Radiomics and Artificial Intelligence Landscape for [ 18 F]FDG PET/CT in Multiple Myeloma.

Autor: Sachpekidis C; Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: christos_saxpe@yahoo.gr., Goldschmidt H; Internal Medicine V, Hematology, Oncology and Rheumatology, German-Speaking Myeloma Multicenter Group (GMMG), Heidelberg University Hospital, Heidelberg, Germany., Edenbrandt L; Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden., Dimitrakopoulou-Strauss A; Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Jazyk: angličtina
Zdroj: Seminars in nuclear medicine [Semin Nucl Med] 2024 Dec 13. Date of Electronic Publication: 2024 Dec 13.
DOI: 10.1053/j.semnuclmed.2024.11.005
Abstrakt: [ 18 F]FDG PET/CT is a powerful imaging modality of high performance in multiple myeloma (MM) and is considered the appropriate method for assessing treatment response in this disease. On the other hand, due to the heterogeneous and sometimes complex patterns of bone marrow infiltration in MM, the interpretation of PET/CT can be particularly challenging, hampering interobserver reproducibility and limiting the diagnostic and prognostic ability of the modality. Although many approaches have been developed to address the issue of standardization, none can yet be considered a standard method for interpretation or objective quantification of PET/CT. Therefore, advanced diagnostic quantification approaches are needed to support and potentially guide the management of MM. In recent years, radiomics has emerged as an innovative method for high-throughput mining of image-derived features for clinical decision making, which may be particularly helpful in oncology. In addition, machine learning and deep learning, both subfields of artificial intelligence (AI) closely related to the radiomics process, have been increasingly applied to automated image analysis, offering new possibilities for a standardized evaluation of imaging modalities such as CT, PET/CT and MRI in oncology. In line with this, the initial but steadily growing literature on the application of radiomics and AI-based methods in the field of [ 18 F]FDG PET/CT in MM has already yielded encouraging results, offering a potentially reliable tool towards optimization and standardization of interpretation in this disease. The main results of these studies are presented in this review.
Competing Interests: Declaration of competing interest Hartmut Goldschmidt declares the following: Grants and/or provision of Investigational Medicinal Product: Amgen, Array Biopharma/Pfizer, BMS/Celgene, Chugai, Dietmar-Hopp-Foundation, Janssen, Johns Hopkins University, Mundipharma GmbH, Sanofi. Research Support/ Forschung und Studien: Amgen, BMS, Celgene, GlycoMimetics Inc., GSK, Heidelberg Pharma, Hoffmann-La Roche, Karyopharm, Janssen, Incyte Corporation, Millenium Pharmaceuticals Inc., Molecular Partners, Merck Sharp and Dohme (MSD), MorphoSys AG, Pfizer, Sanofi, Takeda, Novartis. Advisory Boards: Adaptive Biotechnology, Amgen, BMS, Janssen, Sanofi. Honoraria / Nebentätigkeiten: Amgen, BMS, Chugai, GlaxoSmithKline (GSK), Janssen, Novartis, Sanofi, Pfizer. Support for attending meetings and/or travel / Reisekosten: Amgen, BMS, GlaxoSmithKline (GSK), Janssen, Novartis, Sanofi, Pfizer. All other authors declare no conflicts of interest.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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