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

DOI
https://doi.org/10.3389/fonc.2023.1085874
Journal volume & issue
Vol. 13

Abstract

Read online

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