Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform

Abstract

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.

FIG 1.

A computational framework to create image-based patient-specific tumor molds. (A) The schematic depicts the various steps of the method, bridging from magnetic resonance imaging (MRI) scans to spatially targeted surgical biopsies. The method starts with the delineation of an MRI scan, which is then reoriented, carved out of a 3-dimensional–printed mold, and used for spatially accurate surgical biopsies. The slots of the mold guide the knife for cutting. (B) Flowchart of the different analysis steps performed by the radiology, surgery, pathology, and computational groups to ensure seamless integration between the clinical and research arms. The blue box highlights the computational steps of the pipeline. mpMRI, multiparametric MRI.

FIG 2.

Optimized, patient-specific tumor molds. Representative T₁-weighted magnetic resonance imaging slices and corresponding 3-dimensional renderings of the tumor molds created for the 6 patients included in the study.

FIG 3.

Validation results. (A) Overlay of the tissue region boundaries (black) and the corresponding magnetic resonance imaging (MRI) segmentations (red) for tumor and kidney regions. Dice similarity coefficients (DSCs) are calculated for tumor and kidney tissues separately. (B) Left: Overlay of a photograph of the section from the first patient and the corresponding MRI maps, including anatomic region segmentations (top) and multiparametric tumor habitats (bottom). Right: Relative distributions of imaging parameters for the 3 tumor habitats. AU, arbitrary units; DCE, dynamic contrast-enhanced; IVIM, intravoxel incoherent motion; RP, renal pelvis; T₁w, T₁-weighted; T₂w, T₂-weighted.