Computer-Aided Decision Support and 3D Models in Pancreatic Cancer Surgery
A Pilot Study
Diederik W.M. Rasenberg (Student TU Delft)
Mark Ramaekers (Catharina Hospital)
Igor Jacobs (Philips Research)
Jon R. Pluyter (Philips Research)
Luc J.F. Geurts (Philips Research)
Bin Yu (Philips Research)
John C.P. van der Ven (Philips Research)
Joost Nederend (Catharina Hospital)
Ignace H.J.T. de Hingh (Maastricht University, Catharina Hospital)
Bert A. Bonsing (Leiden University Medical Center)
Alexander L. Vahrmeijer (Leiden University Medical Center)
Erwin van der Harst (Maasstad Hospital)
Marcel den Dulk (Maastricht University Medical Center)
Ronald M. van Dam (Maastricht University Medical Center)
Bas Groot Koerkamp (Erasmus MC)
Joris I. Erdmann (Universiteit van Amsterdam)
Freek Daams (Universiteit van Amsterdam)
Olivier R. Busch (Universiteit van Amsterdam)
Marc G. Besselink (Universiteit van Amsterdam)
Wouter W. te Riele (St. Antonius Hospital)
Rinze Reinhard (Onze Lieve Vrouwe Gasthuis)
Frank Willem Jansen (TU Delft - Medical Instruments & Bio-Inspired Technology, Leiden University Medical Center)
Jenny Dankelman (TU Delft - Medical Instruments & Bio-Inspired Technology)
J. Sven D. Mieog (Leiden University Medical Center)
Misha D.P. Luyer (Eindhoven University of Technology, Catharina Hospital)
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Abstract
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”.
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