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Delgado-Ortet M, Reinius MAV, McCague C, Bura V, Woitek R, Rundo L, Gill AB, Gehrung M, Ursprung S, Bolton H, Haldar K, Pathiraja P, Brenton JD, Crispin-Ortuzar M, Jimenez-Linan M, Escudero Sanchez L, Sala E. Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study. Front Oncol 2023; 13:1085874. [PMID: 36860310 PMCID: PMC9969130 DOI: 10.3389/fonc.2023.1085874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
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.
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Affiliation(s)
- Maria Delgado-Ortet
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
| | - Marika A. V. Reinius
- 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
| | - Cathal McCague
- 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
| | - Vlad Bura
- 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
| | - Ramona Woitek
- 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
| | - Leonardo Rundo
- 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
| | - Andrew B. Gill
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Marcel Gehrung
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Stephan Ursprung
- 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
| | - 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
- 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
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Mercedes Jimenez-Linan
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
| | - Evis Sala
- 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
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Correlation of in-vivo imaging with histopathology: A review. Eur J Radiol 2021; 144:109964. [PMID: 34619617 DOI: 10.1016/j.ejrad.2021.109964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/26/2021] [Accepted: 09/17/2021] [Indexed: 11/21/2022]
Abstract
Despite tremendous advancements in in vivo imaging modalities, there remains substantial uncertainty with respect to tumor delineation on in these images. Histopathology remains the gold standard for determining the extent of malignancy, with in vivo imaging to histopathologic correlation enabling spatial comparisons. In this review, the steps necessary for successful imaging to histopathologic correlation are described, including in vivo imaging, resection, fixation, specimen sectioning (sectioning technique, securing technique, orientation matching, slice matching), microtome sectioning and staining, correlation (including image registration) and performance evaluation. The techniques used for each of these steps are also discussed. Hundreds of publications from the past 20 years were surveyed, and 62 selected for detailed analysis. For these 62 publications, each stage of the correlative pathology process (and the sub-steps of specimen sectioning) are listed. A statistical analysis was conducted based on 19 studies that reported target registration error as their performance metric. While some methods promise greater accuracy, they may be expensive. Due to the complexity of the processes involved, correlative pathology studies generally include a small number of subjects, which hinders advanced developments in this field.
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Sakai K. [2. Radiomics of MRI]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:866-875. [PMID: 34421076 DOI: 10.6009/jjrt.2021_jsrt_77.8.866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Koji Sakai
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
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Francoisse CA, Sescleifer AM, King WT, Lin AY. Three-dimensional printing in medicine: a systematic review of pediatric applications. Pediatr Res 2021; 89:415-425. [PMID: 32503028 DOI: 10.1038/s41390-020-0991-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 05/01/2020] [Accepted: 05/04/2020] [Indexed: 01/17/2023]
Abstract
BACKGROUND Three-dimensional printing (3DP) addresses distinct clinical challenges in pediatric care including: congenital variants, compact anatomy, high procedural risk, and growth over time. We hypothesized that patient-specific applications of 3DP in pediatrics could be categorized into concise, discrete categories of use. METHODS Terms related to "three-dimensional printing" and "pediatrics" were searched on PubMed, Scopus, Ovid MEDLINE, Cochrane CENTRAL, and Web of Science. Initial search yielded 2122 unique articles; 139 articles characterizing 508 patients met full inclusion criteria. RESULTS Four categories of patient-specific 3DP applications were identified: Teaching of families and medical staff (9.3%); Developing intervention strategies (33.9%); Procedural applications, including subtypes: contour models, guides, splints, and implants (43.0%); and Material manufacturing of shaping devices or prosthetics (14.0%). Procedural comparative studies found 3DP devices to be equivalent or better than conventional methods, with less operating time and fewer complications. CONCLUSION Patient-specific applications of Three-Dimensional Printing in Medicine can be elegantly classified into four major categories: Teaching, Developing, Procedures, and Materials, sharing the same TDPM acronym. Understanding this schema is important because it promotes further innovation and increased implementation of these devices to improve pediatric care. IMPACT This article classifies the pediatric applications of patient-specific three-dimensional printing. This is a first comprehensive review of patient-specific three-dimensional printing in both pediatric medical and surgical disciplines, incorporating previously described classification schema to create one unifying paradigm. Understanding these applications is important since three-dimensional printing addresses challenges that are uniquely pediatric including compact anatomy, unique congenital variants, greater procedural risk, and growth over time. We identified four classifications of patient-specific use: teaching, developing, procedural, and material uses. By classifying these applications, this review promotes understanding and incorporation of this expanding technology to improve the pediatric care.
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Affiliation(s)
- Caitlin A Francoisse
- Division of Plastic Surgery, Saint Louis University School of Medicine, St. Louis, MO, USA
| | - Anne M Sescleifer
- Division of Plastic Surgery, Saint Louis University School of Medicine, St. Louis, MO, USA
| | - Wilson T King
- Division of Pediatric Cardiology, Saint Louis University School of Medicine, St. Louis, MO, USA.,SSM Health Cardinal Glennon Children's Hospital at SLU, St. Louis, MO, USA
| | - Alexander Y Lin
- Division of Plastic Surgery, Saint Louis University School of Medicine, St. Louis, MO, USA. .,SSM Health Cardinal Glennon Children's Hospital at SLU, St. Louis, MO, USA.
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Martin-Gonzalez P, Crispin-Ortuzar M, Rundo L, Delgado-Ortet M, Reinius M, Beer L, Woitek R, Ursprung S, Addley H, Brenton JD, Markowetz F, Sala E. Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer. Insights Imaging 2020; 11:94. [PMID: 32804260 PMCID: PMC7431480 DOI: 10.1186/s13244-020-00895-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/16/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Ovarian cancer survival rates have not changed in the last 20 years. The majority of cases are High-grade serous ovarian carcinomas (HGSOCs), which are typically diagnosed at an advanced stage with multiple metastatic lesions. Taking biopsies of all sites of disease is infeasible, which challenges the implementation of stratification tools based on molecular profiling. MAIN BODY In this review, we describe how these challenges might be overcome by integrating quantitative features extracted from medical imaging with the analysis of paired genomic profiles, a combined approach called radiogenomics, to generate virtual biopsies. Radiomic studies have been used to model different imaging phenotypes, and some radiomic signatures have been associated with paired molecular profiles to monitor spatiotemporal changes in the heterogeneity of tumours. We describe different strategies to integrate radiogenomic information in a global and local manner, the latter by targeted sampling of tumour habitats, defined as regions with distinct radiomic phenotypes. CONCLUSION Linking radiomics and biological correlates in a targeted manner could potentially improve the clinical management of ovarian cancer. Radiogenomic signatures could be used to monitor tumours during the course of therapy, offering additional information for clinical decision making. In summary, radiogenomics may pave the way to virtual biopsies and treatment monitoring tools for integrative tumour analysis.
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Affiliation(s)
- Paula Martin-Gonzalez
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Leonardo Rundo
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Maria Delgado-Ortet
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Marika Reinius
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Lucian Beer
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
| | - Ramona Woitek
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
| | - Stephan Ursprung
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Helen Addley
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - James D Brenton
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Evis Sala
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK.
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK.
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7
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Ballard DH, Wake N, Witowski J, Rybicki FJ, Sheikh A. Radiological Society of North America (RSNA) 3D Printing Special Interest Group (SIG) clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: abdominal, hepatobiliary, and gastrointestinal conditions. 3D Print Med 2020; 6:13. [PMID: 32514795 PMCID: PMC7278118 DOI: 10.1186/s41205-020-00065-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 04/23/2020] [Indexed: 02/06/2023] Open
Abstract
Background Medical 3D printing has demonstrated value in anatomic models for abdominal, hepatobiliary, and gastrointestinal conditions. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides appropriateness criteria for abdominal, hepatobiliary, and gastrointestinal 3D printing indications. Methods A literature search was conducted to identify all relevant articles using 3D printing technology associated with a number of abdominal pathologic processes. Each included study was graded according to published guidelines. Results Evidence-based appropriateness guidelines are provided for the following areas: intra-hepatic masses, hilar cholangiocarcinoma, biliary stenosis, biliary stones, gallbladder pathology, pancreatic cancer, pancreatitis, splenic disease, gastric pathology, small bowel pathology, colorectal cancer, perianal fistula, visceral trauma, hernia, abdominal sarcoma, abdominal wall masses, and intra-abdominal fluid collections. Conclusion This document provides initial appropriate use criteria for medical 3D printing in abdominal, hepatobiliary, and gastrointestinal conditions.
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Affiliation(s)
- David H Ballard
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, Campus Box 8131, St. Louis, MO, 63110, USA.
| | - Nicole Wake
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jan Witowski
- 2nd Department of General Surgery, Jagiellonian University Medical College, Kopernika 21, 31-501, Krakow, Poland
| | - Frank J Rybicki
- Department of Radiology, University of Cincinnati Medical Center, Cincinnati, OH, USA
| | - Adnan Sheikh
- Department of Radiology and The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
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Liver-specific 3D sectioning molds for correlating in vivo CT and MRI with tumor histopathology in woodchucks (Marmota monax). PLoS One 2020; 15:e0230794. [PMID: 32214365 PMCID: PMC7098627 DOI: 10.1371/journal.pone.0230794] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 03/08/2020] [Indexed: 12/19/2022] Open
Abstract
Purpose To evaluate the spatial registration and correlation of liver and tumor histopathology sections with corresponding in vivo CT and MRI using 3D, liver-specific cutting molds in a woodchuck (Marmota monax) hepatic tumor model. Methods Five woodchucks chronically infected with woodchuck hepatitis virus following inoculation at birth and with confirmed hepatic tumors were imaged by contrast enhanced CT or MRI. Virtual 3D liver or tumor models were generated by segmentation of in vivo CT or MR imaging. A specimen-specific cavity was created inside a block containing cutting slots aligned with an imaging plane using computer-aided design software, and the final cutting molds were fabricated using a 3D printer. Livers were resected two days after initial imaging, fixed with formalin or left unfixed, inserted into the 3D molds, and cut into parallel pieces by passing a sharp blade through the parallel slots in the mold. Histopathology sections were acquired and their spatial overlap with in vivo image slices was quantified using the Dice similarity coefficient (DSC). Results Imaging of the woodchucks revealed heterogeneous hepatic tumors of varying size, number, and location. Specimen-specific 3D molds provided accurate co-localization of histopathology of whole livers, liver lobes, and pedunculated tumors with in vivo CT and MR imaging, with or without tissue fixation. Visual inspection of histopathology sections and corresponding in vivo image slices revealed spatial registration of analogous pathologic features. The mean DSC for all specimens was 0.83+/-0.05. Conclusion Use of specimen-specific 3D molds for en bloc liver dissection provided strong spatial overlap and feature correspondence between in vivo image slices and histopathology sections.
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Personalized 3D-Printed Transparent Liver Model Using the Hepatobiliary Phase MRI: Usefulness in the Lesion-by-Lesion Imaging-Pathologic Matching of Focal Liver Lesions-Preliminary Results. Invest Radiol 2019; 54:138-145. [PMID: 30379728 DOI: 10.1097/rli.0000000000000521] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE The aim of this study was to investigate the usefulness of a personalized, 3-dimensional (3D)-printed, transparent liver model with focal liver lesions (FLLs) for lesion-by-lesion imaging-pathologic matching. MATERIALS AND METHODS This preliminary, prospective study was approved by our institutional review board, and written informed consent was obtained. Twenty patients (male-to-female ratio, 13:7; mean age, 56 years) with multiple FLLs, including at least one presumed malignant, or an indeterminate lesion 10 mm or less on the preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI), were included. After digital segmentation of hepatobiliary phase MRI, a transparent, 3D-printed liver model with colored anatomical structures and FLLs was produced. During the gross examination of the liver specimen, the per-lesion detection rates were compared between those without (routine protocol) and those with the aid of the 3D-printed liver model. RESULTS Among 98 MRI-detected FLLs (11.5 ± 12.5 mm), the per-lesion detection rate on gross examination using the 3D-printed liver model was 99.0% (97/98), which was significantly higher than that obtained on routine examination (82.7% [81/98]; P < 0.001). In the subgroup analysis, according to the tumor size, 23.9% (16/67) of FLLs 10 mm or less were additionally detected using the liver model, whereas none were additionally detected in greater than 10 mm. The additionally detected 16 FLLs in 12 patients included histologic diagnoses of viable metastases, pathologic complete response of metastases, hepatocellular carcinomas, focal nodular hyperplasia-like nodules, and hemangiomas. CONCLUSIONS A personalized, 3D-printed liver model with FLLs may improve the lesion-by-lesion imaging-pathologic matching for small FLLs, thus leading to accurate pathologic tumor staging and obtaining a reliable reference for imaging-detected FLLs.
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Bangeas P, Tsioukas V, Papadopoulos VN, Tsoulfas G. Role of innovative 3D printing models in the management of hepatobiliary malignancies. World J Hepatol 2019; 11:574-585. [PMID: 31388399 PMCID: PMC6669192 DOI: 10.4254/wjh.v11.i7.574] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 06/12/2019] [Accepted: 06/27/2019] [Indexed: 02/06/2023] Open
Abstract
Three-dimensional (3D) printing has recently emerged as a new technique in various liver-related surgical fields. There are currently only a few systematic reviews that summarize the evidence of its impact. In order to construct a systematic literature review of the applications and effects of 3D printing in liver surgery, we searched the PubMed, Embase and ScienceDirect databases for relevant titles, according to the PRISMA statement guidelines. We retrieved 162 titles, of which 32 met the inclusion criteria and are reported. The leading application of 3D printing in liver surgery is for preoperative planning. 3D printing techniques seem to be beneficial for preoperative planning and educational tools, despite their cost and time requirements, but this conclusion must be confirmed by additional randomized controlled trials.
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Affiliation(s)
- Peter Bangeas
- Department of Surgery, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Vassilios Tsioukas
- Department of School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | | | - Georgios Tsoulfas
- Department of Surgery, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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MR Imaging-Histology Correlation by Tailored 3D-Printed Slicer in Oncological Assessment. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:1071453. [PMID: 31275082 PMCID: PMC6560325 DOI: 10.1155/2019/1071453] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 05/12/2019] [Indexed: 12/14/2022]
Abstract
3D printing and reverse engineering are innovative technologies that are revolutionizing scientific research in the health sciences and related clinical practice. Such technologies are able to improve the development of various custom-made medical devices while also lowering design and production costs. Recent advances allow the printing of particularly complex prototypes whose geometry is drawn from precise computer models designed on in vivo imaging data. This review summarizes a new method for histological sample processing (applicable to e.g., the brain, prostate, liver, and renal mass) which employs a personalized mold developed from diagnostic images through computer-aided design software and 3D printing. Through positioning the custom mold in a coherent manner with respect to the organ of interest (as delineated by in vivo imaging data), the cutting instrument can be precisely guided in order to obtain blocks of tissue which correspond with high accuracy to the slices imaged. This approach appeared crucial for validation of new quantitative imaging tools, for an accurate imaging-histopathological correlation and for the assessment of radiogenomic features extracted from oncological lesions. The aim of this review is to define and describe 3D printing technologies which are applicable to oncological assessment and slicer design, highlighting the radiological and pathological perspective as well as recent applications of this approach for the histological validation of and correlation with MR images.
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Wang JZ, Xiong NY, Zhao LZ, Hu JT, Kong DC, Yuan JY. Review fantastic medical implications of 3D-printing in liver surgeries, liver regeneration, liver transplantation and drug hepatotoxicity testing: A review. Int J Surg 2018; 56:1-6. [PMID: 29886280 DOI: 10.1016/j.ijsu.2018.06.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 06/05/2018] [Indexed: 02/07/2023]
Abstract
The epidemiological trend in liver diseases becomes more serious worldwide. Several recent articles published by International Journal of Surgery in 2018 particularly emphasized the encouraging clinical benefits of hepatectomy, liver regeneration and liver transplantation, however, there are still many technical bottlenecks underlying these therapeutic approaches. Remarkably, a few preliminary studies have shown some clues to the role of three-dimensional (3D) printing in improving traditional therapy for liver diseases. Here, we concisely elucidated the curative applications of 3D-printing (no cells) and 3D Bio-printing (with hepatic cells), such as 3D-printed patient-specific liver models and devices for medical education, surgical simulation, hepatectomy and liver transplantation, 3D Bio-printed hepatic constructs for liver regeneration and artificial liver, 3D-printed liver tissues for evaluating drug's hepatotoxicity, and so on. Briefly, 3D-printed liver models and bioactive tissues may facilitate a lot of key steps to cure liver disorders, predictably bringing promising clinical benefits. This work further provides novel insights into facilitating treatment of hepatic carcinoma, promoting liver regeneration both in vivo and in vitro, expanding transplantable liver resources, maximizing therapeutic efficacy as well as minimizing surgical complications, medical hepatotoxicity, operational time, economic costs, etc.
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Affiliation(s)
- Jing-Zhang Wang
- Department of Medical Technology, College of Medicine, Affiliated Hospital, Hebei University of Engineering, Handan, 056002, PR China.
| | - Nan-Yan Xiong
- College of Medicine, Hebei University of Engineering, Handan, 056002, PR China
| | - Li-Zhen Zhao
- Department of Clinical Laboratory, Affiliated Hospital of Hebei University of Engineering, Handan, 056002, PR China
| | - Jin-Tian Hu
- Department of Clinical Laboratory, Affiliated Hospital of Hebei University of Engineering, Handan, 056002, PR China
| | - De-Cheng Kong
- College of Medicine, Hebei University of Engineering, Handan, 056002, PR China
| | - Jiang-Yong Yuan
- Department of Cardiology, Affiliated Hospital of Hebei University of Engineering, Handan, 056002, PR China.
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