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Tricarico P, Chardin D, Martin N, Contu S, Hugonnet F, Otto J, Humbert O. Total metabolic tumor volume on 18F-FDG PET/CT is a game-changer for patients with metastatic lung cancer treated with immunotherapy. J Immunother Cancer 2024; 12:e007628. [PMID: 38649279 PMCID: PMC11043703 DOI: 10.1136/jitc-2023-007628] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE Because of atypical response imaging patterns in patients with metastatic non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICPIs), new biomarkers are needed for a better monitoring of treatment efficacy. The aim of this prospective study was to evaluate the prognostic value of volume-derived positron-emission tomography (PET) parameters on baseline and follow-up 18F-fluoro-deoxy-glucose PET (18F-FDG-PET) scans and compare it with the conventional PET Response Criteria in Solid Tumors (PERCIST). METHODS Patients with metastatic NSCLC were included in two different single-center prospective trials. 18F-FDG-PET studies were performed before the start of immunotherapy (PETbaseline), after 6-8 weeks (PETinterim1) and after 12-16 weeks (PETinterim2) of treatment, using PERCIST criteria for tumor response assessment. Different metabolic parameters were evaluated: absolute values of maximum standardized uptake value (SUVmax) of the most intense lesion, total metabolic tumor volume (TMTV), total lesion glycolysis (TLG), but also their percentage changes between PET studies (ΔSUVmax, ΔTMTV and ΔTLG). The median follow-up of patients was 31 (7.3-31.8) months. Prognostic values and optimal thresholds of PET parameters were estimated by ROC (Receiver Operating Characteristic) curve analysis of 12-month overall survival (12M-OS) and 6-month progression-free survival (6M-PFS). Tumor progression needed to be confirmed by a multidisciplinary tumor board, considering atypical response patterns on imaging. RESULTS 110 patients were prospectively included. On PETbaseline, TMTV was predictive of 12M-OS [AUC (Area Under Curve) =0.64; 95% CI: 0.61 to 0.66] whereas SUVmax and TLG were not. On PETinterim1 and PETinterim2, all metabolic parameters were predictive for 12M-OS and 6M-PFS, the residual TMTV on PETinterim1 (TMTV1) being the strongest prognostic biomarker (AUC=0.83 and 0.82; 95% CI: 0.74 to 0.91, for 12M-OS and 6M-PFS, respectively). Using the optimal threshold by ROC curve to classify patients into three TMTV1 subgroups (0 cm3; 0-57 cm3; >57 cm3), TMTV1 prognostic stratification was independent of PERCIST criteria on both PFS and OS, and significantly outperformed them. Subgroup analysis demonstrated that TMTV1 remained a strong prognostic biomarker of 12M-OS for non-responding patients (p=0.0003) according to PERCIST criteria. In the specific group of patients with PERCIST progression on PETinterim1, low residual tumor volume (<57 cm3) was still associated with a very favorable patients' outcome (6M-PFS=73%; 24M-OS=55%). CONCLUSION The absolute value of residual metabolic tumor volume, assessed 6-8 weeks after the start of ICPI, is an optimal and independent prognostic measure, exceeding and complementing conventional PERCIST criteria. Oncologists should consider it in patients with first tumor progression according to PERCIST criteria, as it helps identify patients who benefit from continued treatment. TRIAL REGISTRATION NUMBER 2018-A02116-49; NCT03584334.
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Affiliation(s)
- Pierre Tricarico
- Department of Nuclear Medicine, Centre Antoine-Lacassagne, Nice, France
| | - David Chardin
- Department of Nuclear Medicine, Centre Antoine-Lacassagne, Nice, France
- IBV, Université Côte d'Azur, CNRS, Inserm, Nice, France
| | - Nicolas Martin
- Department of Medical Oncology, Centre Antoine-Lacassagne, Nice, France
| | - Sara Contu
- Department of Biostatistics, Centre Antoine-Lacassagne, Nice, France
| | - Florent Hugonnet
- Department of Nuclear Medicine, Centre Hospitalier Princesse Grâce, Monaco
| | - Josiane Otto
- Department of Medical Oncology, Centre Antoine-Lacassagne, Nice, France
| | - Olivier Humbert
- Department of Nuclear Medicine, Centre Antoine-Lacassagne, Nice, France
- IBV, Université Côte d'Azur, CNRS, Inserm, Nice, France
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Barrington SF, Cottereau AS, Zijlstra JM. Is 18F-FDG Metabolic Tumor Volume in Lymphoma Really Happening? J Nucl Med 2024; 65:jnumed.123.267022. [PMID: 38388515 PMCID: PMC10995527 DOI: 10.2967/jnumed.123.267022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/29/2024] [Accepted: 01/29/2024] [Indexed: 02/24/2024] Open
Affiliation(s)
- Sally F Barrington
- King's College London and Guy's and St. Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom;
| | - Anne-Ségolène Cottereau
- Department of Nuclear Medicine, Cochin Hospital, APHP, Paris Cité University, Paris, France; and
| | - Josée M Zijlstra
- Department of Hematology and Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, The Netherlands
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Wang H, Sarrami A, Wu JTY, Baratto L, Sharma A, Wong KCL, Singh SB, Daldrup-Link HE, Syeda-Mahmood T. Multimodal Pediatric Lymphoma Detection using PET and MRI. AMIA Annu Symp Proc 2024; 2023:736-743. [PMID: 38222333 PMCID: PMC10785920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Lymphoma is one of the most common types of cancer for children (ages 0 to 19). Due to the reduced radiation exposure, PET/MR systems that allow simultaneous PET and MR imaging have become the standard of care for diagnosing cancers and monitoring tumor response to therapy in the pediatric population. In this work, we developed a multimodal deep learning algorithm for automatic pediatric lymphoma detection using PET and MRI. Through innovative designs such as standardized uptake value (SUV) guided tumor candidate generation, location aware classification model learning and weighted multimodal feature fusion, our algorithm can be effectively trained with limited data and achieved superior tumor detection performance over the state-of-the-art in our experiments.
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Affiliation(s)
- Hongzhi Wang
- IBM Almaden Research Center, San Jose, CA, U.S.A
| | | | - Joy Tzung-Yu Wu
- IBM Almaden Research Center, San Jose, CA, U.S.A
- Stanford University, Palo Alto, CA, U.S.A
| | | | - Arjun Sharma
- IBM Almaden Research Center, San Jose, CA, U.S.A
| | - Ken C L Wong
- IBM Almaden Research Center, San Jose, CA, U.S.A
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Vassilakopoulos TP, Arapaki M, Diamantopoulos PT, Liaskas A, Panitsas F, Siakantaris MP, Dimou M, Kokoris SI, Sachanas S, Belia M, Chatzidimitriou C, Konstantinou EA, Asimakopoulos JV, Petevi K, Boutsikas G, Kanellopoulos A, Piperidou A, Lefaki ME, Georgopoulou A, Kopsaftopoulou A, Zerzi K, Drandakis I, Dimopoulou MN, Kyrtsonis MC, Tsaftaridis P, Plata E, Variamis E, Tsourouflis G, Kontopidou FN, Konstantopoulos K, Pangalis GA, Panayiotidis P, Angelopoulou MK. Prognostic Impact of Serum β 2-Microglobulin Levels in Hodgkin Lymphoma Treated with ABVD or Equivalent Regimens: A Comprehensive Analysis of 915 Patients. Cancers (Basel) 2024; 16:238. [PMID: 38254729 PMCID: PMC10813286 DOI: 10.3390/cancers16020238] [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: 12/04/2023] [Revised: 12/26/2023] [Accepted: 12/29/2023] [Indexed: 01/24/2024] Open
Abstract
The significance of serum beta-2 microglobulin (sβ2m) in Hodgkin lymphoma (HL) is controversial. We analyzed 915 patients with HL, who were treated with ABVD or equivalent regimens with or without radiotherapy. Sβ2m levels were measured by a radioimmunoassay (upper normal limit 2.4 mg/L). Sequential cutoffs (1.8-3.0 by 0.1 mg/L increments, 3.5 and 4.0 mg/L) were tested along with ROC analysis. The median sβ2m levels were 2.20 mg/L and were elevated (>2.4 mg/L) in 383/915 patients (41.9%). Higher sβ2m was associated with inferior freedom from progression (FFP) at all tested cutoffs. The best cutoff was 2.0 mg/L (10-year FFP 83% vs. 70%, p = 0.001), which performed better than the 2.4 mg/L cutoff ("normal versus high"). In multivariate analysis, sβ2m > 2.0 mg/L was an independent adverse prognostic factor in the whole patient population. In multivariate overall survival analysis, sβ2m levels were predictive at 2.0 mg/L cutoff in the whole patient population and in advanced stages. Similarly, sβ2m > 2.0 mg/L independently predicted inferior HL-specific survival in the whole patient population. Our data suggest that higher sβ2m is an independent predictor of outcome in HL but the optimal cutoff lies within the normal limits (i.e., at 2.0 mg/L) in this predominantly young patient population, performing much better than a "normal versus high" cutoff set at 2.4 mg/L.
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Affiliation(s)
- Theodoros P. Vassilakopoulos
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Maria Arapaki
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Panagiotis T. Diamantopoulos
- First Department of Internal Medicine, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (P.T.D.)
| | - Athanasios Liaskas
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Fotios Panitsas
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Marina P. Siakantaris
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Maria Dimou
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Styliani I. Kokoris
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Sotirios Sachanas
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Marina Belia
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Chrysovalantou Chatzidimitriou
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Elianna A. Konstantinou
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - John V. Asimakopoulos
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Kyriaki Petevi
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - George Boutsikas
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Alexandros Kanellopoulos
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Alexia Piperidou
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Maria-Ekaterini Lefaki
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Angeliki Georgopoulou
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Anastasia Kopsaftopoulou
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Kalliopi Zerzi
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Ioannis Drandakis
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Maria N. Dimopoulou
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Marie-Christine Kyrtsonis
- First Department of Internal Medicine Propedeutic, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece
| | - Panayiotis Tsaftaridis
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Eleni Plata
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Eleni Variamis
- First Department of Internal Medicine, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (P.T.D.)
| | - Gerassimos Tsourouflis
- Second Department of Surgery Propedeutic, National and Kapodistrian University of Athens, Laikon General Hospital, 11527Athens, Greece
| | - Flora N. Kontopidou
- Second Department of Internal Medicine, National and Kapodistrian University of Athens, Ippokration General Hospital, 11527 Athens, Greece;
| | - Kostas Konstantopoulos
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Gerassimos A. Pangalis
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Panayiotis Panayiotidis
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
| | - Maria K. Angelopoulou
- Department of Haematology and Bone Marrow Transplantation, National and Kapodistrian University of Athens, School of Medicine, Laikon General Hospital, 17 Ag. Thoma Str., 11527 Athens, Greece; (M.A.); (M.D.); (M.B.); (C.C.); (E.A.K.); (J.V.A.); (A.K.); (P.T.); (M.K.A.)
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Driessen J, Zwezerijnen GJC, Schöder H, Kersten MJ, Moskowitz AJ, Moskowitz CH, Eertink JJ, Heymans MW, Boellaard R, Zijlstra JM. Prognostic model using 18F-FDG PET radiomics predicts progression-free survival in relapsed/refractory Hodgkin lymphoma. Blood Adv 2023; 7:6732-6743. [PMID: 37722357 PMCID: PMC10651466 DOI: 10.1182/bloodadvances.2023010404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/22/2023] [Accepted: 08/25/2023] [Indexed: 09/20/2023] Open
Abstract
Investigating prognostic factors in patients with relapsed or primary refractory classical Hodgkin lymphoma (R/R cHL) is essential to optimize risk-adapted treatment strategies. We built a prognostic model using baseline quantitative 18F-fluorodeoxyglucose positron emission tomography (PET) radiomics features and clinical characteristics to predict the progression-free survival (PFS) among patients with R/R cHL treated with salvage chemotherapy followed by autologous stem cell transplantation. Metabolic tumor volume and several novel radiomics dissemination features, representing interlesional differences in distance, volume, and standard uptake value, were extracted from the baseline PET. Machine learning using backward selection and logistic regression were applied to develop and train the model on a total of 113 patients from 2 clinical trials. The model was validated on an independent external cohort of 69 patients. In addition, we validated 4 different PET segmentation methods to calculate radiomics features. We identified a subset of patients at high risk for progression with significant inferior 3-year PFS outcomes of 38.1% vs 88.4% for patients in the low-risk group in the training cohort (P < .001) and 38.5% vs 75.0% in the validation cohort (P = .015), respectively. The overall survival was also significantly better in the low-risk group (P = .022 and P < .001). We provide a formula to calculate a risk score for individual patients based on the model. In conclusion, we developed a prognostic model for PFS combining radiomics and clinical features in a large cohort of patients with R/R cHL. This model calculates a PET-based risk profile and can be applied to develop risk-stratified treatment strategies for patients with R/R cHL. These trials were registered at www.clinicaltrials.gov as #NCT02280993, #NCT00255723, and #NCT01508312.
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Affiliation(s)
- Julia Driessen
- Department of Hematology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- Division of Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- LYMMCARE, Lymphoma and Myeloma Center Amsterdam, Amsterdam, The Netherlands
| | - Gerben J. C. Zwezerijnen
- Division of Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, The Netherlands
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Marie José Kersten
- Department of Hematology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- LYMMCARE, Lymphoma and Myeloma Center Amsterdam, Amsterdam, The Netherlands
| | - Alison J. Moskowitz
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Craig H. Moskowitz
- Department of Medicine, Sylvester Comprehensive Cancer Center, Miami, FL
| | - Jakoba J. Eertink
- Division of Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Hematology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn W. Heymans
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Division of Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, The Netherlands
| | - Josée M. Zijlstra
- Division of Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Hematology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Jing F, Liu Y, Zhao X, Wang N, Dai M, Chen X, Zhang Z, Zhang J, Wang J, Wang Y. Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma. EJNMMI Res 2023; 13:92. [PMID: 37884763 PMCID: PMC10603012 DOI: 10.1186/s13550-023-01047-5] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma in adults. Standard treatment includes chemoimmunotherapy with R-CHOP or similar regimens. Despite treatment advancements, many patients with DLBCL experience refractory disease or relapse. While baseline 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) parameters have shown promise in predicting survival, they may not fully capture lesion heterogeneity. This study aimed to assess the prognostic value of baseline 18F-FDG PET radiomics features in comparison with clinical factors and metabolic parameters for assessing 2-year progression-free survival (PFS) and 5-year overall survival (OS) in patients with DLBCL. RESULTS A total of 201 patients with DLBCL were enrolled in this study, and 1328 radiomics features were extracted. The radiomics signatures, clinical factors, and metabolic parameters showed significant prognostic value for individualized prognosis prediction in patients with DLBCL. Radiomics signatures showed the lowest Akaike information criterion (AIC) value and highest Harrell's concordance index (C-index) value in comparison with clinical factors and metabolic parameters for both PFS (AIC: 571.688 vs. 596.040 vs. 576.481; C-index: 0.732 vs. 0.658 vs. 0.702, respectively) and OS (AIC: 339.843 vs. 363.671 vs. 358.412; C-index: 0.759 vs. 0.667 vs. 0.659, respectively). Statistically significant differences were observed in the area under the curve (AUC) values between the radiomics signatures and clinical factors for both PFS (AUC: 0.768 vs. 0.681, P = 0.017) and OS (AUC: 0.767 vs. 0.667, P = 0.023). For OS, the AUC of the radiomics signatures were significantly higher than those of metabolic parameters (AUC: 0.767 vs. 0.688, P = 0.007). However, for PFS, no significant difference was observed between the radiomics signatures and metabolic parameters (AUC: 0.768 vs. 0.756, P = 0.654). The combined model and the best-performing individual model (radiomics signatures) alone showed no significant difference for both PFS (AUC: 0.784 vs. 0.768, P = 0.163) or OS (AUC: 0.772 vs. 0.767, P = 0.403). CONCLUSIONS Radiomics signatures derived from PET images showed the high predictive power for progression in patients with DLBCL. The combination of radiomics signatures, clinical factors, and metabolic parameters may not significantly improve predictive value beyond that of radiomics signatures alone.
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Affiliation(s)
- Fenglian Jing
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Yunuan Liu
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Xinming Zhao
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China.
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China.
| | - Na Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Meng Dai
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Xiaolin Chen
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Zhaoqi Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Jingmian Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Jianfang Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
| | - Yingchen Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, 050011, Hebei, China
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7
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Alderuccio JP, Kuker RA, Yang F, Moskowitz CH. Quantitative PET-based biomarkers in lymphoma: getting ready for primetime. Nat Rev Clin Oncol 2023; 20:640-657. [PMID: 37460635 DOI: 10.1038/s41571-023-00799-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2023] [Indexed: 08/20/2023]
Abstract
The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.
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Affiliation(s)
- Juan Pablo Alderuccio
- Department of Medicine, Division of Hematology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Russ A Kuker
- Department of Radiology, Division of Nuclear Medicine, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Fei Yang
- Department of Radiation Oncology, Division of Medical Physics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Craig H Moskowitz
- Department of Medicine, Division of Hematology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
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8
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Barrington SF. Advances in positron emission tomography and radiomics. Hematol Oncol 2023; 41 Suppl 1:11-19. [PMID: 37294959 PMCID: PMC10775708 DOI: 10.1002/hon.3137] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 06/11/2023]
Abstract
Positron emission tomography is established for staging and response evaluation in lymphoma using visual evaluation and semi-quantitative analysis. Radiomic analysis involving quantitative imaging features at baseline, such as metabolic tumor volume and markers of disease dissemination and changes in the standardized uptake value during treatment are emerging as powerful biomarkers. The combination of radiomic features with clinical risk factors and genomic analysis offers the potential to improve clinical risk prediction. This review discusses the state of current knowledge, progress toward standardization of tumor delineation for radiomic analysis and argues that radiomic features, molecular markers and circulating tumor DNA should be included in clinical trial designs to enable the development of baseline and dynamic risk scores that could further advance the field to facilitate testing of novel treatments and personalized therapy in aggressive lymphomas.
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Affiliation(s)
- Sally F. Barrington
- School of Biomedical Engineering and Imaging SciencesSt Thomas' Campus, Kings College LondonLondonUK
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9
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Abstract
18F-FDG-PET/CT is now an integral part of the workup and management of patients with Hodgkin's lymphoma (HL). PET/CT is currently routinely performed for staging and for response assessment at the end of treatment. Interim PET/CT is typically performed after 1-4 of 6-8 chemo/chemoimmunotherapy cycles ± radiation for prognostication and potential treatment escalation or de-escalation early in the course of therapy, a concept known as response-or risk-adapted treatment. Quantitative PET is an area of growing interest. Metrics such as the standardized uptake value (SUV), metabolic tumor volume, total lesion glycolysis, and their changes with treatment are being investigated as more reproducible and, potentially, more accurate predictors of response and prognosis. Despite the progress made in standardizing the use of PET/CT in lymphoma, challenges remain, particularly with respect to its limited positive predictive value. This review highlights the most relevant applications of PET/CT in HL, its strengths and limitations, as well as recent efforts to implement PET/CT-based metrics as promising tools for precision medicine. Finally, the value of PET/CT for response assessment to immunotherapy is discussed.
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Affiliation(s)
- Akram Al-Ibraheem
- Department of Nuclear Medicine, King Hussein Cancer Center, Amman, Jordan; Division of Nuclear Medicine/Department of Radiology and Nuclear Medicine, University of Jordan, Amman, Jordan
| | - Felix M Mottaghy
- Department of Nuclear Medicine, University Hospital RWTH, Aachen University, Aachen, 52074, Germany, Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD), Aachen, Germany and Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
| | - Malik E Juweid
- Division of Nuclear Medicine/Department of Radiology and Nuclear Medicine, University of Jordan, Amman, Jordan
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10
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Albano D, Treglia G, Dondi F, Calabrò A, Rizzo A, Annunziata S, Guerra L, Morbelli S, Tucci A, Bertagna F. 18F-FDG PET/CT Maximum Tumor Dissemination (Dmax) in Lymphoma: A New Prognostic Factor? Cancers (Basel) 2023; 15:cancers15092494. [PMID: 37173962 PMCID: PMC10177347 DOI: 10.3390/cancers15092494] [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: 03/31/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Recently, several studies introduced the potential prognostic usefulness of maximum tumor dissemination (Dmax) measured by 2-deoxy-2-fluorine-18-fluoro-D-glucose positron-emission tomography/computed tomography (18F-FDG PET/CT). Dmax is a simple three-dimensional feature that represents the maximal distance between the two farthest hypermetabolic PET lesions. A comprehensive computer literature search of PubMed/MEDLINE, Embase, and Cochrane libraries was conducted, including articles indexed up to 28 February 2023. Ultimately, 19 studies analyzing the value of 18F-FDG PET/CT Dmax in patients with lymphomas were included. Despite their heterogeneity, most studies showed a significant prognostic role of Dmax in predicting progression-free survival (PFS) and overall survival (OS). Some articles showed that the combination of Dmax with other metabolic features, such as MTV and interim PET response, proved to better stratify the risk of relapse or death. However, some methodological open questions need to be clarified before introducing Dmax into clinical practice.
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Affiliation(s)
- Domenico Albano
- Division of Nuclear Medicine, Università degli Studi di Brescia, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Giorgio Treglia
- Clinic of Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6501 Bellinzona, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, 1011 Lausanne, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
| | - Francesco Dondi
- Division of Nuclear Medicine, Università degli Studi di Brescia, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Anna Calabrò
- Division of Nuclear Medicine, Università degli Studi di Brescia, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Alessio Rizzo
- Department of Nuclear Medicine, Candiolo Cancer Institute, FPO-IRCCS, 10060 Turin, Italy
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy
| | - Luca Guerra
- Nuclear Medicine Division, Ospedale San Gerardo, 20900 Monza, Italy
| | - Silvia Morbelli
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | | | - Francesco Bertagna
- Division of Nuclear Medicine, Università degli Studi di Brescia, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
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11
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Triumbari EKA, Gatta R, Maiolo E, De Summa M, Boldrini L, Mayerhoefer ME, Hohaus S, Nardo L, Morland D, Annunziata S. Baseline 18F-FDG PET/CT Radiomics in Classical Hodgkin's Lymphoma: The Predictive Role of the Largest and the Hottest Lesions. Diagnostics (Basel) 2023; 13:1391. [PMID: 37189492 PMCID: PMC10137254 DOI: 10.3390/diagnostics13081391] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/06/2023] [Accepted: 04/08/2023] [Indexed: 05/17/2023] Open
Abstract
This study investigated the predictive role of baseline 18F-FDG PET/CT (bPET/CT) radiomics from two distinct target lesions in patients with classical Hodgkin's lymphoma (cHL). cHL patients examined with bPET/CT and interim PET/CT between 2010 and 2019 were retrospectively included. Two bPET/CT target lesions were selected for radiomic feature extraction: Lesion_A, with the largest axial diameter, and Lesion_B, with the highest SUVmax. Deauville score at interim PET/CT (DS) and 24-month progression-free-survival (PFS) were recorded. Mann-Whitney test identified the most promising image features (p < 0.05) from both lesions with regards to DS and PFS; all possible radiomic bivariate models were then built through a logistic regression analysis and trained/tested with a cross-fold validation test. The best bivariate models were selected based on their mean area under curve (mAUC). A total of 227 cHL patients were included. The best models for DS prediction had 0.78 ± 0.05 maximum mAUC, with a predominant contribution of Lesion_A features to the combinations. The best models for 24-month PFS prediction reached 0.74 ± 0.12 mAUC and mainly depended on Lesion_B features. bFDG-PET/CT radiomic features from the largest and hottest lesions in patients with cHL may provide relevant information in terms of early response-to-treatment and prognosis, thus representing an earlier and stronger decision-making support for therapeutic strategies. External validations of the proposed model are planned.
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Affiliation(s)
- Elizabeth Katherine Anna Triumbari
- Section of Nuclear Medicine, Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy;
- Department of Radiology, UC Davis, Sacramento, CA 95817, USA;
| | - Roberto Gatta
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy;
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
- Radiomics, Dipartimento di Radiologia, Radioterapia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Roma, Italy;
| | - Elena Maiolo
- Ematologia, Dipartimento di Radiologia, Radioterapia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Roma, Italy;
| | - Marco De Summa
- Medipass S.p.a. Integrative Service PET/CT–Radiofarmacy TracerGLab, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Roma, Italy;
| | - Luca Boldrini
- Radiomics, Dipartimento di Radiologia, Radioterapia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Roma, Italy;
| | - Marius E. Mayerhoefer
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Wien, Austria;
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Stefan Hohaus
- Ematologia, Dipartimento di Radiologia, Radioterapia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Roma, Italy;
- Hematology Section, Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, 00168 Roma, Italy
| | - Lorenzo Nardo
- Department of Radiology, UC Davis, Sacramento, CA 95817, USA;
| | - David Morland
- Unità di Medicina Nucleare, GSTeP Radiofarmacia, TracerGLab, Dipartimento di Radiologia, Radioterapia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Roma, Italy;
- Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- CReSTIC EA 3804 et Laboratoire de Biophysique, Université de Reims Champagne-Ardenne, 51100 Reims, France
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, GSTeP Radiofarmacia, TracerGLab, Dipartimento di Radiologia, Radioterapia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Roma, Italy;
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12
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Ortega C, Eshet Y, Prica A, Anconina R, Johnson S, Constantini D, Keshavarzi S, Kulanthaivelu R, Metser U, Veit-Haibach P. Combination of FDG PET/CT Radiomics and Clinical Parameters for Outcome Prediction in Patients with Hodgkin’s Lymphoma. Cancers (Basel) 2023; 15:cancers15072056. [PMID: 37046717 PMCID: PMC10093084 DOI: 10.3390/cancers15072056] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/06/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
Purpose: The aim of the study is to evaluate the prognostic value of a joint evaluation of PET and CT radiomics combined with standard clinical parameters in patients with HL. Methods: Overall, 88 patients (42 female and 46 male) with a median age of 43.3 (range 21–85 years) were included. Textural analysis of the PET/CT images was performed using freely available software (LIFE X). 65 radiomic features (RF) were evaluated. Univariate and multivariate models were used to determine the value of clinical characteristics and FDG PET/CT radiomics in outcome prediction. In addition, a binary logistic regression model was used to determine potential predictors for radiotherapy treatment and odds ratios (OR), with 95% confidence intervals (CI) reported. Features relevant to survival outcomes were assessed using Cox proportional hazards to calculate hazard ratios with 95% CI. Results: albumin (p = 0.034) + ALP (p = 0.028) + CT radiomic feature GLRLM GLNU mean (p = 0.012) (Area under the curve (AUC): 95% CI (86.9; 100.0)—Brier score: 3.9, 95% CI (0.1; 7.8) remained significant independent predictors for PFS outcome. PET-SHAPE Sphericity (p = 0.033); CT grey-level zone length matrix with high gray-level zone emphasis (GLZLM SZHGE mean (p = 0.028)); PARAMS XSpatial Resampling (p = 0.0091) as well as hemoglobin results (p = 0.016) remained as independent factors in the final model for a binary outcome as predictors of the need for radiotherapy (AUC = 0.79). Conclusion: We evaluated the value of baseline clinical parameters as well as combined PET and CT radiomics in HL patients for survival and the prediction of the need for radiotherapy treatment. We found that different combinations of all three factors/features were independently predictive of the here evaluated endpoints.
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13
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Keijzer K, Niezink AG, de Boer JW, van Doesum JA, Noordzij W, van Meerten T, van Dijk LV. Semi-automated 18F-FDG PET segmentation methods for tumor volume determination in Non-Hodgkin lymphoma patients: a literature review, implementation and multi-threshold evaluation. Comput Struct Biotechnol J 2023; 21:1102-1114. [PMID: 36789266 PMCID: PMC9900370 DOI: 10.1016/j.csbj.2023.01.023] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 01/21/2023] Open
Abstract
In the treatment of Non-Hodgkin lymphoma (NHL), multiple therapeutic options are available. Improving outcome predictions are essential to optimize treatment. The metabolic active tumor volume (MATV) has shown to be a prognostic factor in NHL. It is usually retrieved using semi-automated thresholding methods based on standardized uptake values (SUV), calculated from 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) images. However, there is currently no consensus method for NHL. The aim of this study was to review literature on different segmentation methods used, and to evaluate selected methods by using an in house created software tool. A software tool, MUltiple SUV Threshold (MUST)-segmenter was developed where tumor locations are identified by placing seed-points on the PET images, followed by subsequent region growing. Based on a literature review, 9 SUV thresholding methods were selected and MATVs were extracted. The MUST-segmenter was utilized in a cohort of 68 patients with NHL. Differences in MATVs were assessed with paired t-tests, and correlations and distributions figures. High variability and significant differences between the MATVs based on different segmentation methods (p < 0.05) were observed in the NHL patients. Median MATVs ranged from 35 to 211 cc. No consensus for determining MATV is available based on the literature. Using the MUST-segmenter with 9 selected SUV thresholding methods, we demonstrated a large and significant variation in MATVs. Identifying the most optimal segmentation method for patients with NHL is essential to further improve predictions of toxicity, response, and treatment outcomes, which can be facilitated by the MUST-segmenter.
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Key Words
- 18F-FDG PET
- AT, adaptive thresholding methods
- CAR, chimeric antigen receptor
- CT, computed tomography
- DICOM, Digital Imaging and Communications in Medicine
- DLBCL, Diffuse large B-cell lymphoma
- EANM, European Association of Nuclear Medicine
- EARL, EANM Research Ltd.
- FDG, fluorodeoxyglucose
- HL, Hodgkin lymphoma
- IMG, robustness across image reconstruction methods
- IQR, interquartile range
- LBCL, Large B-cell lymphoma
- LDH, lactate dehydrogenase
- MAN, clinician based evaluation using manual segmentations
- MATV, Metabolic active tumor volume
- MIP, Maximum Intensity Projection
- MUST, Multiple SUV Thresholding
- Metabolic tumor volume
- NHL, Non-Hodgkin lymphoma
- Non-Hodgkin lymphoma
- OBS, robustness across observers
- OS, overall survival
- PD-L1, programmed cell death ligand-1
- PET segmentation
- PET, positron emission tomography
- PFS, progression free survival
- PROG, progression vs non-progression
- PTCL, Peripheral T-cell lymphoma
- PTLD, Post-transplant lymphoproliferative disorder
- QS, quality scores
- SOFT, robustness across software
- SUV thresholding
- SUV, standardized uptake value
- Segmentation software
- TCL, T-cell lymphoma
- UMCG, University Medical Center Groningen
- VOI, volume of interest
- cc, cubic centimeter
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Affiliation(s)
- Kylie Keijzer
- Department of Hematology, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands,Department of Radiation Oncology, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Anne G.H. Niezink
- Department of Radiation Oncology, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Janneke W. de Boer
- Department of Hematology, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Jaap A. van Doesum
- Department of Hematology, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Walter Noordzij
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Tom van Meerten
- Department of Hematology, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Lisanne V. van Dijk
- Department of Radiation Oncology, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands,Corresponding author.
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14
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Lue KH, Chen YH, Wu YF, Liu SH. Influence of the methodological aspects of the dichotomization of total metabolic tumor volume measured through baseline fluorine-18 fluorodeoxyglucose PET on survival prediction in lymphoma. Nucl Med Commun 2023; 44:74-80. [PMID: 36514929 DOI: 10.1097/mnm.0000000000001640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE The total metabolic tumor volume (TMTV) measured from fluorine-18 fluorodeoxyglucose (18F-FDG) PET can be useful for determining the prognosis of patients with lymphoma. Stratifying patients into high- and low-TMTV risk groups requires a cutoff point, which is determined through the dichotomization method. This study investigated whether different TMTV dichotomization methods influenced survival prediction in patients with lymphoma. METHODS We retrospectively enrolled 129 patients with lymphoma who had undergone baseline 18F-FDG PET. TMTV was calculated using a fixed standardized uptake value threshold of 4.0. A total of six methods were employed to determine the optimal TMTV cutoff point using receiver-operating characteristic curve analyses, X-Tile bioinformatics software, and the Cutoff Finder web application. The prognostic performance of each method in survival prediction was examined. RESULTS The median (interquartile range) TMTV was 123 cm3 (21-335 cm3). The optimal TMTV cutoff values for predicting progression-free survival (PFS) and overall survival (OS) were in the range of 144-748 cm3. The cutoff points were used to dichotomize patients into two groups with distinct prognoses. All TMTV dichotomizations were significantly predictive of PFS and OS. The survival curves showed significant differences between the high- and low-TMTV groups. The C-indices of the survival models did not significantly differ in any of the dichotomizations. CONCLUSION The prognostic significance of TMTV was maintained regardless of the methodological aspects of dichotomization. However, the optimal TMTV cutoff point varied according to the chosen dichotomization method. Care should be taken when establishing an optimal TMTV cutoff point for clinical use.
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Affiliation(s)
- Kun-Han Lue
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology
| | - Yu-Hung Chen
- School of Medicine, College of Medicine, Tzu Chi University
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation
| | - Yi-Feng Wu
- School of Medicine, College of Medicine, Tzu Chi University
- Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Shu-Hsin Liu
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation
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15
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Driessen J, Kersten MJ, Visser L, van den Berg A, Tonino SH, Zijlstra JM, Lugtenburg PJ, Morschhauser F, Hutchings M, Amorim S, Gastinne T, Nijland M, Zwezerijnen GJC, Boellaard R, de Vet HCW, Arens AIJ, Valkema R, Liu RDK, Drees EEE, de Jong D, Plattel WJ, Diepstra A. Prognostic value of TARC and quantitative PET parameters in relapsed or refractory Hodgkin lymphoma patients treated with brentuximab vedotin and DHAP. Leukemia 2022; 36:2853-2862. [PMID: 36241696 DOI: 10.1038/s41375-022-01717-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 08/20/2022] [Accepted: 09/26/2022] [Indexed: 11/08/2022]
Abstract
Risk-stratified treatment strategies have the potential to increase survival and lower toxicity in relapsed/refractory classical Hodgkin lymphoma (R/R cHL) patients. This study investigated the prognostic value of serum (s)TARC, vitamin D and lactate dehydrogenase (LDH), TARC immunohistochemistry and quantitative PET parameters in 65 R/R cHL patients who were treated with brentuximab vedotin (BV) and DHAP followed by autologous stem-cell transplantation (ASCT) within the Transplant BRaVE study (NCT02280993). At a median follow-up of 40 months, the 3-year progression free survival (PFS) was 77% (95% CI: 67-88%) and the overall survival was 95% (90-100%). Significant adverse prognostic markers for progression were weak/negative TARC staining of Hodgkin Reed-Sternberg cells in the baseline biopsy, and a high standard uptake value (SUV)mean or SUVpeak on the baseline PET scan. After one cycle of BV-DHAP, sTARC levels were strongly associated with the risk of progression using a cutoff of 500 pg/ml. On the pre-ASCT PET scan, SUVpeak was highly prognostic for progression post-ASCT. Vitamin D, LDH and metabolic tumor volume had low prognostic value. In conclusion, we established the prognostic impact of sTARC, TARC staining, and quantitative PET parameters for R/R cHL, allowing the use of these parameters in prospective risk-stratified clinical trials. Trial registration: NCT02280993.
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Affiliation(s)
- Julia Driessen
- Department of Hematology, Amsterdam UMC, University of Amsterdam, LYMMCARE, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Marie José Kersten
- Department of Hematology, Amsterdam UMC, University of Amsterdam, LYMMCARE, Cancer Center Amsterdam, Amsterdam, The Netherlands.
| | - Lydia Visser
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anke van den Berg
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sanne H Tonino
- Department of Hematology, Amsterdam UMC, University of Amsterdam, LYMMCARE, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Pieternella J Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | | | | | - Sandy Amorim
- Department of Hematology, Hopital Saint Louis, Paris, France
| | - Thomas Gastinne
- Department of Hematology, Centre Hospitalier Universitaire, Nantes, France
| | - Marcel Nijland
- Department of Hematology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Gerben J C Zwezerijnen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Henrica C W de Vet
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Anne I J Arens
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Roelf Valkema
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Roberto D K Liu
- Department of Hematology, Amsterdam UMC, University of Amsterdam, LYMMCARE, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Esther E E Drees
- Department of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Daphne de Jong
- Department of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Wouter J Plattel
- Department of Hematology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Arjan Diepstra
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
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16
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Gong H, Tang B, Li T, Li J, Tang L, Ding C. The added prognostic values of baseline PET dissemination parameter in patients with angioimmunoblastic T-cell lymphoma. EJHaem 2022; 4:67-77. [PMID: 36819177 PMCID: PMC9928789 DOI: 10.1002/jha2.610] [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] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/13/2022] [Accepted: 10/20/2022] [Indexed: 11/30/2022]
Abstract
To explore the prognostic values of baseline 2-deoxy-2-[18F] fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) dissemination parameter in angioimmunoblastic T-cell lymphoma (AITL) and its added values to total metabolic tumour volume (TMTV). Eighty-one AITL patients with at least two FDG-avid lesions in baseline PET/CT were retrospectively included. PET parameters including TMTV and the distance between the two lesions that are the furthest apart (Dmax) were obtained. Univariate Cox analysis showed that both Dmax and TMTV were risk factors for progression-free survival (PFS) and overall survival (OS). Multivariate Cox analysis models of different combinations showed that high Dmax (> 65.7 cm) could independently predict both PFS and OS, while high TMTV (>456.6 cm3) was only significant for OS. A concise PET model based on TMTV and Dmax can effectively risk-stratify patients. PFS and OS rates were significantly lower in patients with high Dmax and high TMTV than in patients with low Dmax and low TMTV (3-year PFS rate: 15.0% vs. 48.7%, p = 0.001; 3-year OS rate: 27.6% vs. 79.0%, p < 0.001). Dmax can directly reflect the disease dissemination characteristic and has a significant prognostic value for FDG-avid AITL patients. It has the potential to be introduced into new risk stratification models for tailored treatment.
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Affiliation(s)
- Huanyu Gong
- Department of Nuclear MedicineJiangsu Province HospitalThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Bo Tang
- Department of RadiologyShuyang Hospital of Traditional Chinese MedicineSuqianChina
| | - Tiannv Li
- Department of Nuclear MedicineJiangsu Province HospitalThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Jianyong Li
- Department of HematologyJiangsu Province HospitalThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Lijun Tang
- Department of Nuclear MedicineJiangsu Province HospitalThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Chongyang Ding
- Department of Nuclear MedicineJiangsu Province HospitalThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
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17
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Lopci E, Elia C, Catalfamo B, Burnelli R, De Re V, Mussolin L, Piccardo A, Cistaro A, Borsatti E, Zucchetta P, Bianchi M, Buffardi S, Farruggia P, Garaventa A, Sala A, Vinti L, Mauz-Koerholz C, Mascarin M. Prospective Evaluation of Different Methods for Volumetric Analysis on [ 18F]FDG PET/CT in Pediatric Hodgkin Lymphoma. J Clin Med 2022; 11:jcm11206223. [PMID: 36294544 PMCID: PMC9605658 DOI: 10.3390/jcm11206223] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/27/2022] [Accepted: 10/17/2022] [Indexed: 11/30/2022] Open
Abstract
Rationale: Therapy response evaluation by 18F-fluorodeoxyglucose PET/CT (FDG PET) has become a powerful tool for the discrimination of responders from non-responders in pediatric Hodgkin lymphoma (HL). Recently, volumetric analyses have been regarded as a valuable tool for disease prognostication and biological characterization in cancer. Given the multitude of methods available for volumetric analysis in HL, the AIEOP Hodgkin Lymphoma Study Group has designed a prospective analysis of the Italian cohort enrolled in the EuroNet-PHL-C2 trial. Methods: Primarily, the study aimed to compare the different segmentation techniques used for volumetric assessment in HL patients at baseline (PET1) and during therapy: early (PET2) and late assessment (PET3). Overall, 50 patients and 150 scans were investigated for the current analysis. A dedicated software was used to semi-automatically delineate contours of the lesions by using different threshold methods. More specifically, four methods were applied: (1) fixed 41% threshold of the maximum standardized uptake value (SUVmax) within the respective lymphoma site (V41%), (2) fixed absolute SUV threshold of 2.5 (V2.5); (3) SUVmax(lesion)/SUVmean liver >1.5 (Vliver); (4) adaptive method (AM). All parameters obtained from the different methods were analyzed with respect to response. Results: Among the different methods investigated, the strongest correlation was observed between AM and Vliver (rho > 0.9; p < 0.001 for SUVmean, MTV and TLG at all scan timing), along with V2.5 and AM or Vliver (rho 0.98, p < 0.001 for TLG at baseline; rho > 0.9; p < 0.001 for SUVmean, MTV and TLG at PET2 and PET3, respectively). To determine the best segmentation method, we applied logistic regression and correlated different results with Deauville scores at late evaluation. Logistic regression demonstrated that MTV (metabolic tumor volume) and TLG (total lesion glycolysis) computation according to V2.5 and Vliver significantly correlated to response to treatment (p = 0.01 and 0.04 for MTV and 0.03 and 0.04 for TLG, respectively). SUVmean also resulted in significant correlation as absolute value or variation. Conclusions: The best correlation for volumetric analysis was documented for AM and Vliver, followed by V2.5. The volumetric analyses obtained from V2.5 and Vliver significantly correlated to response to therapy, proving to be preferred thresholds in our pediatric HL cohort.
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Affiliation(s)
- Egesta Lopci
- Nuclear Medicine Unit, IRCCS—Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Italy
- Correspondence: or
| | - Caterina Elia
- AYA and Pediatric Radiotherapy Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Barbara Catalfamo
- Nuclear Medicine Unit, University Hospital “Mater Domini, 88100 Catanzaro, Italy
| | - Roberta Burnelli
- Pediatric Onco-Hematologic Unit, University Hospital S. Anna, 44121 Ferrara, Italy
| | - Valli De Re
- Immunopathology and Cancer Biomarkers Unit, Department of Translational Research, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Lara Mussolin
- Pediatric Hemato-Oncology Clinic, Department of Women’s and Children’s Health, University of Padua, 35128 Padua, Italy
- Institute of Pediatric Research-Fondazione Città della Speranza, 35127 Padua, Italy
| | - Arnoldo Piccardo
- Department of Nuclear Medicine, Galliera Hospital, 16128 Genoa, Italy
| | - Angelina Cistaro
- Nuclear Medicine Division, Salus Alliance Medical, 16128 Genoa, Italy
| | - Eugenio Borsatti
- Nuclear Medicine Department, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Pietro Zucchetta
- Nuclear Medicine Department, Padova University Hospital, 35128 Padua, Italy
| | - Maurizio Bianchi
- Onco-Hematology Division, Regina Margherita Hospital, 10126 Torino, Italy
| | - Salvatore Buffardi
- Department of Oncology, Hospital Santobono-Pausilipon, 80123 Naples, Italy
| | - Piero Farruggia
- Department of Pediatric Onco-Hematology, A.R.N.A.S. Ospedali Civico, 90127 Palermo, Italy
| | - Alberto Garaventa
- Pediatric Oncology Unit, I RCCS G.Gaslini Hospital, 16147 Genoa, Italy
| | - Alessandra Sala
- Pediatric Division, Hospital San Gerardo, 20900 Monza, Italy
| | - Luciana Vinti
- Department of Pediatric Hematology and Oncology, Ospedale Bambino Gesù, IRCSS, 00165 Rome, Italy
| | - Christine Mauz-Koerholz
- Pädiatrische Hämatologie und Onkologie, Zentrum für Kinderheilkunde der Justus-Liebig-Universität Gießen, 35392 Giessen, Germany
- Medizinische Fakultät der Martin-Luther-Universität Halle-Wittenberg, 06120 Halle, Germany
| | - Maurizio Mascarin
- AYA and Pediatric Radiotherapy Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
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18
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Frood R, Clark M, Burton C, Tsoumpas C, Frangi AF, Gleeson F, Patel C, Scarsbrook A. Utility of pre-treatment FDG PET/CT-derived machine learning models for outcome prediction in classical Hodgkin lymphoma. Eur Radiol 2022; 32:7237-7247. [PMID: 36006428 PMCID: PMC9403224 DOI: 10.1007/s00330-022-09039-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 03/15/2022] [Revised: 06/13/2022] [Accepted: 07/16/2022] [Indexed: 12/22/2022]
Abstract
Objectives Relapse occurs in ~20% of patients with classical Hodgkin lymphoma (cHL) despite treatment adaption based on 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography/computed tomography response. The objective was to evaluate pre-treatment FDG PET/CT–derived machine learning (ML) models for predicting outcome in patients with cHL. Methods All cHL patients undergoing pre-treatment PET/CT at our institution between 2008 and 2018 were retrospectively identified. A 1.5 × mean liver standardised uptake value (SUV) and a fixed 4.0 SUV threshold were used to segment PET/CT data. Feature extraction was performed using PyRadiomics with ComBat harmonisation. Training (80%) and test (20%) cohorts stratified around 2-year event-free survival (EFS), age, sex, ethnicity and disease stage were defined. Seven ML models were trained and hyperparameters tuned using stratified 5-fold cross-validation. Area under the curve (AUC) from receiver operator characteristic analysis was used to assess performance. Results A total of 289 patients (153 males), median age 36 (range 16–88 years), were included. There was no significant difference between training (n = 231) and test cohorts (n = 58) (p value > 0.05). A ridge regression model using a 1.5 × mean liver SUV segmentation had the highest performance, with mean training, validation and test AUCs of 0.82 ± 0.002, 0.79 ± 0.01 and 0.81 ± 0.12. However, there was no significant difference between a logistic model derived from metabolic tumour volume and clinical features or the highest performing radiomic model. Conclusions Outcome prediction using pre-treatment FDG PET/CT–derived ML models is feasible in cHL patients. Further work is needed to determine optimum predictive thresholds for clinical use. Key points • A fixed threshold segmentation method led to more robust radiomic features. • A radiomic-based model for predicting 2-year event-free survival in classical Hodgkin lymphoma patients is feasible. • A predictive model based on ridge regression was the best performing model on our dataset. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-09039-0.
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Affiliation(s)
- Russell Frood
- Department of Nuclear Medicine, Leeds Teaching Hospitals NHS Trust, Leeds, UK. .,Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK. .,Leeds Institute of Health Research, University of Leeds, Leeds, UK.
| | - Matt Clark
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Cathy Burton
- Department of Haematology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Charalampos Tsoumpas
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center of Groningen, University of Groningen, Groningen, Netherlands.,Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Alejandro F Frangi
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.,Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing and School of Medicine, University of Leeds, Leeds, UK.,Medical Imaging Research Center (MIRC), University Hospital Gasthuisberg, KU Leuven, Leuven, Belgium
| | - Fergus Gleeson
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Chirag Patel
- Department of Nuclear Medicine, Leeds Teaching Hospitals NHS Trust, Leeds, UK.,Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Andrew Scarsbrook
- Department of Nuclear Medicine, Leeds Teaching Hospitals NHS Trust, Leeds, UK.,Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.,Leeds Institute of Health Research, University of Leeds, Leeds, UK
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19
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Drees EEE, Driessen J, Zwezerijnen GJC, Verkuijlen SAWM, Eertink JJ, van Eijndhoven MAJ, Groenewegen NJ, Vallés‐Martí A, de Jong D, Boellaard R, de Vet HCW, Pegtel DM, Zijlstra JM. Blood-circulating EV-miRNAs, serum TARC, and quantitative FDG-PET features in classical Hodgkin lymphoma. EJHaem 2022; 3:908-912. [PMID: 36051072 PMCID: PMC9422001 DOI: 10.1002/jha2.432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 03/20/2022] [Accepted: 03/23/2022] [Indexed: 05/27/2023]
Abstract
Blood-based biomarkers are gaining interest for response evaluation in classical Hodgkin lymphoma (cHL). However, it is unknown how blood-based biomarkers relate to quantitative 18F-FDG-PET features. We correlated extracellular vesicle-associated miRNAs (EV-miRNA), serum TARC, and complete blood count (CBC) with PET features (e.g., metabolic tumor volume [MTV], dissemination and intensity features) in 30 cHL patients at baseline. EV-miR127-3p, EV-miR24-3p, sTARC, and several CBC parameters showed weak to strong correlations with MTV and dissemination features, but not with intensity features. Two other EV-miRNAs only showed weak correlations with PET features. Therefore, blood-based biomarkers may be complementary to PET features, which warrants further exploration of combining these biomarkers in prognostic models.
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Affiliation(s)
- Esther E. E. Drees
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of PathologyBoelelaanAmsterdamThe Netherlands
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
| | - Julia Driessen
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
- Amsterdam UMClocation University of AmsterdamDepartment of HematologyLYMMCARE (Lymphoma and Myeloma Center)MeibergdreefAmsterdamThe Netherlands
| | - Gerben J. C. Zwezerijnen
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of Radiology and Nuclear MedicineBoelelaanAmsterdamThe Netherlands
| | - Sandra A. W. M. Verkuijlen
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of PathologyBoelelaanAmsterdamThe Netherlands
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
| | - Jakoba J. Eertink
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of HematologyBoelelaanAmsterdamThe Netherlands
| | - Monique A. J. van Eijndhoven
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of PathologyBoelelaanAmsterdamThe Netherlands
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
| | - Nils J. Groenewegen
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of PathologyBoelelaanAmsterdamThe Netherlands
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
- Exbiome B.V.AmsterdamThe Netherlands
| | - Andrea Vallés‐Martí
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of PathologyBoelelaanAmsterdamThe Netherlands
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
| | - Daphne de Jong
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of PathologyBoelelaanAmsterdamThe Netherlands
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
| | - Ronald Boellaard
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of Radiology and Nuclear MedicineBoelelaanAmsterdamThe Netherlands
| | - Henrica C. W. de Vet
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of Epidemiology and Data ScienceAmsterdam Public Health research instituteBoelelaanAmsterdamThe Netherlands
| | - Dirk M. Pegtel
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of PathologyBoelelaanAmsterdamThe Netherlands
- Cancer Center AmsterdamImaging and BiomarkersAmsterdamThe Netherlands
- Exbiome B.V.AmsterdamThe Netherlands
| | - Josée M. Zijlstra
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of PathologyBoelelaanAmsterdamThe Netherlands
- Amsterdam UMCLocation Vrije Universiteit AmsterdamDepartment of HematologyBoelelaanAmsterdamThe Netherlands
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20
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Morland D, Triumbari EKA, Maiolo E, Cuccaro A, Treglia G, Hohaus S, Annunziata S. Healthy Organs Uptake on Baseline 18F-FDG PET/CT as an Alternative to Total Metabolic Tumor Volume to Predict Event-Free Survival in Classical Hodgkin's Lymphoma. Front Med (Lausanne) 2022; 9:913866. [PMID: 35814740 PMCID: PMC9256984 DOI: 10.3389/fmed.2022.913866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeHealthy organs uptake, including cerebellar and liver SUVs have been reported to be inversely correlated to total metabolic tumor volume (TMTV), a controversial predictor of event-free survival (EFS) in classical Hodgkin's Lymphoma (cHL). The objective of this study was to estimate TMTV by using healthy organs SUV measurements and assess the performance of this new index (UF, Uptake Formula) to predict EFS in cHL.MethodsPatients with cHL were retrospectively included. SUV values and TMTV derived from baseline 18F-FDG PET/CT were harmonized using ComBat algorithm across PET/CT systems. UF was estimated using ANOVA analysis. Optimal thresholds of TMTV and UF were calculated and tested using Cox models.Results163 patients were included. Optimal UF model of TMTV included age, lymphoma maximum SUVmax, hepatic SUVmean and cerebellar SUVmax (R2 14.0% - p < 0.001). UF > 236.8 was a significant predictor of EFS (HR: 2.458 [1.201–5.030], p = 0.01) and was not significantly different from TMTV > 271.0 (HR: 2.761 [1.183–5.140], p = 0.001). UF > 236.8 remained significant in a bivariate model including IPS score (p = 0.02) and determined two populations with different EFS (63.7 vs. 84.9%, p = 0.01).ConclusionThe Uptake Formula, a new index including healthy organ SUV values, shows similar performance to TMTV in predicting EFS in Hodgkin's Lymphoma. Validation cohorts will be needed to confirm this new prognostic parameter.
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Affiliation(s)
- David Morland
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
- Service de Médecine Nucléaire, Institut Godinot, Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l'Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, Reims, France
- *Correspondence: David Morland
| | - Elizabeth Katherine Anna Triumbari
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Elena Maiolo
- Unità di Ematologia, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Annarosa Cuccaro
- Unità di Ematologia, ASL Toscana N/O Spedali Riuniti Livorno, Livorno, Italy
| | - Giorgio Treglia
- Clinic of Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Stefan Hohaus
- Unità di Ematologia, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
- Section of Hematology, Department of Radiological Sciences, Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
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