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Schmitz F, Sedaghat S. Inferring malignancy grade of soft tissue sarcomas from magnetic resonance imaging features: A systematic review. Eur J Radiol 2024; 177:111548. [PMID: 38852328 DOI: 10.1016/j.ejrad.2024.111548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/22/2024] [Accepted: 06/02/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE Systematic reviews on the grading of STS using MRI are lacking. This review analyses the role of different MRI features in inferring the histological grade of STS. MATERIALS AND METHODS A systematic review was conducted and is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) checklist. The electronic databases of PubMed/MEDLINE were systematically searched for literature addressing the correlation of MRI findings in soft tissue sarcoma with tumor grade. As keywords "MRI", "magnetic resonance imaging", "sarcoma", "grade", "grading", and "FNCLCC" have been selected. RESULTS 14 studies have been included in this systematic review. Tumor size (p = 0.015 (51 patients) to p = 0.81 (36 patients)), tumor margin (p < 0.001 (95 patients) to 0.93 (36 patients)), necrosis (p = 0.004 (50 patients) to p = 0.65 (95 patients)), peritumoral edema (p = 0.002 (130 patients) to p = 0.337 (40 patients)), contrast enhancement (p < 0.01 (50 patients) to 0.019 (51 patients)) and polycyclic/multilobulated tumor configuration (p = 0.008 (71 patients)) were significantly associated with STS malignancy grade in most of the included studies. Heterogeneity in T2w images (p = 0.003 (130 patients) to 0.202 (40 patients)), signal intensity in T1w images/ hemorrhage (p = 0.02 (130 patients) to 0.5 (31 patients)), peritumoral contrast enhancement (p < 0.001 (95 patients) to 0.253 (51 patients)) and tumoral diffusion restriction (p = 0.01 (51 patients) to 0.53 (52 patients)) were regarded as significantly associated with FNCLCC grade in some of the studies which investigated these features. Most other MRI features were not significant. CONCLUSION Several MRI features, such as tumor size, necrosis, peritumoral edema, peritumoral contrast enhancement, intratumoral contrast enhancement, and polycyclic/multilobulated tumor configuration may indicate the malignancy grade of STS. However, further studies are needed to gain consensus.
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Affiliation(s)
- Fabian Schmitz
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
| | - Sam Sedaghat
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany.
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2
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Glielmo P, Fusco S, Gitto S, Zantonelli G, Albano D, Messina C, Sconfienza LM, Mauri G. Artificial intelligence in interventional radiology: state of the art. Eur Radiol Exp 2024; 8:62. [PMID: 38693468 PMCID: PMC11063019 DOI: 10.1186/s41747-024-00452-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/26/2024] [Indexed: 05/03/2024] Open
Abstract
Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.
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Affiliation(s)
- Pierluigi Glielmo
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy.
| | - Stefano Fusco
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
| | - Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Giulia Zantonelli
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Via della Commenda, 10, 20122, Milan, Italy
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Giovanni Mauri
- Divisione di Radiologia Interventistica, IEO, IRCCS Istituto Europeo di Oncologia, Milan, Italy
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3
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Spinnato P, Bianchi G. Beyond the AJR: CT-Based Virtual Biopsy in Retroperitoneal Soft-Tissue Sarcomas. AJR Am J Roentgenol 2024. [PMID: 38415577 DOI: 10.2214/ajr.24.30965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Affiliation(s)
- Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Giuseppe Bianchi
- Department of Orthopaedic Oncology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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Brandenberger D, White LM. Radiomics in Musculoskeletal Tumors. Semin Musculoskelet Radiol 2024; 28:49-61. [PMID: 38330970 DOI: 10.1055/s-0043-1776428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal (MSK) system. Due to significant variations in their natural history and variable response to conventional treatments, the discovery of novel diagnostic and prognostic biomarkers to guide therapeutic decision-making is an active and ongoing field of research. As new cellular, molecular, and metabolic biomarkers continue to be discovered, quantitative radiologic imaging is becoming increasingly important in sarcoma management. Radiomics offers the potential for discovering novel imaging diagnostic and predictive biomarkers using standard-of-care medical imaging. In this review, we detail the core concepts of radiomics and the application of radiomics to date in MSK sarcoma research. Also described are specific challenges related to radiomic studies, as well as viewpoints on clinical adoption and future perspectives in the field.
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Affiliation(s)
- Daniel Brandenberger
- Department of Medical Imaging, Musculoskeletal Imaging, University of Toronto, Toronto, Ontario, Canada
- Institut für Radiologie und Nuklearmedizin, Kantonsspital Baselland, Liestal, Switzerland
- Toronto Joint Department of Medical Imaging, University Health Network, Sinai Health System, and Women's College Hospital, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Lawrence M White
- Department of Medical Imaging, Musculoskeletal Imaging, University of Toronto, Toronto, Ontario, Canada
- Toronto Joint Department of Medical Imaging, University Health Network, Sinai Health System, and Women's College Hospital, Mount Sinai Hospital, Toronto, Ontario, Canada
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5
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Cellina M, De Padova G, Caldarelli N, Libri D, Cè M, Martinenghi C, Alì M, Papa S, Carrafiello G. Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy. Crit Rev Oncog 2024; 29:1-13. [PMID: 38505877 DOI: 10.1615/critrevoncog.2023050439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and virtual biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
| | - Giuseppe De Padova
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Nazarena Caldarelli
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Dario Libri
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Carlo Martinenghi
- Radiology Department, Ospedale San Raffaele, Via Olgettina, 60 - 20132 Milan, Italy
| | - Marco Alì
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Sergio Papa
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
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Walker K, Simister SK, Carr-Ascher J, Monument MJ, Thorpe SW, Randall RL. Emerging innovations and advancements in the treatment of extremity and truncal soft tissue sarcomas. J Surg Oncol 2024; 129:97-111. [PMID: 38010997 DOI: 10.1002/jso.27526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 11/29/2023]
Abstract
In this special edition update on soft tissue sarcomas (STS), we cover classifications, emerging technologies, prognostic tools, radiation schemas, and treatment disparities in extremity and truncal STS. We discuss the importance of enhancing local control and reducing complications, including the role of innovative imaging, surgical guidance, and hypofractionated radiation. We review advancements in systemic and immunotherapeutic treatments and introduce disparities seen in this vulnerable population that must be considered to improve overall patient care.
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Affiliation(s)
- Kyle Walker
- Department of Orthopaedics, University of California, Davis, Sacramento, California, USA
| | - Samuel K Simister
- Department of Orthopaedics, University of California, Davis, Sacramento, California, USA
| | - Janai Carr-Ascher
- Department of Hematology and Oncology, University of California, Davis, Sacramento, California, USA
| | - Michael J Monument
- Department of Surgery, The University of Calgary, Calgary, Alberta, Canada
| | - Steven W Thorpe
- Department of Orthopaedics, University of California, Davis, Sacramento, California, USA
| | - R Lor Randall
- Department of Orthopaedics, University of California, Davis, Sacramento, California, USA
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7
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Arthur A, Orton MR, Emsley R, Vit S, Kelly-Morland C, Strauss D, Lunn J, Doran S, Lmalem H, Nzokirantevye A, Litiere S, Bonvalot S, Haas R, Gronchi A, Van Gestel D, Ducassou A, Raut CP, Meeus P, Spalek M, Hatton M, Le Pechoux C, Thway K, Fisher C, Jones R, Huang PH, Messiou C. A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis. Lancet Oncol 2023; 24:1277-1286. [PMID: 37922931 PMCID: PMC10618402 DOI: 10.1016/s1470-2045(23)00462-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Retroperitoneal sarcomas are tumours with a poor prognosis. Upfront characterisation of the tumour is difficult, and under-grading is common. Radiomics has the potential to non-invasively characterise the so-called radiological phenotype of tumours. We aimed to develop and independently validate a CT-based radiomics classification model for the prediction of histological type and grade in retroperitoneal leiomyosarcoma and liposarcoma. METHODS A retrospective discovery cohort was collated at our centre (Royal Marsden Hospital, London, UK) and an independent validation cohort comprising patients recruited in the phase 3 STRASS study of neoadjuvant radiotherapy in retroperitoneal sarcoma. Patients aged older than 18 years with confirmed primary leiomyosarcoma or liposarcoma proceeding to surgical resection with available contrast-enhanced CT scans were included. Using the discovery dataset, a CT-based radiomics workflow was developed, including manual delineation, sub-segmentation, feature extraction, and predictive model building. Separate probabilistic classifiers for the prediction of histological type and low versus intermediate or high grade tumour types were built and tested. Independent validation was then performed. The primary objective of the study was to develop radiomic classification models for the prediction of retroperitoneal leiomyosarcoma and liposarcoma type and histological grade. FINDINGS 170 patients recruited between Oct 30, 2016, and Dec 23, 2020, were eligible in the discovery cohort and 89 patients recruited between Jan 18, 2012, and April 10, 2017, were eligible in the validation cohort. In the discovery cohort, the median age was 63 years (range 27-89), with 83 (49%) female and 87 (51%) male patients. In the validation cohort, median age was 59 years (range 33-77), with 46 (52%) female and 43 (48%) male patients. The highest performing model for the prediction of histological type had an area under the receiver operator curve (AUROC) of 0·928 on validation, based on a feature set of radiomics and approximate radiomic volume fraction. The highest performing model for the prediction of histological grade had an AUROC of 0·882 on validation, based on a radiomics feature set. INTERPRETATION Our validated radiomics model can predict the histological type and grade of retroperitoneal sarcomas with excellent performance. This could have important implications for improving diagnosis and risk stratification in retroperitoneal sarcomas. FUNDING Wellcome Trust, European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group, the National Institutes for Health, and the National Institute for Health and Care Research Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research.
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Affiliation(s)
| | | | | | - Sharon Vit
- The Institute of Cancer Research, London, UK
| | | | - Dirk Strauss
- The Royal Marsden NHS Foundation Trust, London, UK
| | - Jason Lunn
- The Institute of Cancer Research, London, UK
| | - Simon Doran
- The Institute of Cancer Research, London, UK
| | - Hafida Lmalem
- The European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - Axelle Nzokirantevye
- The European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - Saskia Litiere
- The European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | | | - Rick Haas
- The Netherlands Cancer Institute (Antoni Van Leeuwenhoekziekenhuis), Amsterdam, Netherlands
| | - Alessandro Gronchi
- Department of Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Dirk Van Gestel
- Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Anne Ducassou
- Centre Hospitalier Universitaire de Toulouse, Toulouse, France; Institut Claudius Regaud, Toulouse, France; Institut Universitaire du Cancer de Toulouse Oncopole, Toulouse, France
| | - Chandrajit P Raut
- Brigham and Women's Hospital, Boston, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Mateusz Spalek
- Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Matthew Hatton
- Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | | | - Khin Thway
- The Institute of Cancer Research, London, UK; The Royal Marsden NHS Foundation Trust, London, UK
| | - Cyril Fisher
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Robin Jones
- The Institute of Cancer Research, London, UK; The Royal Marsden NHS Foundation Trust, London, UK
| | | | - Christina Messiou
- The Institute of Cancer Research, London, UK; The Royal Marsden NHS Foundation Trust, London, UK.
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8
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Johnston EW, Fotiadis N, Cummings C, Basso J, Tyne T, Lameijer J, Messiou C, Koh DM, Winfield JM. Developing and testing a robotic MRI/CT fusion biopsy technique using a purpose-built interventional phantom. Eur Radiol Exp 2022; 6:55. [PMID: 36411379 PMCID: PMC9679095 DOI: 10.1186/s41747-022-00308-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/28/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) can be used to target tumour components in biopsy procedures, while the ability to precisely correlate histology and MRI signal is crucial for imaging biomarker validation. Robotic MRI/computed tomography (CT) fusion biopsy offers the potential for this without in-gantry biopsy, although requires development. METHODS Test-retest T1 and T2 relaxation times, attenuation (Hounsfield units, HU), and biopsy core quality were prospectively assessed (January-December 2021) in a range of gelatin, agar, and mixed gelatin/agar solutions of differing concentrations on days 1 and 8 after manufacture. Suitable materials were chosen, and four biopsy phantoms were constructed with twelve spherical 1-3-cm diameter targets visible on MRI, but not on CT. A technical pipeline was developed, and intraoperator and interoperator reliability was tested in four operators performing a total of 96 biopsies. Statistical analysis included T1, T2, and HU repeatability using Bland-Altman analysis, Dice similarity coefficient (DSC), and intraoperator and interoperator reliability. RESULTS T1, T2, and HU repeatability had 95% limits-of-agreement of 8.3%, 3.4%, and 17.9%, respectively. The phantom was highly reproducible, with DSC of 0.93 versus 0.92 for scanning the same or two different phantoms, respectively. Hit rate was 100% (96/96 targets), and all operators performed robotic biopsies using a single volumetric acquisition. The fastest procedure time was 32 min for all 12 targets. CONCLUSIONS A reproducible biopsy phantom was developed, validated, and used to test robotic MRI/CT-fusion biopsy. The technique was highly accurate, reliable, and achievable in clinically acceptable timescales meaning it is suitable for clinical application.
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Affiliation(s)
- Edward W. Johnston
- grid.424926.f0000 0004 0417 0461Royal Marsden Hospital, 203 Fulham Road, London, SW3 6JJ UK ,grid.18886.3fInstitute of Cancer Research, 123 Old Brompton Road, London, SW73RP UK
| | - Nicos Fotiadis
- grid.424926.f0000 0004 0417 0461Royal Marsden Hospital, 203 Fulham Road, London, SW3 6JJ UK ,grid.18886.3fInstitute of Cancer Research, 123 Old Brompton Road, London, SW73RP UK
| | - Craig Cummings
- grid.18886.3fInstitute of Cancer Research, 123 Old Brompton Road, London, SW73RP UK
| | - Jodie Basso
- grid.424926.f0000 0004 0417 0461Royal Marsden Hospital, 203 Fulham Road, London, SW3 6JJ UK
| | - Toby Tyne
- grid.18886.3fInstitute of Cancer Research, 123 Old Brompton Road, London, SW73RP UK
| | - Joost Lameijer
- grid.424926.f0000 0004 0417 0461Royal Marsden Hospital, 203 Fulham Road, London, SW3 6JJ UK
| | - Christina Messiou
- grid.424926.f0000 0004 0417 0461Royal Marsden Hospital, 203 Fulham Road, London, SW3 6JJ UK ,grid.18886.3fInstitute of Cancer Research, 123 Old Brompton Road, London, SW73RP UK
| | - Dow-Mu Koh
- grid.424926.f0000 0004 0417 0461Royal Marsden Hospital, 203 Fulham Road, London, SW3 6JJ UK ,grid.18886.3fInstitute of Cancer Research, 123 Old Brompton Road, London, SW73RP UK
| | - Jessica M. Winfield
- grid.424926.f0000 0004 0417 0461Royal Marsden Hospital, 203 Fulham Road, London, SW3 6JJ UK ,grid.18886.3fInstitute of Cancer Research, 123 Old Brompton Road, London, SW73RP UK
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Popova E, Tkachev S, Reshetov I, Timashev P, Ulasov I. Imaging Hallmarks of Sarcoma Progression Via X-ray Computed Tomography: Beholding the Flower of Evil. Cancers (Basel) 2022; 14:cancers14205112. [PMID: 36291896 PMCID: PMC9600487 DOI: 10.3390/cancers14205112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/13/2022] [Accepted: 10/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Sarcomas represent the largest group of rare solid tumors that arise from mesenchymal stem cells and are a leading cause of cancer death in individuals younger than 20 years of age. There is an immediate need for the development of an algorithm for the early accurate diagnosis of sarcomas due to the high rate of diagnostic inaccuracy, which reaches up to 30%. X-ray computed tomography is a non-invasive imaging technique used to obtain detailed internal images of the human or animal body in clinical practice and preclinical studies. We summarized the main imaging features of soft tissue and bone sarcomas, and noted the development of new molecular markers to reach tumor type-specific imaging. Also, we demonstrated the possibility of the use X-ray computed microtomography for non-destructive 3D visualization of sarcoma progression in preclinical studies. Finding correlations between X-ray computed tomography modalities and the results of the histopathological specimen examination may significantly increase the accuracy of diagnostics, which leads to the initiation of appropriate management in a timely manner and, consequently, to improved outcomes. Abstract Sarcomas are a leading cause of cancer death in individuals younger than 20 years of age and represent the largest group of rare solid tumors. To date, more than 100 morphological subtypes of sarcomas have been described, among which epidemiology, clinical features, management, and prognosis differ significantly. Delays and errors in the diagnosis of sarcomas limit the number of effective therapeutic modalities and catastrophically worsen the prognosis. Therefore, the development of an algorithm for the early accurate diagnosis of sarcomas seems to be as important as the development of novel therapeutic advances. This literature review aims to summarize the results of recent investigations regarding the imaging of sarcoma progression based on the use of X-ray computed tomography (CT) in preclinical studies and in current clinical practice through the lens of cancer hallmarks. We attempted to summarize the main CT imaging features of soft-tissue and bone sarcomas. We noted the development of new molecular markers with high specificity to antibodies and chemokines, which are expressed in particular sarcoma subtypes to reach tumor type-specific imaging. We demonstrate the possibility of the use of X-ray computed microtomography (micro-CT) for non-destructive 3D visualization of solid tumors by increasing the visibility of soft tissues with X-ray scattering agents. Based on the results of recent studies, we hypothesize that micro-CT enables the visualization of neovascularization and stroma formation in sarcomas at high-resolution in vivo and ex vivo, including the novel techniques of whole-block and whole-tissue imaging. Finding correlations between CT, PET/CT, and micro-CT imaging features, the results of the histopathological specimen examination and clinical outcomes may significantly increase the accuracy of soft-tissue and bone tumor diagnostics, which leads to the initiation of appropriate histotype-specific management in a timely manner and, consequently, to improved outcomes.
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Affiliation(s)
- Elena Popova
- World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
| | - Sergey Tkachev
- World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
| | - Igor Reshetov
- University Clinical Hospital No. 1, I. M. Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
| | - Peter Timashev
- World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
| | - Ilya Ulasov
- Group of Experimental Biotherapy and Diagnostic, Institute for Regenerative Medicine, World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
- Correspondence: ; Tel.: +7-901-797-5406
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