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Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform
Mireia Crispin-Ortuzar1, Marcel Gehrung1, Stephan Ursprung1,2
1Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.
Purpose:
Spatial heterogeneity of tumors is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood because it relies on the accurate coregistration of medical images and tissue biopsies. Tumor molds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumor heterogeneity data.
Methods:
We have developed an open-source computational framework to automatically produce patient-specific 3-dimensional-printed molds that can be used in the clinical setting. Our approach achieves accurate coregistration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging, and pathology workflows.
Results:
We applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalized molds for 6 patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.96 for tumor regions and between 0.70 and 0.76 for healthy kidneys. The framework required minimal manual intervention, producing the final mold design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes.
Conclusion:
Our work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumor heterogeneity on multiple spatial scales.
Insights
We developed an automated 3D-printed mold framework for precise tumor biopsy localization. This tool enhances the integration of imaging and tissue data for precision oncology research.
Area of Science:
- Oncology
- Medical Imaging
- Biotechnology
Background:
- Tumor spatial heterogeneity presents a significant challenge in precision oncology.
- Understanding the link between molecular and imaging heterogeneity requires accurate coregistration of medical images and tissue biopsies.
- Current methods for creating tumor molds are time-consuming and difficult to integrate into clinical practice, hindering multiscale tumor heterogeneity research.
Purpose of the Study:
- To develop an open-source computational framework for automated, patient-specific 3D-printed tumor molds.
- To enable accurate spatial coregistration between medical imaging and tissue samples for clinical use.
- To facilitate seamless integration with existing clinical, imaging, and pathology workflows.
Main Methods:
- Developed an open-source computational framework for automated 3D-printed mold generation.
- The framework produces patient-specific molds for precise biopsy localization.
- Integrated the framework into clinical workflows for renal cancer patients undergoing radical nephrectomy.
Main Results:
- Successfully created personalized molds for 6 renal cancer patients.
- Achieved high Dice similarity coefficients (0.86-0.96) for tumor regions between imaging and tissue.
- Demonstrated minimal manual intervention and rapid mold design generation (minutes).
Conclusions:
- The developed framework provides a robust, automated interface between medical imaging and tissue samples.
- This technology enables clinical studies to investigate tumor heterogeneity across multiple spatial scales.
- Facilitates advancements in precision oncology by improving multiscale data integration.
