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Gabiache G, Zadro C, Rozenblum L, Vezzosi D, Mouly C, Thoulouzan M, Guimbaud R, Otal P, Dierickx L, Rousseau H, Trepanier C, Dercle L, Mokrane FZ. Image-Guided Precision Medicine in the Diagnosis and Treatment of Pheochromocytomas and Paragangliomas. Cancers (Basel) 2023; 15:4666. [PMID: 37760633 PMCID: PMC10526298 DOI: 10.3390/cancers15184666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/11/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
In this comprehensive review, we aimed to discuss the current state-of-the-art medical imaging for pheochromocytomas and paragangliomas (PPGLs) diagnosis and treatment. Despite major medical improvements, PPGLs, as with other neuroendocrine tumors (NETs), leave clinicians facing several challenges; their inherent particularities and their diagnosis and treatment pose several challenges for clinicians due to their inherent complexity, and they require management by multidisciplinary teams. The conventional concepts of medical imaging are currently undergoing a paradigm shift, thanks to developments in radiomic and metabolic imaging. However, despite active research, clinical relevance of these new parameters remains unclear, and further multicentric studies are needed in order to validate and increase widespread use and integration in clinical routine. Use of AI in PPGLs may detect changes in tumor phenotype that precede classical medical imaging biomarkers, such as shape, texture, and size. Since PPGLs are rare, slow-growing, and heterogeneous, multicentric collaboration will be necessary to have enough data in order to develop new PPGL biomarkers. In this nonsystematic review, our aim is to present an exhaustive pedagogical tool based on real-world cases, dedicated to physicians dealing with PPGLs, augmented by perspectives of artificial intelligence and big data.
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Affiliation(s)
- Gildas Gabiache
- Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France (F.-Z.M.)
| | - Charline Zadro
- Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France (F.-Z.M.)
| | - Laura Rozenblum
- Department of Nuclear Medicine, Sorbonne Université, AP-HP, Hôpital La Pitié-Salpêtrière, 75013 Paris, France
| | - Delphine Vezzosi
- Department of Endocrinology, Rangueil University Hospital, 31400 Toulouse, France
| | - Céline Mouly
- Department of Endocrinology, Rangueil University Hospital, 31400 Toulouse, France
| | | | - Rosine Guimbaud
- Department of Oncology, Rangueil University Hospital, 31400 Toulouse, France
| | - Philippe Otal
- Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France (F.-Z.M.)
| | - Lawrence Dierickx
- Department of Nuclear Medicine, IUCT-Oncopole, 31059 Toulouse, France;
| | - Hervé Rousseau
- Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France (F.-Z.M.)
| | - Christopher Trepanier
- New York-Presbyterian Hospital/Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Laurent Dercle
- New York-Presbyterian Hospital/Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Fatima-Zohra Mokrane
- Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France (F.-Z.M.)
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Maclean D, Tsakok M, Gleeson F, Breen DJ, Goldin R, Primrose J, Harris A, Franklin J. Comprehensive Imaging Characterization of Colorectal Liver Metastases. Front Oncol 2021; 11:730854. [PMID: 34950575 PMCID: PMC8688250 DOI: 10.3389/fonc.2021.730854] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 11/15/2021] [Indexed: 12/21/2022] Open
Abstract
Colorectal liver metastases (CRLM) have heterogenous histopathological and immunohistochemical phenotypes, which are associated with variable responses to treatment and outcomes. However, this information is usually only available after resection, and therefore of limited value in treatment planning. Improved techniques for in vivo disease assessment, which can characterise the variable tumour biology, would support further personalization of management strategies. Advanced imaging of CRLM including multiparametric MRI and functional imaging techniques have the potential to provide clinically-actionable phenotypic characterisation. This includes assessment of the tumour-liver interface, internal tumour components and treatment response. Advanced analysis techniques, including radiomics and machine learning now have a growing role in assessment of imaging, providing high-dimensional imaging feature extraction which can be linked to clinical relevant tumour phenotypes, such as a the Consensus Molecular Subtypes (CMS). In this review, we outline how imaging techniques could reproducibly characterize the histopathological features of CRLM, with several matched imaging and histology examples to illustrate these features, and discuss the oncological relevance of these features. Finally, we discuss the future challenges and opportunities of CRLM imaging, with a focus on the potential value of advanced analytics including radiomics and artificial intelligence, to help inform future research in this rapidly moving field.
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Affiliation(s)
- Drew Maclean
- Department of Radiology, University Hospital Southampton, Southampton, United Kingdom.,Department of Medical Imaging, Bournemouth University, Bournemouth, United Kingdom
| | - Maria Tsakok
- Department of Radiology, Oxford University Hospitals, Oxford, United Kingdom
| | - Fergus Gleeson
- Department of Oncology, Oxford University, Oxford, United Kingdom
| | - David J Breen
- Department of Radiology, University Hospital Southampton, Southampton, United Kingdom
| | - Robert Goldin
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - John Primrose
- Department of Surgery, University Hospital Southampton, Southampton, United Kingdom.,Academic Unit of Cancer Sciences, University of Southampton, Southampton, United Kingdom
| | - Adrian Harris
- Department of Oncology, Oxford University, Oxford, United Kingdom
| | - James Franklin
- Department of Medical Imaging, Bournemouth University, Bournemouth, United Kingdom
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