Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Frontiers in Oncology

Oncology

Pubudu Pathiraja¹  Krishnayan Haldar¹  Helen Bolton¹  Mercedes Jimenez-Linan²  Marcel Gehrung³  James D. Brenton⁴  Marika A. V. Reinius⁴  Mireia Crispin-Ortuzar⁵  Andrew B. Gill⁶  Lorena Escudero Sanchez⁷  Maria Delgado-Ortet⁷  Cathal McCague⁸  Stephan Ursprung⁸  Vlad Bura⁹  Evis Sala^1,10  Ramona Woitek^1,11  Leonardo Rundo^1,12 

[1] Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom;Cancer Research UK Cambridge Centre, Cambridge, United Kingdom;Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom;Cancer Research UK Cambridge Centre, Cambridge, United Kingdom;Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom;Cancer Research UK Cambridge Centre, Cambridge, United Kingdom;Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom;Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom;Department of Oncology, University of Cambridge, Cambridge, United Kingdom;Cancer Research UK Cambridge Centre, Cambridge, United Kingdom;Department of Oncology, University of Cambridge, Cambridge, United Kingdom;Department of Radiology, University of Cambridge, Cambridge, United Kingdom;Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom;Department of Radiology, University of Cambridge, Cambridge, United Kingdom;Cancer Research UK Cambridge Centre, Cambridge, United Kingdom;Department of Radiology, University of Cambridge, Cambridge, United Kingdom;Cancer Research UK Cambridge Centre, Cambridge, United Kingdom;Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom;Department of Radiology, University of Cambridge, Cambridge, United Kingdom;Cancer Research UK Cambridge Centre, Cambridge, United Kingdom;Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom;Department of Radiology, Clinical Emergency Children’s Hospital, Cluj-Napoca, Romania;Department of Radiology, University of Cambridge, Cambridge, United Kingdom;Cancer Research UK Cambridge Centre, Cambridge, United Kingdom;Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom;Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy;Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy;Department of Radiology, University of Cambridge, Cambridge, United Kingdom;Cancer Research UK Cambridge Centre, Cambridge, United Kingdom;Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom;Research Center for Medical Image Analysis & Artificial Intelligence (MIAAI), Danube Private University, Krems, Austria;Department of Radiology, University of Cambridge, Cambridge, United Kingdom;Cancer Research UK Cambridge Centre, Cambridge, United Kingdom;Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, SA, Italy;

关键词: precision oncology;    ovarian cancer;    cancer imaging;    radiogenomics;    co-registration;    3D-printing;    custom tumour moulds;    tumour sampling;   

DOI  :  10.3389/fonc.2023.1085874

 received in 2022-10-31, accepted in 2023-01-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundHigh-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.MethodsIn 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.ResultsFive 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.ConclusionsWe 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.

【 授权许可】

Unknown   
Copyright © 2023 Delgado-Ortet, Reinius, McCague, Bura, Woitek, Rundo, Gill, Gehrung, Ursprung, Bolton, Haldar, Pathiraja, Brenton, Crispin-Ortuzar, Jimenez-Linan, Escudero Sanchez and Sala