Computational Biomedicine – 3D tumour mould

The technique is currently being developed in both Ovarian and Renal cancer.

Computational Biomedicine - 3D tumour mould as part of ICM

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study. Delgardo-Ortet, M., et al, Feb 2023.

Background: High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours.

Methods: In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process.

Results: Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments.

Conclusions: We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.

3D-printed moulds of renal tumours for image-guided tissue sampling in the clinical setting. Crispin-Ortuzar, M. et al, JCO Clinical Cancer Informatics, 2019.

Summary: To address intra-tumoural heterogeneity in renal clear cell carcinoma we have started to develop a novel method to link imaging (MRI) data to digital pathology data, by developing an image guided 3D-printed tumour mould. By tightly integrating our approach into the workflows of clinical trials, our methodology will enable the creation of large spatially-matched multiscale datasets including radiomics, genomics and histology data.

Radiogenomics Analysis of Intratumor Heterogeneity in a Patient With High-Grade Serous Ovarian Cancer. Weigelt, B, et al., JCO Precision Oncology, June 2019.

In this study we use lesion-specific three-dimensional (3D) molds for phenotypic image-guided tumor sampling to ensure spatial colocation of imaging, histology, and genomic data, critical for understanding tumor biology. Phenotypic imaging maps of heterogeneity (ie, imaging habitats) of two HGSOC sites were obtained by combining perfusion, diffusion, and metabolic maps derived from multiparametric imaging. We evaluated if this phenotypic imaging-based heterogeneity reflects the underlying histologic and/or genetic heterogeneity of the tumor.


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