Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform
Publication: JCO Clinical Cancer Informatics
Mireia Crispin-Ortuzar, Marcel Gehrung, Stephan Ursprung, Andrew B. Gill, Anne Y. Warren, Lucian Beer, Ferdia A. Gallagher, Thomas J. Mitchell, Iosif A. Mendichovszky, Andrew N. Priest, Grant D. Stewart, Evis Sala, Florian Markowetz
August 2020
Summary
Cancer is a highly heterogeneous disease. Different parts of a single tumour often look different in medical images; they sometimes even carry different genetic information. This complexity may be key to understanding why some tumours respond better to therapy than others. Once the tumour has been removed through surgery, researchers can obtain tissue samples that allow them to study its spatial composition. However, matching these data to the images that were obtained before surgery is challenging.
The research team developed a computational methodology that relies on 3D printing to automatically design and create tumour moulds that help to match images and tissue accurately without disrupting clinical practice.
Their work provides a robust and automated interface between imaging and tissue, enabling the development of clinical studies to probe tumor heterogeneity on multiple spatial scales. Understanding this heterogeneity may be key to understand why some tumours respond better to therapy than others.
