Innovative 3D-Printed Tumor Models Enhance Surgical Imaging Techniques
Texas Tech researchers have developed 3D-printed tumor models that replicate human tissue, aiming to refine surgical imaging techniques and improve tumor removal accuracy.
Researchers at Texas Tech University have developed advanced 3D-printed tumor phantoms that closely replicate the optical and physical properties of real human tumors. These models, created by Assistant Professor Indrajit Srivastava and his team, aim to improve surgical imaging and tumor removal procedures. Crafted from substances like lipids, hemoglobin, enzymes, and tumor cells bound with gelatin, the models are designed in various shapes that mimic actual tumors, enabling more accurate testing of imaging agents.
A major motivation behind this innovation is to enhance the effectiveness of afterglow imaging, a promising technique that allows light to penetrate deeper into tumors and remain visible for up to ten minutes. This extended visibility could provide surgeons with more precise guidance during tumor excisions, potentially surpassing traditional fluorescence-guided surgery.
Currently, testing in animals such as mice reveals limitations, primarily because mice have much thinner skin layers compared to humans, affecting the reliability of imaging results. The 3D phantom models offer a cost-effective and human-relevant alternative, allowing researchers to evaluate how imaging agents behave in environments akin to human tissues.
The team’s research, published in ACS Nano, involved collaboration with graduate and undergraduate students and other university labs. They aim to refine these models further, including extending the lifespan of tumor cells within the phantoms to simulate living tumors more accurately. Srivastava envisions that these models could support regulatory approval processes and facilitate the development of new surgical imaging technologies.
With advances in this area, the goal is to reduce reliance on animal studies, enhance the precision of surgical imaging, and ultimately improve patient outcomes. Srivastava notes that these models align with the move toward more human-centric research approaches supported by agencies like the FDA and NIH.
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