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Daw S, Claviez A, Kurch L, Stoevesandt D, Attarbaschi A, Balwierz W, Beishuizen A, Cepelova M, Ceppi F, Fernandez-Teijeiro A, Fosså A, Georgi TW, Hjalgrim LL, Hraskova A, Leblanc T, Mascarin M, Pears J, Landman-Parker J, Prelog T, Klapper W, Ramsay A, Kluge R, Dieckmann K, Pelz T, Vordermark D, Körholz D, Hasenclever D, Mauz-Körholz C. Transplant and Nontransplant Salvage Therapy in Pediatric Relapsed or Refractory Hodgkin Lymphoma: The EuroNet-PHL-R1 Phase 3 Nonrandomized Clinical Trial. JAMA Oncol 2025; 11:258-267. [PMID: 39745682 PMCID: PMC11926631 DOI: 10.1001/jamaoncol.2024.5636] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 08/16/2024] [Indexed: 03/21/2025]
Abstract
Importance The current standard-of-care salvage therapy in relapsed/refractory classic Hodgkin lymphoma (cHL) includes consolidation high-dose chemotherapy (HDCT)/autologous stem cell transplant (aSCT). Objective To investigate whether presalvage risk factors and fludeoxyglucose-18 (FDG) positron emission tomography (PET) response to reinduction chemotherapy can guide escalation or de-escalation between HDCT/aSCT or transplant-free consolidation with radiotherapy to minimize toxic effects while maintaining high cure rates. Design, Setting, and Participants EuroNet-PHL-R1 was a nonrandomized clinical trial that enrolled patients younger than 18 years with first relapsed/refractory cHL across 68 sites in 13 countries in Europe between January 2007 and January 2013. Data were analyzed between September 2022 and July 2024. Intervention Reinduction chemotherapy consisted of alternating IEP (ifosfamide, etoposide, prednisolone) and ABVD (adriamycin, bleomycin, vinblastine, dacarbazine). Patients with low-risk disease (late relapse after 2 cycles of first-line chemotherapy and any relapse with an adequate response after 1 IEP/ABVD defined as complete metabolic response on FDG-PET and at least 50% volume reduction) received a second IEP/ABVD cycle and radiotherapy (RT) to all sites involved at relapse. Patients with high-risk disease (all primary progressions and relapses with inadequate response after 1 IEP/ABVD cycle) received a second IEP/ABVD cycle plus HDCT/aSCT with or without RT. Main Outcomes and Measures The primary end point was 5-year event-free survival. Secondary end points were overall survival (OS) and progression-free survival (PFS). PFS was identical to event-free survival because no secondary cancers were observed. PFS data alone are presented for simplicity. Results Of 118 patients analyzed, 58 (49.2%) were female, and the median (IQR) age was 16.3 (14.5-17.6) years. The median (IQR) follow-up was 67.5 (58.5-77.0) months. The overall 5-year PFS was 71.3% (95% CI, 63.5%-80.1%), and OS was 82.7% (95% CI, 75.8%-90.1%). For patients in the low-risk group (n = 59), 41 received nontransplant salvage with a 5-year PFS of 89.7% (95% CI, 80.7%-99.8%) and OS of 97.4% (95% CI, 92.6%-100%). In contrast, 18 received HDCT/aSCT off protocol, with a 5-year PFS of 88.9% (95% CI, 75.5%-100%) and OS of 100%. All 59 patients with high-risk disease received HDCT/aSCT (and 23 received post-HDCT/aSCT RT) with a 5-year PFS of 53.3% (95% CI, 41.8%-67.9%) and OS of 66.5% (95% CI, 54.9%-80.5%). Conclusion and Relevance In this nonrandomized clinical trial, FDG-PET response-guided salvage in relapsed cHL may identify patients in whom transplant-free salvage achieves excellent outcomes. HDCT/aSCT may be reserved for primary progression and relapsed cHL with inadequate response. Trial Registration ClinicalTrials.gov Identifier: NCT00433459.
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Affiliation(s)
- Stephen Daw
- Pediatric Division, Children and Young People’s Cancer Services, University College London Hospital, London, United Kingdom
| | - Alexander Claviez
- Department of Pediatrics, University Hospital Magdeburg, Magdeburg, Germany
| | - Lars Kurch
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Dietrich Stoevesandt
- Department of Radiology, University Hospital Halle (Saale), Halle (Saale), Germany
| | - Andishe Attarbaschi
- Department of Paediatric Haematology and Oncology, St. Anna Children’s Hospital, Medical University of Vienna, St Anna Children’s Cancer Research Institute, Vienna, Austria
| | - Walentyna Balwierz
- Jagiellonian University Medical College, Institute of Pediatrics, Krakow, Poland
| | - Auke Beishuizen
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Michaela Cepelova
- Department of Paediatric Haematology and Oncology, University Hospital Motol and 2nd Medical Faculty of Charles University, Prague, Czech Republic
| | - Francesco Ceppi
- Division of Pediatrics, Department of Woman-Mother-Child, Pediatric Hematology-Oncology Unit, University Hospital of Lausanne and University of Lausanne, Lausanne, Switzerland
| | | | - Alexander Fosså
- Oslo University Hospital, Department of Oncology, and KG Jebsen Centre for B-cell malignancies, University of Oslo, Oslo, Norway
| | - Thomas W. Georgi
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Lisa Lyngsie Hjalgrim
- Department of Paediatrics and Adolescents Medicine, Rigshospitalet Copenhagen, The Juliane Marie Centre, Copenhagen, Denmark
| | - Andrea Hraskova
- Disease and Comenius University Bratislava, Bratislava, Slovakia
| | - Thierry Leblanc
- Hôpital Robert-Debré, Service d’Hématologie Pédiatrique and Université Paris-Cité Paris, Paris, France
| | - Maurizio Mascarin
- Department of Radiation Oncology, AYA Oncology and Pediatric Radiotherapy Unit, CRO Centro di Riferimento Oncologico, IRCCS, Aviano (PN), Italy
| | - Jane Pears
- Children’s Health Ireland, Crumlin, Dublin, Ireland
| | - Judith Landman-Parker
- Department of Paediatric Oncology and Haematology, Hôpital Armand-Trousseau, Sorbonne Université, Paris, France
| | - Tomaž Prelog
- Department of Pediatric Hematology and Oncology, University Children’s Hospital, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Wolfram Klapper
- Department of Pathology, Hematopathology Section, University Hospital Schleswig-Holstein, Christian-Albrechts-Universität, Kiel, Germany
| | - Alan Ramsay
- Department of Cellular Pathology, University College Hospital London, London, United Kingdom
| | - Regine Kluge
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Karin Dieckmann
- Department of Radiooncology, Allgemeines Krankenhaus Wien, Medical University Vienna, Vienna, Austria
| | - Tanja Pelz
- Department of Radiooncology, University Hospital Halle (Saale), Halle (Saale), Germany
| | - Dirk Vordermark
- Department of Radiooncology, University Hospital Halle (Saale), Halle (Saale), Germany
| | - Dieter Körholz
- Department of Paediatric Haematology, Oncology and Immunodeficiency, University Hospital Justus-Liebig University Giessen, Giessen, Germany
| | - Dirk Hasenclever
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Christine Mauz-Körholz
- Department of Paediatric Haematology, Oncology and Immunodeficiency, University Hospital Justus-Liebig University Giessen, Giessen, Germany
- Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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Yang HX, Xiong J, Zhao WL. [Advancements in artificial intelligence for the precise diagnosis and treatment of hematological malignancies]. ZHONGHUA XUE YE XUE ZA ZHI = ZHONGHUA XUEYEXUE ZAZHI 2025; 46:186-192. [PMID: 40134203 PMCID: PMC11951223 DOI: 10.3760/cma.j.cn121090-20241022-00409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/22/2024] [Indexed: 03/27/2025]
Abstract
Hematological malignancy is a highly heterogeneous disease with complex biological characteristics and diverse clinical manifestations. Therefore, precise diagnosis and treatment are crucial and urgently needed. To further improve the accuracy of diagnosis and prognostication and to promote personalized therapy, artificial intelligence (AI) has been increasingly used. This study reviewed literature published in the last 5 years and summarized the application, benefits, and drawbacks of AI in the diagnosis, treatment, and prognosis of hematologic malignancies. Although AI can effectively improve the accuracy of diagnosis and therapy, low-quality data, poor interpretability of the model, and limited clinical transformation have impeded its popularization and application. In the future, the clinical application of AI in hematologic malignancy can be accelerated by establishing standards for clinical data processing, integrating multimodal information for accurate diagnosis and prognostication, and conducting systematic clinical verification of model algorithms.
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Affiliation(s)
- H X Yang
- Department of Haematology, State Key Laboratory of Medical Genomics, Shanghai Institute of Haematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - J Xiong
- Department of Haematology, State Key Laboratory of Medical Genomics, Shanghai Institute of Haematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - W L Zhao
- Department of Haematology, State Key Laboratory of Medical Genomics, Shanghai Institute of Haematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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Garuffo L, Leoni A, Gatta R, Bernardi S. The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready? Cancers (Basel) 2025; 17:395. [PMID: 39941764 PMCID: PMC11816169 DOI: 10.3390/cancers17030395] [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: 12/19/2024] [Revised: 01/10/2025] [Accepted: 01/23/2025] [Indexed: 02/16/2025] Open
Abstract
Hematopoietic stem cell transplantation (HSCT) is a life-saving therapy for hematologic malignancies, such as leukemia and lymphoma and other severe conditions but is associated with significant risks, including graft versus host disease (GVHD), relapse, and treatment-related mortality. The increasing complexity of clinical, genomic, and biomarker data has spurred interest in machine learning (ML), which has emerged as a transformative tool to enhance decision-making and optimize outcomes in HSCT. This review examines the applications of ML in HSCT, focusing on donor selection, conditioning regimen, and prediction of post-transplant outcomes. Machine learning approaches, including decision trees, random forests, and neural networks, have demonstrated potential in improving donor compatibility algorithms, mortality and relapse prediction, and GVHD risk stratification. Integrating "omics" data with ML models has enabled the identification of novel biomarkers and the development of highly accurate predictive tools, supporting personalized treatment strategies. Despite promising advancements, challenges persist, including data standardization, algorithm interpretability, and ethical considerations regarding patient privacy. While ML holds promise for revolutionizing HSCT management, addressing these barriers through multicenter collaborations and regulatory frameworks remains essential for broader clinical adoption. In addition, the potential of ML can cope with some challenges such as data harmonization, patients' data protection, and availability of adequate infrastructure. Future research should prioritize larger datasets, multimodal data integration, and robust validation methods to fully realize ML's transformative potential in HSCT.
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Affiliation(s)
- Luca Garuffo
- Unit of Blood Disease and Stem Cell Transplantation, Department of Clinical and Experimental Sciences, University of Brescia, ASST Spedali Civili, 25123 Brescia, Italy; (L.G.); (S.B.)
- CREA (Centro di Ricerca Emato-Oncologica AIL), ASST Spedali Civili of Brescia, 25123 Brescia, Italy
| | - Alessandro Leoni
- Unit of Blood Disease and Stem Cell Transplantation, Department of Clinical and Experimental Sciences, University of Brescia, ASST Spedali Civili, 25123 Brescia, Italy; (L.G.); (S.B.)
- CREA (Centro di Ricerca Emato-Oncologica AIL), ASST Spedali Civili of Brescia, 25123 Brescia, Italy
| | - Roberto Gatta
- Department of Clinical and Experimental Sciences, University of Brescia, 25123 Brescia, Italy;
| | - Simona Bernardi
- Unit of Blood Disease and Stem Cell Transplantation, Department of Clinical and Experimental Sciences, University of Brescia, ASST Spedali Civili, 25123 Brescia, Italy; (L.G.); (S.B.)
- CREA (Centro di Ricerca Emato-Oncologica AIL), ASST Spedali Civili of Brescia, 25123 Brescia, Italy
- National Center for Gene Therapy and Drugs Based on RNA Technology—CN3, 35122 Padua, Italy
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Triumbari EKA, Morland D, Gatta R, Boldrini L, De Summa M, Chiesa S, Cuccaro A, Maiolo E, Hohaus S, Annunziata S. The predictive power of 18F-FDG PET/CT two-lesions radiomics and conventional models in classical Hodgkin's Lymphoma: a comparative retrospectively-validated study. Ann Hematol 2025; 104:641-651. [PMID: 39808225 PMCID: PMC11868178 DOI: 10.1007/s00277-025-06190-8] [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: 08/28/2024] [Accepted: 01/03/2025] [Indexed: 01/16/2025]
Abstract
In a previous preliminary study, radiomic features from the largest and the hottest lesion in baseline 18F-FDG PET/CT (bPET/CT) of classical Hodgkin's Lymphoma (cHL) predicted early response-to-treatment and prognosis. Aim of this large retrospectively-validated study is to evaluate the predictive role of two-lesions radiomics in comparison with other clinical and conventional PET/CT models. cHL patients with bPET/CT between 2010 and 2020 were retrospectively included and randomized into training-validation sets. Target lesions were: Lesion_A, with largest axial diameter (Dmax); Lesion_B, with highest SUVmax. Total-metabolic-tumor-volume (TMTV) was calculated and 212 radiomic features were extracted. PET/CT features were harmonized using ComBat across two scanners. Outcomes were progression-free-survival (PFS) and Deauville Score at interim PET/CT (DS). For each outcome, three predictive models and their combinations were trained and validated: - radiomic model "R"; - conventional PET/CT model "P"; - clinical model "C". 197 patients were included (training = 118; validation = 79): 38/197 (19%) patients had adverse events and 42/193 (22%) had DS ≥ 4. In the training phase, only one radiomic feature was selected for PFS prediction in model "R" (Lesion_B F_cm.corr, C-index 66.9%). Best "C" model combined stage and IPS (C-index 74.8%), while optimal "P" model combined TMTV and Dmax (C-index 63.3%). After internal validation, "C", "C + R", "R + P" and "C + R + P" significantly predicted PFS. The best validated model was "C + R" (C-index 66.3%). No model was validated for DS prediction. In this large retrospectively-validated study, a combination of baseline 18F-FDG PET/CT two-lesions radiomics and other conventional models showed an added prognostic power in patients with cHL. As single models, conventional clinical parameters maintain their prognostic power, while radiomics or conventional PET/CT alone seem to be sub-optimal to predict survival.
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Affiliation(s)
- Elizabeth Katherine Anna Triumbari
- Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - David Morland
- Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Institut Godinot and CReSTIC EA 3804, Université de Reims Champagne-Ardenne, Reims, France
| | - Roberto Gatta
- Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Luca Boldrini
- Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Marco De Summa
- Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Medipass S.p.a. Integrative Service, Rome, Italy
| | - Silvia Chiesa
- Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Annarosa Cuccaro
- Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Elena Maiolo
- Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Stefan Hohaus
- Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Salvatore Annunziata
- Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
- Department of Radiology, Radiotherapy and Hematology, Unità di Medicina Nucleare, GSTeP Radiopharmacy, Fondazione Policlinico Universitario A.Gemelli IRCCS, Rome, Italy.
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Hasanabadi S, Aghamiri SMR, Abin AA, Abdollahi H, Arabi H, Zaidi H. Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis. Cancers (Basel) 2024; 16:3511. [PMID: 39456604 PMCID: PMC11505665 DOI: 10.3390/cancers16203511] [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: 09/05/2024] [Revised: 10/11/2024] [Accepted: 10/15/2024] [Indexed: 10/28/2024] Open
Abstract
Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically 18F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from 18F-FDG PET/CT in the management of lymphoma patients.
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Affiliation(s)
- Setareh Hasanabadi
- Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran 1983969411, Iran; (S.H.); (S.M.R.A.)
| | - Seyed Mahmud Reza Aghamiri
- Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran 1983969411, Iran; (S.H.); (S.M.R.A.)
| | - Ahmad Ali Abin
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran 1983969411, Iran;
| | - Hamid Abdollahi
- Department of Radiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada;
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland;
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland;
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, 500 Odense, Denmark
- University Research and Innovation Center, Óbuda University, 1034 Budapest, Hungary
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Mahmutovic Persson I, Bozovic G, Westergren-Thorsson G, Rolandsson Enes S. Spatial lung imaging in clinical and translational settings. Breathe (Sheff) 2024; 20:230224. [PMID: 39360023 PMCID: PMC11444490 DOI: 10.1183/20734735.0224-2023] [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: 04/08/2024] [Accepted: 07/05/2024] [Indexed: 10/04/2024] Open
Abstract
For many severe lung diseases, non-invasive biomarkers from imaging could improve early detection of lung injury or disease onset, establish a diagnosis, or help follow-up disease progression and treatment strategies. Imaging of the thorax and lung is challenging due to its size, respiration movement, transferred cardiac pulsation, vast density range and gravitation sensitivity. However, there is extensive ongoing research in this fast-evolving field. Recent improvements in spatial imaging have allowed us to study the three-dimensional structure of the lung, providing both spatial architecture and transcriptomic information at single-cell resolution. This fast progression, however, comes with several challenges, including significant image file storage and network capacity issues, increased costs, data processing and analysis, the role of artificial intelligence and machine learning, and mechanisms to combine several modalities. In this review, we provide an overview of advances and current issues in the field of spatial lung imaging.
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Affiliation(s)
- Irma Mahmutovic Persson
- Lund University BioImaging Centre (LBIC), Faculty of Medicine, Lund University, Lund, Sweden
- Respiratory Immunopharmacology, Experimental Medical Science, Faculty of Medicine, Lund University, Lund, Sweden
| | - Gracijela Bozovic
- Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden
- Department of Medical Imaging and Clinical Physiology, Skåne University Hospital, Lund, Sweden
| | - Gunilla Westergren-Thorsson
- Lund University BioImaging Centre (LBIC), Faculty of Medicine, Lund University, Lund, Sweden
- Lung Biology, Experimental Medical Science, Faculty of Medicine, Lund University, Lund, Sweden
| | - Sara Rolandsson Enes
- Lung Biology, Experimental Medical Science, Faculty of Medicine, Lund University, Lund, Sweden
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Parihar AS, Pant N, Subramaniam RM. Quarter-Century PET/CT Transformation of Oncology: Lymphoma. PET Clin 2024; 19:281-290. [PMID: 38403384 DOI: 10.1016/j.cpet.2023.12.014] [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] [Indexed: 02/27/2024]
Abstract
The clinical landscape of lymphomas has changed dramatically over the last 2 decades, including significant progress made in the understanding and utilization of imaging modalities and the available treatment options for both indolent and aggressive lymphomas. Since the introduction of hybrid PET/CT scanners in 2001, the indications of 18F-fluorodeoxyglucose (FDG) PET/CT in the management of lymphomas have grown rapidly. In today's clinical practice, FDG PET/CT is used in successful management of the vast majority patients with lymphomas.
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Affiliation(s)
- Ashwin Singh Parihar
- Mallinckrodt Institute of Radiology; Siteman Cancer Center, Washington University School of Medicine, St Louis, MO, USA.
| | | | - Rathan M Subramaniam
- Faculty of Medicine, Nursing, Midwifery & Health Sciences, The University of Notre Dame Australia, Sydney, Australia; Department of Radiology, Duke University, Durham, NC, USA; Department of Medicine, University of Otago Medical School, Dunedin, New Zealand
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