Diederik W. M. Rasenberg
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Diederik W. M. Rasenberg has a total of 24 co-authors. Here are the top co-authors by number of shared publications:
- Alexander L. Vahrmeijer
Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
1 shared publication - Bas Groot Koerkamp
Department of Surgery, Erasmus Medical Center, Rotterdam, the Netherlands
1 shared publication - Bert A. Bonsing
Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
1 shared publication - Bin Yu
Department of Experience Design, Philips, 5656 AE Eindhoven, The Netherlands
1 shared publication - Erwin van der Harst
Department of Surgery, Maasstad Hospital, 3079 DZ Rotterdam, The Netherlands
1 shared publication - Frank Willem Jansen
Department of Obstetrics and Gynaecology, Leiden University Medical Centre, Leiden, the Netherlands
1 shared publication
Latest Publications
- Open Access
- 26 February 2025
- Published by MDPI AG in Journal of Clinical Medicine
- Vol. 14 (5) , 1567
Computer-Aided Decision Support and 3D Models in Pancreatic Cancer Surgery: A Pilot Study
Background: Preoperative planning of patients diagnosed with pancreatic head cancer is difficult and requires specific expertise. This pilot study assesses the added value of three-dimensional (3D) patient models and computer-aided detection (CAD) algorithms in determining the resectability of pancreatic head tumors. Methods: This study included 14 hepatopancreatobiliary experts from eight hospitals. The participants assessed three radiologically resectable and three radiologically borderline resectable cases in a simulated setting via crossover design. Groups were divided in controls (using a CT scan), a 3D group (using a CT scan and 3D models), and a CAD group (using a CT scan, 3D and CAD). For the perceived fulfillment of preoperative needs, the quality and confidence of clinical decision-making were evaluated. Results: A higher perceived ability to determine degrees and the length of tumor–vessel contact was reported in the CAD group compared to controls (p = 0.022 and p = 0.003, respectively). Lower degrees of tumor–vessel contact were predicted for radiologically borderline resectable tumors in the CAD group compared to controls (p = 0.037). Higher confidence levels were observed in predicting the need for vascular resection in the 3D group compared to controls (p = 0.033) for all cases combined. Conclusions: “CAD (including 3D) improved experts’ perceived ability to accurately assess vessel involvement and supports the development of evolving techniques that may enhance the diagnosis and treatment of pancreatic cancer”.
- 26 February 2025
- Published by MDPI AG in Journal of Clinical Medicine
- Vol. 14 (5) , 1567
Open AccessComputer-Aided Decision Support and 3D Models in Pancreatic Cancer Surgery: A Pilot Study
Background: Preoperative planning of patients diagnosed with pancreatic head cancer is difficult and requires specific expertise. This pilot study assesses the added value of three-dimensional (3D) patient models and computer-aided detection (CAD) algorithms in determining the resectability of pancreatic head tumors. Methods: This study included 14 hepatopancreatobiliary experts from eight hospitals. The participants assessed three radiologically resectable and three radiologically borderline resectable cases in a simulated setting via crossover design. Groups were divided in controls (using a CT scan), a 3D group (using a CT scan and 3D models), and a CAD group (using a CT scan, 3D and CAD). For the perceived fulfillment of preoperative needs, the quality and confidence of clinical decision-making were evaluated. Results: A higher perceived ability to determine degrees and the length of tumor–vessel contact was reported in the CAD group compared to controls (p = 0.022 and p = 0.003, respectively). Lower degrees of tumor–vessel contact were predicted for radiologically borderline resectable tumors in the CAD group compared to controls (p = 0.037). Higher confidence levels were observed in predicting the need for vascular resection in the 3D group compared to controls (p = 0.033) for all cases combined. Conclusions: “CAD (including 3D) improved experts’ perceived ability to accurately assess vessel involvement and supports the development of evolving techniques that may enhance the diagnosis and treatment of pancreatic cancer”.