Abstract
Spatial intratumoural heterogeneity is a major challenge in precision medicine. Progress to better understand the relationship between genetic heterogeneity and tissue heterogeneity depends on accurately co-registering imaging data and tissue samples. We address this challenge in patients with renal cell carcinoma undergoing radical nephrectomy and propose a computational approach to produce patient-specific 3D-printed moulds that can be used in the clinical setting. Our approach achieves accurate co-registration of sampling location between tissue and imaging, and integrates seamlessly with the clinical, imaging and pathology workflows. It also provides image guidance for tissue sampling while respecting pathologists’ preference for specific cutting planes, irrespective of the presence of perinephric fat. The methodology is tested on a patient undergoing radical nephrectomy, obtaining Dice similarity coefficients between imaging and tissue ranging from 0.75 to 0.92. Our work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to dissect tumour heterogeneity at multiple scales.
Author summary Cancer is a complex 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, we can obtain tissue samples that allow us to study its spatial composition. However, matching these data to the images that were obtained before surgery is challenging. We have developed a computational methodology that relies on 3D printing to create tumour moulds that help us match images and tissue accurately. In addition, unlike previous approaches, our technology does not disrupt clinical practice, so it can be used routinely.