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Proceedings Paper

Development of a novel tumor phantom model for head and neck squamous cell carcinoma and its applications
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Paper Abstract

Tumor phantoms (TP) have been described for the purposes of training surgical residents and further understanding tissue characteristics in malignancy. To date, there has not been a tumor phantom described for the purposes of research and training in oncologic surgery of the head and neck focusing on the larynx and pharynx. With the goal of providing radiographic, visual, and physical mimicry of head and neck squamous cell carcinoma (HNSCC), a phantom was developed as a proposed training and research tool for trans-oral surgical procedures such as transoral laser microsurgery (TLM) and transoral robotic surgery (TORS). TP’s were constructed with an agar-gelatin chicken stock base to approximate reported physical properties, then glutaraldehyde and Omnipaque-350 were used as a fixative and to enhance CT-visualization respectively. Further, to ensure heterogeneity in radiographic imaging, other materials like olive oil and condensed milk were explored. These ingredients were combined with the use of a novel, 3D printed, syringe adaptor designed to allow for the direct injection of the liquid tumor into model tissue. TP’s fixed quickly in vivo upon implantation and were imaged using CT and segmented. This injection-based model was piloted in bovine tissue and verified in porcine tissue with excess Omnipaque-350 for volumetric reliability then optimized utilizing 6 well plates. Following radiographic optimization, the viscoelastic properties of TP’s were measured through uniaxial compression. We observed a Young’s modulus similar to published literature values and consistent reproducibility. Most notably, our proposed TP can be used by multiple specialties by altering the color and concentration of agar in the base solution to approximate physical properties.

Paper Details

Date Published: 16 March 2020
PDF: 11 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113152X (16 March 2020); doi: 10.1117/12.2550597
Show Author Affiliations
Michael Sramek, Geisel School of Medicine at Dartmouth (United States)
Yuan Shi, Thayer School of Engineering at Dartmouth (United States)
Erick Quintanilla, Earlham College (United States)
Xiaotian Wu, Thayer School of Engineering at Dartmouth (United States)
Aravind Ponukumati, Geisel School of Medicine at Dartmouth (United States)
David Pastel, Geisel School of Medicine at Dartmouth (United States)
Dartmouth-Hitchcock Medical Ctr. (United States)
Ryan Halter, Thayer School of Engineering at Dartmouth (United States)
Joseph Paydarfar, Geisel School of Medicine at Dartmouth (United States)
Thayer School of Engineering at Dartmouth (United States)
Dartmouth-Hitchcock Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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