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

• Published in: Journal of clinical medicine Vol. 14; no. 5; p. 1567
• Main Authors: Rasenberg, Diederik W. M., Ramaekers, Mark, Jacobs, Igor, Pluyter, Jon R., Geurts, Luc J. F., Yu, Bin, van der Ven, John C. P., Nederend, Joost, de Hingh, Ignace H. J. T., Bonsing, Bert A., Vahrmeijer, Alexander L., van der Harst, Erwin, den Dulk, Marcel, van Dam, Ronald M., Groot Koerkamp, Bas, Erdmann, Joris I., Daams, Freek, Busch, Olivier R., Besselink, Marc G., te Riele, Wouter W., Reinhard, Rinze, Jansen, Frank Willem, Dankelman, Jenny, Mieog, J. Sven D., Luyer, Misha D. P.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2025; MDPI
• Subjects: Accuracy; Artificial intelligence; Cancer therapies; Care and treatment; Decision making; Decision support systems; Deep learning; Health aspects; Likert scale; Medical imaging; Methods; Neural networks; Pancreatic cancer; Pancreaticoduodenectomy; Pathology; Patients; Perceptions; Pilot projects; Quality management; Questionnaires; Surgeons; Tumors; Veins & arteries; Visualization; Netherlands; pancreatoduodenectomy; vascular involvement; pancreatic carcinoma; preoperative planning; integrated medical imaging workstation; computer-aided detection; autostereoscopic three-dimensional patient model
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm14051567

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”.