Frontiers in Oncology (Feb 2023)
Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study
- Maria Delgado-Ortet,
- Maria Delgado-Ortet,
- Marika A. V. Reinius,
- Marika A. V. Reinius,
- Marika A. V. Reinius,
- Marika A. V. Reinius,
- Cathal McCague,
- Cathal McCague,
- Cathal McCague,
- Vlad Bura,
- Vlad Bura,
- Vlad Bura,
- Vlad Bura,
- Ramona Woitek,
- Ramona Woitek,
- Ramona Woitek,
- Ramona Woitek,
- Leonardo Rundo,
- Leonardo Rundo,
- Leonardo Rundo,
- Andrew B. Gill,
- Andrew B. Gill,
- Marcel Gehrung,
- Marcel Gehrung,
- Stephan Ursprung,
- Stephan Ursprung,
- Stephan Ursprung,
- Helen Bolton,
- Krishnayan Haldar,
- Pubudu Pathiraja,
- James D. Brenton,
- James D. Brenton,
- James D. Brenton,
- James D. Brenton,
- Mireia Crispin-Ortuzar,
- Mireia Crispin-Ortuzar,
- Mercedes Jimenez-Linan,
- Mercedes Jimenez-Linan,
- Lorena Escudero Sanchez,
- Lorena Escudero Sanchez,
- Evis Sala,
- Evis Sala,
- Evis Sala,
- Evis Sala,
- Evis Sala
Affiliations
- Maria Delgado-Ortet
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Maria Delgado-Ortet
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Marika A. V. Reinius
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Marika A. V. Reinius
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Marika A. V. Reinius
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Marika A. V. Reinius
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
- Cathal McCague
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cathal McCague
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cathal McCague
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Vlad Bura
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Vlad Bura
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Vlad Bura
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Vlad Bura
- Department of Radiology, Clinical Emergency Children’s Hospital, Cluj-Napoca, Romania
- Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Ramona Woitek
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Ramona Woitek
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Ramona Woitek
- Research Center for Medical Image Analysis & Artificial Intelligence (MIAAI), Danube Private University, Krems, Austria
- Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Leonardo Rundo
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, SA, Italy
- Andrew B. Gill
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Andrew B. Gill
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Marcel Gehrung
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Marcel Gehrung
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Stephan Ursprung
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Stephan Ursprung
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Helen Bolton
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Krishnayan Haldar
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Pubudu Pathiraja
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- James D. Brenton
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- James D. Brenton
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- James D. Brenton
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- James D. Brenton
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
- Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Mireia Crispin-Ortuzar
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
- Mercedes Jimenez-Linan
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Mercedes Jimenez-Linan
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Lorena Escudero Sanchez
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Evis Sala
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Evis Sala
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Evis Sala
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Evis Sala
- 0Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
- DOI
- https://doi.org/10.3389/fonc.2023.1085874
- Journal volume & issue
-
Vol. 13
Abstract
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.
Keywords