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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.
JCO Clinical Cancer Informatics
|August 18, 2020
Summary
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.
