ResearcherTUM KlinikumActive in the last 2 years
Fabian Karl Lohöfer
Matched on ORCID iD 0000-0002-0370-122X; OpenAlex author ID A5090408246. Sources: OpenAlex.
Their work
- Papers
- 13 (first author 1, last author 1)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 23
2019: 3 papers · 2020: 2 papers · 2021: 2 papers · 2022: 2 papers · 2023: 2 papers · 2024: none · 2025: 1 paper · 2026: 1 paper
Papers 13
- Contrast enhanced CT and MRI interchangeably reflect tumor characteristics in murine pancreatic cancer. Co-author DOI 10.1038/s41598-026-56008-4↗
- Correction to: Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and reveal gemcitabine‑induce… Co-author DOI 10.1007/s00259-025-07195-1↗
- Determination of "borderline resectable" pancreatic cancer - A global assessment of 30 shades of grey. Co-author DOI 10.1016/j.hpb.2023.07.883↗
- CT Attenuation of Hepatic Pancreatic Cancer Metastases Correlates with Prognostically Detrimental Metastatic Necrosis. Co-author DOI 10.3390/jcm12237319↗
- Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and reveal gemcitabine-induced hypo-vascular… Co-author DOI 10.1007/s00259-022-05930-6↗
- High-Resolution, High b-Value Computed Diffusion-Weighted Imaging Improves Detection of Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.3390/cancers14030470↗
- [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. Co-author DOI 10.1186/s13550-021-00808-4↗
- Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. Last author DOI 10.3390/cancers13092069↗
- Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.3390/jcm9030724↗
- Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters. Co-author DOI 10.3390/jcm9051250↗
- A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging. Co-author DOI 10.1186/s41747-019-0119-0↗
- Borderline-resectable pancreatic adenocarcinoma: Contour irregularity of the venous confluence in pre-operative computed tomography predicts histopathological … First author DOI 10.1371/journal.pone.0208717↗
- A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX … Co-author DOI 10.1371/journal.pone.0218642↗
Closest collaborators
The 23 strongest of 23 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Rickmer Braren · 12 shared papers, 2019–2026 · strength 1.55
- Paper Contrast enhanced CT and MRI interchangeably reflect tumor characteristics in murine pancreatic cancer. DOI 10.1038/s41598-026-56008-4↗
- Paper Correction to: Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC… DOI 10.1007/s00259-025-07195-1↗
- Paper CT Attenuation of Hepatic Pancreatic Cancer Metastases Correlates with Prognostically Detrimental Metastatic Necrosis. DOI 10.3390/jcm12237319↗
- Wilko Weichert · 9 shared papers, 2019–2025 · strength 0.98
- Paper Correction to: Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC… DOI 10.1007/s00259-025-07195-1↗
- Paper Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and … DOI 10.1007/s00259-022-05930-6↗
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Georgios A. Kaissis · 9 shared papers, 2019–2025 · strength 0.98
- Paper Correction to: Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC… DOI 10.1007/s00259-025-07195-1↗
- Paper Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and … DOI 10.1007/s00259-022-05930-6↗
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Sebastian Ziegelmayer · 9 shared papers, 2019–2023 · strength 0.97
- Paper CT Attenuation of Hepatic Pancreatic Cancer Metastases Correlates with Prognostically Detrimental Metastatic Necrosis. DOI 10.3390/jcm12237319↗
- Paper High-Resolution, High b-Value Computed Diffusion-Weighted Imaging Improves Detection of Pancreatic Ductal Adenocarcinom… DOI 10.3390/cancers14030470↗
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Felix N. Harder · 7 shared papers, 2020–2025 · strength 0.86
- Paper Correction to: Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC… DOI 10.1007/s00259-025-07195-1↗
- Paper High-Resolution, High b-Value Computed Diffusion-Weighted Imaging Improves Detection of Pancreatic Ductal Adenocarcinom… DOI 10.3390/cancers14030470↗
- Paper Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and … DOI 10.1007/s00259-022-05930-6↗
- Jens Thomas Siveke · 6 shared papers, 2019–2025 · strength 0.68
- Paper Correction to: Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC… DOI 10.1007/s00259-025-07195-1↗
- Paper Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and … DOI 10.1007/s00259-022-05930-6↗
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Irina Heid · 3 shared papers, 2022–2026 · strength 0.59
- Paper Contrast enhanced CT and MRI interchangeably reflect tumor characteristics in murine pancreatic cancer. DOI 10.1038/s41598-026-56008-4↗
- Paper Correction to: Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC… DOI 10.1007/s00259-025-07195-1↗
- Paper Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and … DOI 10.1007/s00259-022-05930-6↗
- Hana Algül · 4 shared papers, 2019–2025 · strength 0.49
- Paper Correction to: Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC… DOI 10.1007/s00259-025-07195-1↗
- Paper Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and … DOI 10.1007/s00259-022-05930-6↗
- Paper A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preo… DOI 10.1186/s41747-019-0119-0↗
- Roland Michael Schmid · 5 shared papers, 2019–2021 · strength 0.46
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Paper Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic a… DOI 10.3390/jcm9051250↗
- Paper Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. DOI 10.3390/jcm9030724↗
- Friederike Jungmann · 4 shared papers, 2020–2021 · strength 0.41
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Paper Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic a… DOI 10.3390/jcm9051250↗
- Helmut Friess · 4 shared papers, 2019–2021 · strength 0.38
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Paper Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. DOI 10.3390/jcm9030724↗
- Paper A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preo… DOI 10.1186/s41747-019-0119-0↗
- Alexander Muckenhuber · 4 shared papers, 2019–2021 · strength 0.38
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Paper Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic a… DOI 10.3390/jcm9051250↗
- Paper Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. DOI 10.3390/jcm9030724↗
- Lucia Liotta · 2 shared papers, 2022–2025 · strength 0.32
- Paper Correction to: Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC… DOI 10.1007/s00259-025-07195-1↗
- Paper Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and … DOI 10.1007/s00259-022-05930-6↗
- Marija Trajkovic‐Arsic · 2 shared papers, 2022–2025 · strength 0.32
- Paper Correction to: Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC… DOI 10.1007/s00259-025-07195-1↗
- Paper Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and … DOI 10.1007/s00259-022-05930-6↗
- Helmut Frieß · 3 shared papers, 2019–2021 · strength 0.28
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Paper Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic a… DOI 10.3390/jcm9051250↗
- Paper A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response… DOI 10.1371/journal.pone.0218642↗
- Marina Lesina · 1 shared paper, 2026 · strength 0.27
- Lea Würfel · 1 shared paper, 2026 · strength 0.27
- Stefan Reischl · 1 shared paper, 2023 · strength 0.17
- Maximilian Reichert · 1 shared paper, 2021 · strength 0.10
- Michael Quante · 1 shared paper, 2021 · strength 0.10
- İhsan Ekin Demir · 1 shared paper, 2021 · strength 0.10
- Anna Melissa Schlitter · 1 shared paper, 2020 · strength 0.10
- Guralp Onur Ceyhan · 1 shared paper, 2019 · strength 0.09
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.