Ralph Fritsch · 1 shared paper, 2023 · strength 0.16
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.