Computer-Aided Decision Support and 3D Models in Pancreatic Cancer Surgery: A Pilot Study

<b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> A higher perceived ability to determine degrees and the length of tumor-vessel contact was reported in the CAD group compared to controls (<i>p</i> = 0.022 and <i>p</i> = 0.003, respectively). Lower degrees of tumor-vessel contact were predicted for radiologically borderline resectable tumors in the CAD group compared to controls (<i>p</i> = 0.037). Higher confidence levels were observed in predicting the need for vascular resection in the 3D group compared to controls (<i>p</i> = 0.033) for all cases combined. <b>Conclusions:</b> "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".

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Publication Details

Journal
Journal of Clinical Medicine
Published
2025-02-26
DOI
https://doi.org/10.3390/jcm14051567
Citations
2
Primary Topic
Pancreatic and Hepatic Oncology Research
Type
article
Field-Weighted Citation Impact
2.09
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article

Computer-Aided Decision Support and 3D Models in Pancreatic Cancer Surgery: A Pilot Study

Igor Jacobs, Marc G. Besselink, J. Sven D. Mieog, Bas Groot Koerkamp et al.
2 citations
Journal of Clinical Medicine
Pancreatic and Hepatic Oncology Research
2.09
article

Computer-Aided Decision Support and 3D Models in Pancreatic Cancer Surgery: A Pilot Study

Igor Jacobs, Marc G. Besselink, J. Sven D. Mieog, Bas Groot Koerkamp, Alexander L. Vahrmeijer, Mark Ramaekers, Olivier R. Busch, Wouter W. te Riele, Ronald M. van Dam, Joris I. Erdmann, Bert A. Bonsing, Misha D. P. Luyer, Joost Nederend, Luc Geurts, Marcel den Dulk, Jenny Dankelman, Erwin van der Harst, Diederik W. M. Rasenberg, Ignace H. J. T. de Hingh, Jon R. Pluyter, Rinze Reinhard, Freek D. Daams, Bin Yu, Frank Willem Jansen, John C. P. van der Ven
article en
2 citations

Abstract

<b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> A higher perceived ability to determine degrees and the length of tumor-vessel contact was reported in the CAD group compared to controls (<i>p</i> = 0.022 and <i>p</i> = 0.003, respectively). Lower degrees of tumor-vessel contact were predicted for radiologically borderline resectable tumors in the CAD group compared to controls (<i>p</i> = 0.037). Higher confidence levels were observed in predicting the need for vascular resection in the 3D group compared to controls (<i>p</i> = 0.033) for all cases combined. <b>Conclusions:</b> "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".

Journal of Clinical MedicineVol. 14(5)
NXP (Netherlands) (NL), Radboud University Nijmegen (NL), Leiden University Medical Center (NL), Maastricht University Medical Centre (NL), Catharina Ziekenhuis (NL), Maastricht University (NL), St. Antonius Ziekenhuis (NL), OLVG (NL), Maasstad Ziekenhuis (NL), Philips (Netherlands) (NL), Amsterdam University Medical Centers (NL), Eindhoven University of Technology (NL), Erasmus University Rotterdam (NL), Delft University of Technology (NL)
Peace, Justice and strong institutions
Openalex Percentile: Top 14%
Pancreatic and Hepatic Oncology Research
2.09
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