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David Bonekamp

Heidelberg University · DE
Area of research
Pulmonary and Respiratory Medicine · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include Prostate Cancer Diagnosis and Treatment, Radiomics and Machine Learning in Medical Imaging, MRI in cancer diagnosis, and Prostate Cancer Treatment and Research.
h-index
47
citations
9,162
works
260
NIH funding
primary concept
Medicine
email

Recent publications

AI-Assisted vs Unassisted Identification of Prostate Cancer in Magnetic Resonance Images
JAMA Network Open 2025cited by 22position: middledoi
Prostate Cancer Detection in Younger Men: A Comparative Analysis of Systematic and Magnetic Resonance Imaging–targeted Biopsy in the PROBASE Trial
European Urology 2025cited by 10position: middledoi
In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning
Scientific Reports 2025cited by 3position: lastdoi
Value of Additional Systematic Cores During Magnetic Resonance Imaging–guided Targeted Biopsy in Prostate Cancer Screening for Young Men: Results from the PROBASE Trial
European Urology Oncology 2025cited by 1position: middledoi
Combined radiomics and liquid biopsy reflect tumor biology towards multimodal non-invasive prostate cancer risk stratification
2025cited by 0position: contributordoi
Evaluating Biparametric Versus Multiparametric Magnetic Resonance Imaging for Diagnosing Clinically Significant Prostate Cancer: An International, Paired, Noninferiority, Confirmatory Observer Study
European Urology 2024cited by 56position: middledoi
Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms
European Radiology 2024cited by 29position: lastdoi
Effectiveness of the Medical Chatbot PROSCA to Inform Patients About Prostate Cancer: Results of a Randomized Controlled Trial
European Urology Open Science 2024cited by 23position: middledoi
Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.
2024cited by 14position: contributordoi
Dosimetric benefit of online treatment plan adaptation in stereotactic ultrahypofractionated MR-guided radiotherapy for localized prostate cancer
Frontiers in Oncology 2024cited by 10position: middledoi
Weakly Supervised MRI Slice-Level Deep Learning Classification of Prostate Cancer Approximates Full Voxel- and Slice-Level Annotation: Effect of Increasing Training Set Size.
2024cited by 7position: contributordoi
Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study
EJNMMI Reports 2024cited by 3position: middledoi
Abstract: Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection
Informatik aktuell 2024cited by 1position: middledoi
Abstract: Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection
Informatik aktuell 2024cited by 0position: contributordoi
Contribution of Dynamic Contrast-enhanced and Diffusion MRI to PI-RADS for Detecting Clinically Significant Prostate Cancer.
2023cited by 50position: contributordoi
Multiparametric Magnetic Resonance Imaging in Prostate Cancer Screening at the Age of 45 Years: Results from the First Screening Round of the PROBASE Trial
European Urology 2023cited by 48position: middledoi
Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability
European Radiology 2023cited by 17position: lastdoi
Targeted magnetic resonance imaging (tMRI) of small changes in the T1 and spatial properties of normal or near normal appearing white and gray matter in disease of the brain using divided subtracted inversion recovery (dSIR) and divided reverse subtracted inversion recovery (drSIR) sequences
Quantitative Imaging in Medicine and Surgery 2023cited by 15position: middledoi
Weakly Supervised <scp>MRI</scp> Slice‐Level Deep Learning Classification of Prostate Cancer Approximates Full Voxel‐ and Slice‐Level Annotation: Effect of Increasing Training Set Size
Journal of Magnetic Resonance Imaging 2023cited by 12position: lastdoi
Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability.
2023cited by 12position: contributordoi
Same-day repeatability and Between-Sequence reproducibility of Mean ADC in PI-RADS lesions
European Journal of Radiology 2023cited by 10position: lastdoi
Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer
Scientific Reports 2023cited by 9position: middledoi
Assessing the added value of apparent diffusion coefficient, cerebral blood volume, and radiomic magnetic resonance features for differentiation of pseudoprogression versus true tumor progression in patients with glioblastoma
Neuro-Oncology Advances 2023cited by 7position: middledoi
Systematische oder gezielte Fusionsbiopsie der Prostata
Die Urologie 2023cited by 7position: middledoi
Test–retest, inter- and intra-rater reproducibility of size measurements of focal bone marrow lesions in MRI in patients with multiple myeloma
British Journal of Radiology 2023cited by 6position: middledoi
Anatomy-Informed Data Augmentation for Enhanced Prostate Cancer Detection
Lecture notes in computer science 2023cited by 6position: middledoi
Anatomy-Informed Data Augmentation for Enhanced Prostate Cancer Detection
Lecture Notes in Computer Science 2023cited by 6position: contributordoi
Are T2WI PI-RADS sub-scores of transition zone prostate lesions biased by DWI information? A multi-reader, single-center study
European Journal of Radiology 2023cited by 5position: middledoi
Detection Rate of Prostate Cancer in Repeat Biopsy after an Initial Negative Magnetic Resonance Imaging/Ultrasound-Guided Biopsy
Diagnostics 2023cited by 4position: middledoi
Durability of Functional Outcomes After MRI-Guided Transurethral Ultrasound Ablation of the Prostate
JU Open Plus 2023cited by 4position: middledoi

Grants

No grants ingested yet.

Frequent collaborators

Heinz-Peter Schlemmer · German Cancer Research Center76 papers (2015–2024)Markus Hohenfellner · Chirurgische Universitätsklinik Heidelberg45 papers (2016–2024)Klaus Maier‐Hein · German Cancer Research Center33 papers (2016–2025)Jan Philipp Radtke · German Cancer Research Center31 papers (2016–2023)Albrecht Stenzinger · Chirurgische Universitätsklinik Heidelberg25 papers (2016–2025)Philipp Kickingereder · University of Bonn22 papers (2015–2023)Boris Hadaschik · Deutsche Gesellschaft für Urologie22 papers (2016–2023)Magdalena Görtz · German Cancer Research Center21 papers (2019–2025)Wolfgang Wick · Heidelberger Institut für Radioonkologie20 papers (2014–2023)Martin Bendszus · University Hospital and Clinics20 papers (2014–2023)Alexander Radbruch · University of Bonn18 papers (2014–2018)Viktoria Schütz · University Hospital Heidelberg17 papers (2017–2024) · 15 papers (2019–2025)Antje Wick · Heidelberg University14 papers (2015–2019)Thomas Hielscher · German Cancer Research Center14 papers (2020–2025)Claudia Kesch · Groupe Hospitalier Diaconesses Croix Saint-Simon14 papers (2016–2022)Manuel Wiesenfarth · German Cancer Research Center13 papers (2017–2022)Tristan Anselm Kuder · German Cancer Research Center12 papers (2017–2022)Patrick Schelb · German Cancer Research Center11 papers (2018–2022)Stefan Delorme · German Cancer Research Center11 papers (2017–2023)