Image-Guided Precision Medicine in the Diagnosis and Treatment of Pheochromocytomas and Paragangliomas
by
, , and
Gildas Gabiache
1,*,
Charline Zadro
1,
Laura Rozenblum
2,
Delphine Vezzosi
3,
Céline Mouly
3,
Matthieu Thoulouzan
4,
Rosine Guimbaud
5,
Philippe Otal
1,
Lawrence Dierickx
6,
Hervé Rousseau
1,
Christopher Trepanier
7,
Laurent Dercle
7
and
Fatima-Zohra Mokrane
1 1
Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France
2
Department of Nuclear Medicine, Sorbonne Université, AP-HP, Hôpital La Pitié-Salpêtrière, 75013 Paris, France
3
Department of Endocrinology, Rangueil University Hospital, 31400 Toulouse, France
4
Department of Surgery, Rangueil University Hospital, 31400 Toulouse, France
5
Department of Oncology, Rangueil University Hospital, 31400 Toulouse, France
6
Department of Nuclear Medicine, IUCT-Oncopole, 31059 Toulouse, France
7
New York-Presbyterian Hospital/Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
*
Author to whom correspondence should be addressed.
Cancers 2023, 15(18), 4666; https://doi.org/10.3390/cancers15184666
Submission received: 15 June 2023
/
Revised: 11 September 2023
/
Accepted: 18 September 2023
/
Published: 21 September 2023
(This article belongs to the Special Issue Cancer: Updates on Imaging and Machine Learning)
Simple Summary
Pheochromocytomas and paragangliomas are rare neural-crest-derived tumors with variable prognosis. In an era of tailored treatment strategies, medical imaging plays a crucial role in the surgeries, image-guided procedures, chemotherapies, immunotherapies, and radionuclide therapies involved in the diagnosis and treatment of these tumors. Medical imaging helps to confirm the diagnosis, guide surgical resection, assess metastatic staging, and select patients for specific therapies. In this step-by-step review, we perform a comprehensive analysis of recent imaging modalities developed for pheochromocytomas and paragangliomas by covering their content and terminology as well as discussing the future implications of artificial intelligence.
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.
