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Updated: Jul 20, 2026

Step By Step: Microsurgical training method combining two nonliving animal models
Published on: May 9, 2015
Onward to Better Surgery - the Critical Need for Improved Ex Vivo Testing and Training Methods
Eric R Henderson1,2,3, Ryan Halter1, Keith D Paulsen1
1Thayer school of Engineering, Dartmouth College, Hanover, NH.
Abstract:
Guided surgery has demonstrated significant improvements in patient outcomes in some disease processes. Interest in this field has led to substantial growth in the technologies under investigation. Most likely no single technology will prove to be "best," and combinations of macro- and microscale guidance-using radiological imaging navigation, probes (activatable, perfusion, and molecular-targeted; large- and small-molecule), autofluorescence, tissue intrinsic optical properties, bioimpedance, and other characteristics-will offer patients and surgeons the greatest opportunity for high-success/low-morbidity medical interventions. Problems are arising, however, from the lack of valid testing formats; surgical training simulators suffer the same problems. Small animal models do not accurately recreate human anatomy, especially in terms of tissue volume. Large animal models are expensive and have difficulty replicating many pathological states, particularly when molecular specificity for individual cancers is required. Furthermore, the sheer number of technologies and the potential for synergistic combination leads to exponential growth of testing requirements that is unrealistic for in vivo testing. Therefore, critical need exists to expand the ex vivo/in vitro testing platforms available to investigators and, once validated, a need to increase the acceptance of these methods for funding and regulatory endpoints. Herein is a review of the available ex vivo/in vitro testing formats for guided surgery, a review of their advantages/disadvantages, and consideration for how our field may safely and more swiftly move forward through stronger adoption of these testing and validation methods.
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