3D-printed moulds for image-guided surgical biopsies: an open source computational platform

Abstract

PURPOSESpatial heterogeneity of tumours is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood, as it relies on the accurate co-registration of medical images and tissue biopsies. tumour moulds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and di cult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumour heterogeneity data. METHODSWe have developed an open source computational framework to automatically produce patientspecific 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 clinical, imaging and pathology workflows. RESULTSWe applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalised moulds for five patients, obtaining Dice similarity coe cients between imaging and tissue sections ranging from 0.86 to 0.93 for tumour regions, and between 0.70 and 0.76 for healthy kidney. The framework required minimal manual intervention, producing the final mould design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes. CONCLUSIONOur work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumour heterogeneity on multiple spatial scales.

Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer

“…The mould-guided biopsy approach has recently gained popularity and has been used to investigate the association of radiomic features and histopathology phenotypes in different tumour types, such as prostate cancer [76][77][78][79], liver cancer [80] and renal cancer [81]. More recently, updates in the design of these moulds have been proposed to choose the preferred tissue sectioning angle, transforming the images and the corresponding maps [82]. This flexibility makes it easier for patient-specific moulds to be used in the clinical routine without disrupting the usual protocols.…”

“…Nevertheless, most of the methods presented have two clear limitations: (i) very specialised technologies difficult to integrate in the clinical workflow and (ii) only possible after tumour resection, making it impossible to create multiscale models of tumours that track these associations longitudinally. Some of the most recent 3D mould technologies, such as the one proposed in [82], are designed to address the issue of Fig. 2 Comparison between the different approaches used for radiogenomic studies.…”

K-means clustering of hyperpolarised13C-MRI identifies intratumoural perfusion/metabolism mismatch in renal cell carcinoma as best predictor of highest grade

“…Also, biopsy sampling was performed only on 2D slices of nephrectomised kidneys since whole-tumour tissue analyses are technically challenging and impossible in a clinical setting due to the need for diagnostic samples. Still, multiregional sampling with the 3D-printed tumour mould enabled good correspondence between imaging and pathology 22 , and by clustering of 13 C-data we mitigated the effects of any possible misregistration, therefore providing a larger confidence space for potential future biopsy targeting. In summary, we present a novel clustering method of HP 13 C-MRI data that predicts the most aggressive intratumoural ccRCC grades with high specificity, outperforming individual parameters and the clinical standard.…”

“…To enable co-registration of the biopsies to the MRI images, the patient-specific pathology sampling maps were produced from MRI biomarker maps and sliced with the assistance of a 3D printed tumour mould as described previously 22 . Four to fourteen multiregional samples were collected from viable tumour regions by avoiding visible cystic/necrotic areas, and each biopsy was split into half with one part being formalin-fixed paraffin-embedded (FFPE) and the other half flash-frozen.…”