1
|
Gui J, Li M, Xu J, Zhang X, Mei H, Lan X. [ 18F]FDG PET/CT for prognosis and toxicity prediction of diffuse large B-cell lymphoma patients with chimeric antigen receptor T-cell therapy. Eur J Nucl Med Mol Imaging 2024; 51:2308-2319. [PMID: 38467921 DOI: 10.1007/s00259-024-06667-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 02/25/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE Chimeric antigen receptor (CAR) T-cell therapy has been confirmed to benefit patients with relapsed and/or refractory diffuse large B-cell lymphoma (DLBCL). It is important to provide precise and timely predictions of the efficacy and toxicity of CAR T-cell therapy. In this study, we evaluated the value of [18F]fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) combining with clinical indices and laboratory indicators in predicting outcomes and toxicity of anti-CD19 CAR T-cell therapy for DLBCL patients. METHODS Thirty-eight DLBCL patients who received CAR T-cell therapy and underwent [18F]FDG PET/CT within 3 months before (pre-infusion) and 1 month after CAR T-cell infusion (M1) were retrospectively reviewed and regularly followed up. Maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), metabolic tumor volume (MTV), clinical indices, and laboratory indicators were recorded at pre-infusion and M1 time points, and changes in these indices were calculated. Progression-free survival (PFS) and overall survival (OS) were as endpoints. Based on the multivariate Cox regression analysis, two predictive models for PFS and OS were developed and evaluated the efficiency. Pre-infusion indices were subjected to predict the grade of cytokine release syndrome (CRS) resulting from toxic reactions. RESULTS For survival analysis at a median follow-up time of 18.2 months, patients with values of international prognostic index (IPI), SUVmax at M1, and TLG at M1 above their optimal thresholds had a shorter PFS (median PFS: 8.1 months [IPI ≥ 2] vs. 26.2 months [IPI < 2], P = 0.025; 3.1 months [SUVmax ≥ 5.69] vs. 26.8 months [SUVmax < 5.69], P < 0.001; and 3.1 months [TLG ≥ 23.79] vs. 26.8 months [TLG < 23.79], P < 0.001). In addition, patients with values of SUVmax at M1 and ∆SUVmax% above their optimal thresholds had a shorter OS (median OS: 12.6 months [SUVmax ≥ 15.93] vs. 'not reached' [SUVmax < 15.93], P < 0.001; 32.5 months [∆SUVmax% ≥ -46.76] vs. 'not reached' [∆SUVmax% < -46.76], P = 0.012). Two novel predictive models for PFS and OS were visualized using nomogram. The calibration analysis and the decision curves demonstrated good performance of the models. Spearman's rank correlation (rs) analysis revealed that the CRS grade correlated strongly with the pre-infusion SUVmax (rs = 0.806, P < 0.001) and moderately with the pre-infusion TLG (rs = 0.534, P < 0.001). Multinomial logistic regression analysis revealed that the pre-infusion value of SUVmax correlated with the risk of developing a higher grade of CRS (P < 0.001). CONCLUSION In this group of DLBCL patients who underwent CAR T-cell therapy, SUVmax at M1, TLG at M1, and IPI were independent risk factors for PFS, and SUVmax at M1 and ∆SUVmax% for OS. Based on these indicators, two novel predictive models were established and verified the efficiency for evaluating PFS and OS. Moreover, pre-infusion SUVmax correlated with the severity of any subsequent CRS. We conclude that metabolic parameters measured using [18F]FDG PET/CT can identify DLBCL patients who will benefit most from CAR T-cell therapy, and the value before CAR T-cell infusion may predict its toxicity in advance.
Collapse
Affiliation(s)
- Jinbo Gui
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China
- Hubei Key Laboratory of Molecular Imaging, Wuhan, 430022, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, Hubei, 430022, China
| | - Mengting Li
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China
- Hubei Key Laboratory of Molecular Imaging, Wuhan, 430022, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, Hubei, 430022, China
| | - Jia Xu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China
| | - Xiao Zhang
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China
- Hubei Key Laboratory of Molecular Imaging, Wuhan, 430022, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, Hubei, 430022, China
| | - Heng Mei
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, Hubei, 430022, China.
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China.
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China.
| | - Xiaoli Lan
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China.
- Hubei Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, Hubei, 430022, China.
| |
Collapse
|
2
|
Withofs N, Bonnet C, Hustinx R. 2-deoxy-2-[ 18F]FDG PET Imaging for Therapy Assessment in Hodgkin's and Non-Hodgkin Lymphomas. PET Clin 2024:S1556-8598(24)00047-6. [PMID: 38945737 DOI: 10.1016/j.cpet.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography combined with computed tomography (PET/CT) has contributed to outcome improvement of patients with lymphoma. The use of [18F]FDG PET/CT for staging and response assessment is successfully applied both in routine clinical practice and in clinical trials. The challenges lie in enhancing the outcomes of lymphoma patients, particularly those with advanced or refractory/relapsed disease, and to minimize the long-term toxicity associated with treatments, including radiation therapy. The objective of this review article is to present contemporary data on the use of [18F]FDG PET/CT for treatment assessment of aggressive lymphomas.
Collapse
Affiliation(s)
- Nadia Withofs
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Quartier Hopital, Avenue de l'hopital 1, Liege, Belgium; GIGA-Nuclear Medicine Lab, University of Liege, CHU - B34 Quartier Hôpital, Avenue de l'Hôpital 11, Liège, BELGIQUE.
| | - Christophe Bonnet
- Department of Hematology, CHU of Liege, Quartier Hôpital, Avenue de l'hôpital 1, 4000 Liege 1, Belgium
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Quartier Hopital, Avenue de l'hopital 1, Liege, Belgium; GIGA-Nuclear Medicine Lab, University of Liege, CHU - B34 Quartier Hôpital, Avenue de l'Hôpital 11, Liège, BELGIQUE
| |
Collapse
|
3
|
Marchiori S, Cousin F, Papadopoulos I, Bernard C, Thys M, De Prijck B, Pirotte M, Donneau AF, Hustinx R, Caers J, Withofs N. Prognostic value of visual IMPeTUs criteria and metabolic tumor burden at baseline [ 18F]FDG PET/CT in patients with newly diagnosed multiple myeloma. EJNMMI Res 2024; 14:51. [PMID: 38806885 PMCID: PMC11133264 DOI: 10.1186/s13550-024-01113-6] [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: 04/05/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography combined with low-dose computed tomography (PET/CT) can be used at diagnosis to identify myeloma-defining events and also provides prognostic factors. The aim of this study was to assess the prognostic significance of baseline [18F]FDG PET/CT visual IMPeTUs (Italian myeloma criteria for PET Use)-based parameters and/or total metabolic tumor volume (TMTV) in a single-center population of patients with newly diagnosed multiple myeloma (NDMM) eligible for transplantation. METHODS Patients with MM who underwent a baseline [18F]FDG PET/CT were retrospectively selected from a large internal database of the University Hospital of Liege (Liege, Belgium). Initially, all PET/CT images were visually analyzed using IMPeTUs criteria, followed by delineation of TMTV using a semi-automatic lesion delineation workflow, including [18F]FDG-positive MM focal lesions (FL) with an absolute SUV threshold set at 4.0. In a first step, to ensure PET/CT scans accurate reporting, the agreement between two nuclear medicine physicians with distinct experience was assessed. In the second step, univariable and multivariable analyses were conducted to determine the prognostic significance of [18F]FDG PET/CT parameters on progression free survival (PFS) and overall survival (OS), respectively. RESULTS A total of 40 patients with NDMM were included in the study. The observers agreement in the analysis [18F]FDG PET/CT images was substantial for the presence of spine FL, extra spine FL, at least one fracture and paramedullary disease (Cohen's kappa 0.79, 0.87, 0.75 and 0.64, respectively). For the presence of skull FL and extramedullary disease the agreement was moderate (Cohen's kappa 0.56 and 0.53, respectively). Among [18F]FDG PET/CT parameters, a high number of delineated volumes of interest (VOI) using the SUV4.0 threshold was the only independent prognostic factor associated with PFS [HR (95% CI): 1.03 (1.004-1.05), P = 0.019] while a high number of FL (n > 10; F group 4) was the only independent prognostic factor associated with OS [HR (95% CI): 19.10 (1.90-191.95), P = 0.01]. CONCLUSION Our work confirms the reproducibility IMPeTUs criteria. Furthermore, it demonstrates that a high number of FL (n > 10; IMPeTUs F group 4), reflecting a high [18F]FDG-avid tumor burden, is an independent prognostic factor for OS. The prognostic value of the TMTV delineated using a SUV4.0 threshold was not significant. Nevertheless, the count of delineated [18F]FDG-avid lesions VOI using a SUV4.0 threshold was an independent prognostic factor for PFS.
Collapse
Affiliation(s)
- Silvano Marchiori
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Liège, Belgium.
| | - François Cousin
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Liège, Belgium
| | - Iraklis Papadopoulos
- Biostatistics and Research Methods Center (B-STAT), University of Liege, Liège, Belgium
| | - Claire Bernard
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Liège, Belgium
| | - Marie Thys
- Medico-Economic Information Department / Data Analysis, CHU of Liège, Liège, Belgium
| | | | | | | | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Liège, Belgium
- GIGA-CRC in Vivo Imaging, University of Liege, Liège, Belgium
| | - Jo Caers
- Department of Hematology, CHU de Liège, Liège, Belgium
- Laboratory of Hematology, GIGA I3, University of Liège, Liège, Belgium
| | - Nadia Withofs
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Liège, Belgium
- GIGA-CRC in Vivo Imaging, University of Liege, Liège, Belgium
| |
Collapse
|
4
|
Sadik M, Barrington SF, Trägårdh E, Saboury B, Nielsen AL, Jakobsen AL, Gongora JLL, Urdaneta JL, Kumar R, Edenbrandt L. Metabolic tumour volume in Hodgkin lymphoma-A comparison between manual and AI-based analysis. Clin Physiol Funct Imaging 2024; 44:220-227. [PMID: 38011940 DOI: 10.1111/cpf.12868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 11/10/2023] [Accepted: 11/24/2023] [Indexed: 11/29/2023]
Abstract
AIM To compare total metabolic tumour volume (tMTV), calculated using two artificial intelligence (AI)-based tools, with manual segmentation by specialists as the reference. METHODS Forty-eight consecutive Hodgkin lymphoma (HL) patients staged with [18F] fluorodeoxyglucose positron emission tomography/computed tomography were included. The median age was 35 years (range: 7-75), 46% female. The tMTV was automatically measured using the AI-based tools positron emission tomography assisted reporting system (PARS) (from Siemens) and RECOMIA (recomia.org) without any manual adjustments. A group of eight nuclear medicine specialists manually segmented lesions for tMTV calculations; each patient was independently segmented by two specialists. RESULTS The median of the manual tMTV was 146 cm3 (interquartile range [IQR]: 79-568 cm3) and the median difference between two tMTV values segmented by different specialists for the same patient was 26 cm3 (IQR: 10-86 cm3). In 22 of the 48 patients, the manual tMTV value was closer to the RECOMIA tMTV value than to the manual tMTV value segmented by the second specialist. In 11 of the remaining 26 patients, the difference between the RECOMIA tMTV and the manual tMTV was small (<26 cm3, which was the median difference between two manual tMTV values from the same patient). The corresponding numbers for PARS were 18 and 10 patients, respectively. CONCLUSION The results of this study indicate that RECOMIA and Siemens PARS AI tools could be used without any major manual adjustments in 69% (33/48) and 58% (28/48) of HL patients, respectively. This demonstrates the feasibility of using AI tools to support physicians measuring tMTV for assessment of prognosis in clinical practice.
Collapse
Affiliation(s)
- May Sadik
- Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Sally F Barrington
- School of Biomedical Engineering and Imaging Sciences Kings College, King's College London and Guy's and St Thomas' PET Centre, London, UK
| | - Elin Trägårdh
- Departmet of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
| | - Anne L Nielsen
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Annika L Jakobsen
- Department of Clinical Physiology, Nuclear Medicine and PET, Centre of Diagnostic Investigations, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Jose L L Gongora
- Department of Diagnostic Imaging, Akershus University Hospital, Oslo, Norway
| | - Jesus L Urdaneta
- Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Rajender Kumar
- Department of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| |
Collapse
|
5
|
Voltin CA, Paccagnella A, Winkelmann M, Heger JM, Casadei B, Beckmann L, Herrmann K, Dekorsy FJ, Kutsch N, Borchmann P, Fanti S, Kunz WG, Subklewe M, Kobe C, Zinzani PL, Stelljes M, Roth KS, Drzezga A, Noppeney R, Rahbar K, Reinhardt HC, von Tresckow B, Seifert R, Albring JC, Blumenberg V, Farolfi A, Flossdorf S, Gödel P, Hanoun C. Multicenter development of a PET-based risk assessment tool for product-specific outcome prediction in large B-cell lymphoma patients undergoing CAR T-cell therapy. Eur J Nucl Med Mol Imaging 2024; 51:1361-1370. [PMID: 38114616 PMCID: PMC10957657 DOI: 10.1007/s00259-023-06554-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 11/30/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE The emergence of chimeric antigen receptor (CAR) T-cell therapy fundamentally changed the management of individuals with relapsed and refractory large B-cell lymphoma (LBCL). However, real-world data have shown divergent outcomes for the approved products. The present study therefore set out to evaluate potential risk factors in a larger cohort. METHODS Our analysis set included 88 patients, treated in four German university hospitals and one Italian center, who had undergone 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (PET) before CAR T-cell therapy with tisagenlecleucel or axicabtagene ciloleucel. We first determined the predictive value of conventional risk factors, treatment lines, and response to bridging therapy for progression-free survival (PFS) through forward selection based on Cox regression. In a second step, the additive potential of two common PET parameters was assessed. Their optimal dichotomizing thresholds were calculated individually for each CAR T-cell product. RESULTS Extra-nodal involvement emerged as the most relevant of the conventional tumor and patient characteristics. Moreover, we found that inclusion of metabolic tumor volume (MTV) further improves outcome prediction. The hazard ratio for a PFS event was 1.68 per unit increase of our proposed risk score (95% confidence interval [1.20, 2.35], P = 0.003), which comprised both extra-nodal disease and lymphoma burden. While the most suitable MTV cut-off among patients receiving tisagenlecleucel was 11 mL, a markedly higher threshold of 259 mL showed optimal predictive performance in those undergoing axicabtagene ciloleucel treatment. CONCLUSION Our analysis demonstrates that the presence of more than one extra-nodal lesion and higher MTV in LBCL are associated with inferior outcome after CAR T-cell treatment. Based on an assessment tool including these two factors, patients can be assigned to one of three risk groups. Importantly, as shown by our study, metabolic tumor burden might facilitate CAR T-cell product selection and reflect the individual need for bridging therapy.
Collapse
Affiliation(s)
- Conrad-Amadeus Voltin
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
| | - Andrea Paccagnella
- Department of Experimental, Diagnostic, and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Michael Winkelmann
- Department of Radiology, University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany
| | - Jan-Michel Heger
- Department of Internal Medicine I, Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Cologne Lymphoma Working Group (CLWG), Cologne, Germany
| | - Beatrice Casadei
- Department of Experimental, Diagnostic, and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
- 'L. e A. Seràgnoli' Institute of Hematology, Scientific Institute for Research, Hospitalization, and Healthcare (IRCCS) 'Azienda Ospedaliero-Universitaria Di Bologna', University of Bologna, Bologna, Italy
| | - Laura Beckmann
- Department of Internal Medicine I, Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- German Cancer Consortium (DKTK) Partner Site Essen/Düsseldorf, Essen, Germany
| | - Franziska J Dekorsy
- Department of Nuclear Medicine, University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany
| | - Nadine Kutsch
- Department of Internal Medicine I, Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Cologne Lymphoma Working Group (CLWG), Cologne, Germany
| | - Peter Borchmann
- Department of Internal Medicine I, Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Cologne Lymphoma Working Group (CLWG), Cologne, Germany
| | - Stefano Fanti
- Department of Experimental, Diagnostic, and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
- Division of Nuclear Medicine, Scientific Institute for Research, Hospitalization, and Healthcare (IRCCS) 'Azienda Ospedaliero-Universitaria Di Bologna', University of Bologna, Bologna, Italy
| | - Wolfgang G Kunz
- Department of Radiology, University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany
| | - Marion Subklewe
- Department of Medicine III, Comprehensive Cancer Center Munich (CCCM), University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany
- Laboratory for Translational Cancer Immunology, Gene Center Munich, Ludwig Maximilian University Munich, Munich, Germany
- German Cancer Consortium (DKTK) and Bavarian Center for Cancer Research (BZKF) Partner Site Munich, Munich, Germany
| | - Carsten Kobe
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Pier Luigi Zinzani
- Department of Experimental, Diagnostic, and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
- 'L. e A. Seràgnoli' Institute of Hematology, Scientific Institute for Research, Hospitalization, and Healthcare (IRCCS) 'Azienda Ospedaliero-Universitaria Di Bologna', University of Bologna, Bologna, Italy
| | - Matthias Stelljes
- Department of Medicine A-Hematology, Oncology, and Pneumology, West German Cancer Center (WTZ) Network Partner Site, University Hospital Münster, University of Münster, Münster, Germany
| | - Katrin S Roth
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Alexander Drzezga
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Richard Noppeney
- German Cancer Consortium (DKTK) Partner Site Essen/Düsseldorf, Essen, Germany
- Department of Hematology and Stem Cell Transplantation, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Kambiz Rahbar
- Department of Nuclear Medicine, University Hospital Münster, University of Münster, Münster, Germany
| | - H Christian Reinhardt
- German Cancer Consortium (DKTK) Partner Site Essen/Düsseldorf, Essen, Germany
- Department of Hematology and Stem Cell Transplantation, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Bastian von Tresckow
- German Cancer Consortium (DKTK) Partner Site Essen/Düsseldorf, Essen, Germany
- Department of Hematology and Stem Cell Transplantation, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Robert Seifert
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- German Cancer Consortium (DKTK) Partner Site Essen/Düsseldorf, Essen, Germany
- Department of Nuclear Medicine, University Hospital Münster, University of Münster, Münster, Germany
| | - Jörn C Albring
- Department of Medicine A-Hematology, Oncology, and Pneumology, West German Cancer Center (WTZ) Network Partner Site, University Hospital Münster, University of Münster, Münster, Germany
| | - Viktoria Blumenberg
- Department of Medicine III, Comprehensive Cancer Center Munich (CCCM), University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany
- Laboratory for Translational Cancer Immunology, Gene Center Munich, Ludwig Maximilian University Munich, Munich, Germany
- German Cancer Consortium (DKTK) and Bavarian Center for Cancer Research (BZKF) Partner Site Munich, Munich, Germany
| | - Andrea Farolfi
- Division of Nuclear Medicine, Scientific Institute for Research, Hospitalization, and Healthcare (IRCCS) 'Azienda Ospedaliero-Universitaria Di Bologna', University of Bologna, Bologna, Italy
| | - Sarah Flossdorf
- Institute for Medical Informatics, Biometry, and Epidemiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Philipp Gödel
- Department of Internal Medicine I, Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Cologne Lymphoma Working Group (CLWG), Cologne, Germany
| | - Christine Hanoun
- German Cancer Consortium (DKTK) Partner Site Essen/Düsseldorf, Essen, Germany
- Department of Hematology and Stem Cell Transplantation, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| |
Collapse
|
6
|
Yousefirizi F, Shiri I, O JH, Bloise I, Martineau P, Wilson D, Bénard F, Sehn LH, Savage KJ, Zaidi H, Uribe CF, Rahmim A. Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients. Phys Eng Sci Med 2024:10.1007/s13246-024-01408-x. [PMID: 38512435 DOI: 10.1007/s13246-024-01408-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 02/18/2024] [Indexed: 03/23/2024]
Abstract
Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data necessary for training. To overcome this limitation, this study explores semi-supervised approaches utilizing unlabeled data, specifically focusing on PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL) obtained from two centers. We considered 2-[18F]FDG PET images of 292 patients PMBCL (n = 104) and DLBCL (n = 188) (n = 232 for training and validation, and n = 60 for external testing). We harnessed classical wisdom embedded in traditional segmentation methods, such as the fuzzy clustering loss function (FCM), to tailor the training strategy for a 3D U-Net model, incorporating both supervised and unsupervised learning approaches. Various supervision levels were explored, including fully supervised methods with labeled FCM and unified focal/Dice loss, unsupervised methods with robust FCM (RFCM) and Mumford-Shah (MS) loss, and semi-supervised methods combining FCM with supervised Dice loss (MS + Dice) or labeled FCM (RFCM + FCM). The unified loss function yielded higher Dice scores (0.73 ± 0.11; 95% CI 0.67-0.8) than Dice loss (p value < 0.01). Among the semi-supervised approaches, RFCM + αFCM (α = 0.3) showed the best performance, with Dice score of 0.68 ± 0.10 (95% CI 0.45-0.77), outperforming MS + αDice for any supervision level (any α) (p < 0.01). Another semi-supervised approach with MS + αDice (α = 0.2) achieved Dice score of 0.59 ± 0.09 (95% CI 0.44-0.76) surpassing other supervision levels (p < 0.01). Given the time-consuming nature of manual delineations and the inconsistencies they may introduce, semi-supervised approaches hold promise for automating medical imaging segmentation workflows.
Collapse
Affiliation(s)
- Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Joo Hyun O
- College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | | | | | - Don Wilson
- BC Cancer, Vancouver, BC, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
| | | | - Laurie H Sehn
- BC Cancer, Vancouver, BC, Canada
- Centre for Lymphoid Cancer, BC Cancer, Vancouver, Canada
| | - Kerry J Savage
- BC Cancer, Vancouver, BC, Canada
- Centre for Lymphoid Cancer, BC Cancer, Vancouver, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- University Medical Center Groningen, University of Groningens, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Vancouver, Odense, Denmark
- University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - Carlos F Uribe
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- BC Cancer, Vancouver, BC, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- BC Cancer, Vancouver, BC, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
- Departments of Physics and Biomedical Engineering, University of British Columbia, Vancouver, Canada
| |
Collapse
|
7
|
Abenavoli EM, Linguanti F, Anichini M, Miele V, Mungai F, Palazzo M, Nassi L, Puccini B, Romano I, Sordi B, Sciagrà R, Simontacchi G, Vannucchi AM, Berti V. Texture analysis of 18F-FDG PET/CT and CECT: Prediction of refractoriness of Hodgkin lymphoma with mediastinal bulk involvement. Hematol Oncol 2024; 42:e3261. [PMID: 38454623 DOI: 10.1002/hon.3261] [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: 09/26/2023] [Revised: 01/18/2024] [Accepted: 02/22/2024] [Indexed: 03/09/2024]
Abstract
To recognize patients at high risk of refractory disease, the identification of novel prognostic parameters improving stratification of newly diagnosed Hodgkin Lymphoma (HL) is still needed. This study investigates the potential value of metabolic and texture features, extracted from baseline 18F-FDG Positron Emission Tomography/Computed Tomography (PET) and Contrast-Enhanced Computed Tomography scan (CECT), together with clinical data, in predicting first-line therapy refractoriness (R) of classical HL (cHL) with mediastinal bulk involvement. We reviewed 69 cHL patients who underwent staging PET and CECT. Lesion segmentation and texture parameter extraction were performed using the freeware software LIFEx 6.3. The prognostic significance of clinical and imaging features was evaluated in relation to the development of refractory disease. Receiver operating characteristic curve, Cox proportional hazard regression and Kaplan-Meier analyses were performed to examine the potential independent predictors and to evaluate their prognostic value. Among clinical characteristics, only stage according to the German Hodgkin Group (GHSG) classification system significantly differed between R and not-R. Among CECT variables, only parameters derived from second order matrices (gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) demonstrated significant prognostic power. Among PET variables, SUVmean, several variables derived from first (histograms, shape), and second order analyses (GLCM, GLRLM, NGLDM) exhibited significant predictive power. Such variables obtained accuracies greater than 70% at receiver operating characteristic analysis and their PFS curves resulted statistically significant in predicting refractoriness. At multivariate analysis, only HISTO_EntropyPET extracted from PET (HISTO_EntropyPET ) and GHSG stage resulted as significant independent predictors. Their combination identified 4 patient groups with significantly different PFS curves, with worst prognosis in patients with higher HISTO_EntropyPET values, regardless of the stage. Imaging radiomics may provide a reference for prognostic evaluation of patients with mediastinal bulky cHL. The best prognostic value in the prediction of R versus not-R disease was reached by combining HISTO_EntropyPET with GHSG stage.
Collapse
Affiliation(s)
- Elisabetta M Abenavoli
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
| | - Flavia Linguanti
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
| | - Matilde Anichini
- Department of Radiology, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Francesco Mungai
- Department of Radiology, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Marianna Palazzo
- Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Luca Nassi
- Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Benedetta Puccini
- Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Ilaria Romano
- Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Benedetta Sordi
- Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy
| | - Roberto Sciagrà
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
| | - Gabriele Simontacchi
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Alessandro M Vannucchi
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy
| | - Valentina Berti
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
| |
Collapse
|
8
|
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] [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
| |
Collapse
|
9
|
Alderuccio JP, Reis IM, Hamadani M, Nachiappan M, Leslom S, Kahl BS, Ai WZ, Radford J, Solh M, Ardeshna KM, Hess BT, Lunning MA, Zinzani PL, Stathis A, Carlo-Stella C, Lossos IS, Caimi PF, Han S, Yang F, Kuker RA, Moskowitz CH. PET/CT Biomarkers Enable Risk Stratification of Patients with Relapsed/Refractory Diffuse Large B-cell Lymphoma Enrolled in the LOTIS-2 Clinical Trial. Clin Cancer Res 2024; 30:139-149. [PMID: 37855688 PMCID: PMC10872617 DOI: 10.1158/1078-0432.ccr-23-1561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/11/2023] [Accepted: 10/17/2023] [Indexed: 10/20/2023]
Abstract
PURPOSE Significant progress has occurred in developing quantitative PET/CT biomarkers in diffuse large B-cell lymphoma (DLBCL). Total metabolic tumor volume (MTV) is the most extensively studied, enabling assessment of FDG-avid tumor burden associated with outcomes. However, prior studies evaluated the outcome of cytotoxic chemotherapy or chimeric antigen receptor T-cell therapy without data on recently approved FDA agents. Therefore, we aimed to assess the prognosis of PET/CT biomarkers in patients treated with loncastuximab tesirine. EXPERIMENTAL DESIGN We centrally reviewed screening PET/CT scans of patients with relapsed/refractory DLBCL enrolled in the LOTIS-2 (NCT03589469) study. MTV was obtained by computing individual volumes using the SUV ≥4.0 threshold. Other PET/CT metrics, clinical factors, and the International Metabolic Prognostic Index (IMPI) were evaluated. Logistic regression was used to assess the association between biomarkers and treatment response. Cox regression was used to determine the effect of biomarkers on time-to-event outcomes. We estimated biomarker prediction as continuous and binary variables defined by cutoff points. RESULTS Across 138 patients included in this study, MTV with a cutoff point of 96 mL was the biomarker associated with the highest predictive performance in univariable and multivariable models to predict failure to achieve complete metabolic response (OR, 5.42; P = 0.002), progression-free survival (HR, 2.68; P = 0.002), and overall survival (HR, 3.09; P < 0.0001). IMPI demonstrated an appropriate performance, however, not better than MTV alone. CONCLUSIONS Pretreatment MTV demonstrated robust risk stratification, with those patients demonstrating high MTV achieving lower responses and survival to loncastuximab tesirine in relapsed/refractory DLBCL.
Collapse
Affiliation(s)
- Juan Pablo Alderuccio
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Isildinha M. Reis
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Mehdi Hamadani
- Medical College of Wisconsin, Milwaukee, WI, United States
| | - Muthiah Nachiappan
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Salman Leslom
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Brad S. Kahl
- Washington University, St. Louis, MO, United States
| | - Weiyun Z. Ai
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, United States
| | - John Radford
- NIHR Clinical Research Facility, University of Manchester and the Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Melhem Solh
- Blood and Marrow Transplant Program at Northside Hospital, Atlanta, GA, United States
| | - Kirit M. Ardeshna
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Brian T. Hess
- Medical University of South Carolina, Charleston, SC, United States
| | - Matthew A. Lunning
- University of Nebraska Medical Center- Fred and Pamela Buffett Cancer Center, Omaha, NE, United States
| | - Pier Luigi Zinzani
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”; Dipartimento di Scienze Mediche e Chirurgiche, Università di Bologna, Bologna, Italy
| | - Anastasios Stathis
- Oncology Institute of Southern Switzerland, EOC, Bellinzona, Switzerland
| | - Carmelo Carlo-Stella
- Department of Biomedical Sciences, Humanitas University, and Department of Oncology and Hematology, Humanitas Research Hospital–IRCCS, Milano, Italy
| | - Izidore S. Lossos
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Paolo F. Caimi
- Cleveland Clinic Taussig Cancer Center, Cleveland, OH, United States
| | - Sunwoo Han
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Fei Yang
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Russ A. Kuker
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Craig H. Moskowitz
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
| |
Collapse
|
10
|
Carlier T, Frécon G, Mateus D, Rizkallah M, Kraeber-Bodéré F, Kanoun S, Blanc-Durand P, Itti E, Le Gouill S, Casasnovas RO, Bodet-Milin C, Bailly C. Prognostic Value of 18F-FDG PET Radiomics Features at Baseline in PET-Guided Consolidation Strategy in Diffuse Large B-Cell Lymphoma: A Machine-Learning Analysis from the GAINED Study. J Nucl Med 2024; 65:156-162. [PMID: 37945379 DOI: 10.2967/jnumed.123.265872] [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: 04/14/2023] [Revised: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
The results of the GA in Newly Diagnosed Diffuse Large B-Cell Lymphoma (GAINED) study demonstrated the success of an 18F-FDG PET-driven approach to allow early identification-for intensification therapy-of diffuse large B-cell lymphoma patients with a high risk of relapse. Besides, some works have reported the prognostic value of baseline PET radiomics features (RFs). This work investigated the added value of such biomarkers on survival of patients involved in the GAINED protocol. Methods: Conventional PET features and RFs were computed from 18F-FDG PET at baseline and extracted using different volume definitions (patient level, largest lesion, and hottest lesion). Clinical features and the consolidation treatment information were also considered in the model. Two machine-learning pipelines were trained with 80% of patients and tested on the remaining 20%. The training was repeated 100 times to highlight the test set variability. For the 2-y progression-free survival (PFS) outcome, the pipeline included a data augmentation and an elastic net logistic regression model. Results for different feature groups were compared using the mean area under the curve (AUC). For the survival outcome, the pipeline included a Cox univariate model to select the features. Then, the model included a split between high- and low-risk patients using the median of a regression score based on the coefficients of a penalized Cox multivariate approach. The log-rank test P values over the 100 loops were compared with a Wilcoxon signed-ranked test. Results: In total, 545 patients were included for the 2-y PFS classification and 561 for survival analysis. Clinical features alone, consolidation features alone, conventional PET features, and RFs extracted at patient level achieved an AUC of, respectively, 0.65 ± 0.07, 0.64 ± 0.06, 0.60 ± 0.07, and 0.62 ± 0.07 (0.62 ± 0.07 for the largest lesion and 0.54 ± 0.07 for the hottest). Combining clinical features with the consolidation features led to the best AUC (0.72 ± 0.06). Adding conventional PET features or RFs did not improve the results. For survival, the log-rank P values of the model involving clinical and consolidation features together were significantly smaller than all combined-feature groups (P < 0.007). Conclusion: The results showed that a concatenation of multimodal features coupled with a simple machine-learning model does not seem to improve the results in terms of 2-y PFS classification and PFS prediction for patient treated according to the GAINED protocol.
Collapse
Affiliation(s)
- Thomas Carlier
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Gauthier Frécon
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Diana Mateus
- Laboratoire des Sciences Numériques de Nantes, Ecole Centrale de Nantes, CNRS UMR 6004, Nantes, France
| | - Mira Rizkallah
- Laboratoire des Sciences Numériques de Nantes, Ecole Centrale de Nantes, CNRS UMR 6004, Nantes, France
| | - Françoise Kraeber-Bodéré
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Salim Kanoun
- Nuclear Medicine, Georges-François Leclerc Center, Dijon, France
| | - Paul Blanc-Durand
- Nuclear Medicine, CHU Henri Mondor, Paris-Est University, Créteil, France
| | - Emmanuel Itti
- Nuclear Medicine, CHU Henri Mondor, Paris-Est University, Créteil, France
| | - Steven Le Gouill
- Haematology Department, University Hospital, Nantes, France; and
| | | | - Caroline Bodet-Milin
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Clément Bailly
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France;
- Nuclear Medicine Department, University Hospital, Nantes, France
| |
Collapse
|
11
|
Knaup H, Weindler J, van Heek L, Voltin CA, Fuchs M, Borchmann P, Dietlein M, Kobe C, Roth K. PET/CT Reconstruction and Its Impact on [Measures of] Metabolic Tumor Volume. Acad Radiol 2023:S1076-6332(23)00691-8. [PMID: 38155023 DOI: 10.1016/j.acra.2023.12.016] [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: 11/09/2023] [Revised: 12/10/2023] [Accepted: 12/11/2023] [Indexed: 12/30/2023]
Abstract
RATIONALE AND OBJECTIVES In oncological imaging, the use of metabolic tumor volume (MTV) for further prognostic differentiation and the development of risk adapted strategies appears promising. The aim of this analysis was to evaluate ultra-high definition (UHD) and ordered subset expectation maximization (OSEM) PET/CT reconstructions for their potential impact on different methods of MTV measurement. MATERIALS AND METHODS We analyzed positron emission tomography combined with computed tomography (PET/CT) scans of 40 Hodgkin lymphoma patients before first-line treatment who had undergone fluorodeoxyglucose (FDG) PET/CT. The MTVs were determined taking an SUV of 4.0 (MTV4.0) as a fixed threshold or 41% of the single hottest voxel (MTV41%) as an adaptive threshold for automated lymphoma delineation in both UHD and OSEM reconstructions. We then compared the absolute and relative differences between MTV4.0 and MTV41% in UHD and OSEM reconstructions. The relative distribution of MTV4.0 and MTV41% in relation to the reconstruction method applied was recorded and respective differences were tested for statistical significance using the paired sample t-test. RESULTS A comparison of MTV4.0 and MTV41% showed smaller relative and absolute differences in MTV between different reconstruction settings for the MTV4.0 method. Conversely, the absolute as well as the relative differences between MTVs obtained from different reconstructions settings were significantly greater when the MTV41% method was applied (p < 0001). CONCLUSION MTV4.0 brings higher robustness between different reconstruction settings, while with MTV41% the deviation between volumes obtained with different reconstruction settings is greater. For clinical routine and for multicenter settings, the MTV4.0 therefore appears most promising.
Collapse
Affiliation(s)
- Henry Knaup
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| | - Jasmin Weindler
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| | - Lutz van Heek
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| | - Conrad-Amadeus Voltin
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| | - Michael Fuchs
- German Hodgkin Study Group, Department I of Internal Medicine, Center for Integrated Oncology Cologne Bonn, University Hospital of Cologne, Cologne, Germany (M.F., P.B.)
| | - Peter Borchmann
- German Hodgkin Study Group, Department I of Internal Medicine, Center for Integrated Oncology Cologne Bonn, University Hospital of Cologne, Cologne, Germany (M.F., P.B.)
| | - Markus Dietlein
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| | - Carsten Kobe
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.).
| | - Katrin Roth
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| |
Collapse
|
12
|
van Heek L, Weindler J, Gorniak C, Kaul H, Müller H, Mettler J, Baues C, Fuchs M, Borchmann P, Ferdinandus J, Dietlein M, Voltin CA, Kobe C, Roth KS. Prognostic value of baseline metabolic tumor volume (MTV) for forecasting chemotherapy outcome in early-stage unfavorable Hodgkin lymphoma: Data from the phase III HD17 trial. Eur J Haematol 2023; 111:881-887. [PMID: 37644732 DOI: 10.1111/ejh.14093] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/17/2023] [Accepted: 08/19/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVES The prognostic relevance of metabolic tumor volume (MTV) having recently been demonstrated in patients with early-stage favorable and advanced-stage Hodgkin lymphoma. The current study aimed to assess the potential prognostic value of 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in early-stage unfavorable Hodgkin lymphoma patients treated within the German Hodgkin Study Group HD17 trial. METHODS 18 F-FDG PET/CT images were available for MTV analysis in 154 cases. We used three different threshold methods (SUV2.5 , SUV4.0 , and SUV41% ) to calculate MTV. Receiver-operating-characteristic analysis was performed to describe the value of these parameters in predicting an adequate therapy response. Therapy response was evaluated as PET negativity after 2 cycles of eBEACOPP followed by 2 cycles of ABVD. RESULTS All three threshold methods analyzed for MTV showed a positive correlation with the PET response after chemotherapy. Areas under the curve (AUC) were 0.70 (95% CI 0.53-0.87) and 0.65 (0.50-0.80) using the fixed thresholds of SUV4.0 and SUV2.5 , respectively, for MTV- calculation. The calculation of MTV using a relative threshold of SUV41% showed an AUC of 0.63 (0.47-0.79). CONCLUSIONS MTV does have predictive value after chemotherapy in early-stage unfavorable Hodgkin lymphoma, particularly when the fixed threshold of SUV4.0 is used for MTV calculation. TRIAL REGISTRATION ClinicalTrials.gov NCT01356680.
Collapse
Affiliation(s)
- Lutz van Heek
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jasmin Weindler
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Claudia Gorniak
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Helen Kaul
- First Department of Internal Medicine and German Hodgkin Study Group (GHSG), Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Horst Müller
- First Department of Internal Medicine and German Hodgkin Study Group (GHSG), Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jasmin Mettler
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Christian Baues
- Department of Radiooncology, Marienhospital Herne, Ruhr University Bochum, Bochum, Germany
| | - Michael Fuchs
- First Department of Internal Medicine and German Hodgkin Study Group (GHSG), Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Peter Borchmann
- First Department of Internal Medicine and German Hodgkin Study Group (GHSG), Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Justin Ferdinandus
- First Department of Internal Medicine and German Hodgkin Study Group (GHSG), Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Markus Dietlein
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Conrad-Amadeus Voltin
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Carsten Kobe
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Katrin S Roth
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| |
Collapse
|
13
|
Lewis KL, Trotman J. Integration of PET in DLBCL. Semin Hematol 2023; 60:291-304. [PMID: 38326144 DOI: 10.1053/j.seminhematol.2023.12.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 11/24/2023] [Accepted: 12/04/2023] [Indexed: 02/09/2024]
Abstract
F-fluorodeoxyglucose positron emission tomography-computerized tomography (18FDG-PET/CT) is the gold-standard imaging modality for staging and response assessment for most lymphomas. This review focuses on the utility of 18FDG-PET/CT, and its role in staging, prognostication and response assessment in diffuse large B-cell lymphoma (DLBCL), including emerging possibilities for future use.
Collapse
Affiliation(s)
| | - Judith Trotman
- Concord Repatriation General Hospital, Concord, NSW, Australia
| |
Collapse
|
14
|
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] [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.
Collapse
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
| |
Collapse
|
15
|
Zhao S, Wang J, Jin C, Zhang X, Xue C, Zhou R, Zhong Y, Liu Y, He X, Zhou Y, Xu C, Zhang L, Qian W, Zhang H, Zhang X, Tian M. Stacking Ensemble Learning-Based [ 18F]FDG PET Radiomics for Outcome Prediction in Diffuse Large B-Cell Lymphoma. J Nucl Med 2023; 64:1603-1609. [PMID: 37500261 DOI: 10.2967/jnumed.122.265244] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 05/31/2023] [Indexed: 07/29/2023] Open
Abstract
This study aimed to develop an analytic approach based on [18F]FDG PET radiomics using stacking ensemble learning to improve the outcome prediction in diffuse large B-cell lymphoma (DLBCL). Methods: In total, 240 DLBCL patients from 2 medical centers were divided into the training set (n = 141), internal testing set (n = 61), and external testing set (n = 38). Radiomics features were extracted from pretreatment [18F]FDG PET scans at the patient level using 4 semiautomatic segmentation methods (SUV threshold of 2.5, SUV threshold of 4.0 [SUV4.0], 41% of SUVmax, and SUV threshold of mean liver uptake [PERCIST]). All extracted features were harmonized with the ComBat method. The intraclass correlation coefficient was used to evaluate the reliability of radiomics features extracted by different segmentation methods. Features from the most reliable segmentation method were selected by Pearson correlation coefficient analysis and the LASSO (least absolute shrinkage and selection operator) algorithm. A stacking ensemble learning approach was applied to build radiomics-only and combined clinical-radiomics models for prediction of 2-y progression-free survival and overall survival based on 4 machine learning classifiers (support vector machine, random forests, gradient boosting decision tree, and adaptive boosting). Confusion matrix, receiver-operating-characteristic curve analysis, and survival analysis were used to evaluate the model performance. Results: Among 4 semiautomatic segmentation methods, SUV4.0 segmentation yielded the highest interobserver reliability, with 830 (66.7%) selected radiomics features. The combined model constructed by the stacking method achieved the best discrimination performance. For progression-free survival prediction in the external testing set, the areas under the receiver-operating-characteristic curve and accuracy of the stacking-based combined model were 0.771 and 0.789, respectively. For overall survival prediction, the stacking-based combined model achieved an area under the curve of 0.725 and an accuracy of 0.763 in the external testing set. The combined model also demonstrated a more distinct risk stratification than the International Prognostic Index in all sets (log-rank test, all P < 0.05). Conclusion: The combined model that incorporates [18F]FDG PET radiomics and clinical characteristics based on stacking ensemble learning could enable improved risk stratification in DLBCL.
Collapse
Affiliation(s)
- Shuilin Zhao
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
| | - Jing Wang
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Chentao Jin
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Xiang Zhang
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Chenxi Xue
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Yan Zhong
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Yuwei Liu
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Xuexin He
- Department of Medical Oncology, Huashan Hospital of Fudan University, Shanghai, China
| | - Youyou Zhou
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Caiyun Xu
- Department of Nuclear Medicine, First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
| | - Lixia Zhang
- Department of Nuclear Medicine, First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
| | - Wenbin Qian
- Department of Hematology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China;
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China; and
| | - Xiaohui Zhang
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Mei Tian
- Department of Nuclear Medicine and PET Center, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China;
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
- Human Phenome Institute, Fudan University, Shanghai, China
| |
Collapse
|
16
|
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] [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.
Collapse
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
| |
Collapse
|
17
|
Ferrández MC, Golla SSV, Eertink JJ, de Vries BM, Lugtenburg PJ, Wiegers SE, Zwezerijnen GJC, Pieplenbosch S, Kurch L, Hüttmann A, Hanoun C, Dührsen U, de Vet HCW, Zijlstra JM, Boellaard R. An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients. Sci Rep 2023; 13:13111. [PMID: 37573446 PMCID: PMC10423266 DOI: 10.1038/s41598-023-40218-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/07/2023] [Indexed: 08/14/2023] Open
Abstract
Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.
Collapse
Affiliation(s)
- Maria C Ferrández
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
| | - Sandeep S V Golla
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bart M de Vries
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Pieternella J Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sanne E Wiegers
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lars Kurch
- Department of Nuclear Medicine, Clinic and Polyclinic for Nuclear Medicine, University of Leipzig, Leipzig, Germany
| | - Andreas Hüttmann
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Christine Hanoun
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Ulrich Dührsen
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Henrica C W de Vet
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Methodology, Amsterdam Public Health Research Institute, Methodology, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| |
Collapse
|
18
|
Eertink JJ, Zwezerijnen GJC, Heymans MW, Pieplenbosch S, Wiegers SE, Dührsen U, Hüttmann A, Kurch L, Hanoun C, Lugtenburg PJ, Barrington SF, Mikhaeel NG, Ceriani L, Zucca E, Czibor S, Györke T, Chamuleau MED, Hoekstra OS, de Vet HCW, Boellaard R, Zijlstra JM. Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma. Blood 2023; 141:3055-3064. [PMID: 37001036 PMCID: PMC10646814 DOI: 10.1182/blood.2022018558] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 01/31/2023] [Accepted: 02/27/2023] [Indexed: 04/03/2023] Open
Abstract
The objective of this study is to externally validate the clinical positron emission tomography (PET) model developed in the HOVON-84 trial and to compare the model performance of our clinical PET model using the international prognostic index (IPI). In total, 1195 patients with diffuse large B-cell lymphoma (DLBCL) were included in the study. Data of 887 patients from 6 studies were used as external validation data sets. The primary outcomes were 2-year progression-free survival (PFS) and 2-year time to progression (TTP). The metabolic tumor volume (MTV), maximum distance between the largest lesion and another lesion (Dmaxbulk), and peak standardized uptake value (SUVpeak) were extracted. The predictive values of the IPI and clinical PET model (MTV, Dmaxbulk, SUVpeak, performance status, and age) were tested. Model performance was assessed using the area under the curve (AUC), and diagnostic performance, using the positive predictive value (PPV). The IPI yielded an AUC of 0.62. The clinical PET model yielded a significantly higher AUC of 0.71 (P < .001). Patients with high-risk IPI had a 2-year PFS of 61.4% vs 51.9% for those with high-risk clinical PET, with an increase in PPV from 35.5% to 49.1%, respectively. A total of 66.4% of patients with high-risk IPI were free from progression or relapse vs 55.5% of patients with high-risk clinical PET scores, with an increased PPV from 33.7% to 44.6%, respectively. The clinical PET model remained predictive of outcome in 6 independent first-line DLBCL studies, and had higher model performance than the currently used IPI in all studies.
Collapse
Affiliation(s)
- J. J. Eertink
- Hematology, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - G. J. C. Zwezerijnen
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - M. W. Heymans
- Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Methodology, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - S. Pieplenbosch
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - S. E. Wiegers
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - U. Dührsen
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - A. Hüttmann
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - L. Kurch
- Klinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Leipzig, Leipzig, Germany
| | - C. Hanoun
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - P. J. Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - S. F. Barrington
- King’s College London and Guy’s and St Thomas’ PET Centre, School of Biomedical Engineering and Imaging Sciences, King’s Health Partners, King’s College London, London, United Kingdom
| | - N. G. Mikhaeel
- Department of Clinical Oncology, Guy’s Cancer Centre and School of Cancer and Pharmaceutical Sciences, King’s College London University, London, United Kingdom
| | - L. Ceriani
- Department of Nuclear Medicine and PET/CT Centre, Imaging Institute of Southern Switzerland, Università della Svizzera Italiana, Bellinzona, Switzerland
- SAKK Swiss Group for Clinical Cancer Research, Bern, Switzerland
| | - E. Zucca
- SAKK Swiss Group for Clinical Cancer Research, Bern, Switzerland
- Department of Oncology, IOSI - Oncology Institute of Southern Switzerland, Università della Svizzera Italiana, Bellinzona, Switzerland
| | - S. Czibor
- Department of Nuclear Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - T. Györke
- Department of Nuclear Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - M. E. D. Chamuleau
- Hematology, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - O. S. Hoekstra
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - H. C. W. de Vet
- Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Methodology, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - R. Boellaard
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - J. M. Zijlstra
- Hematology, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - PETRA Consortium
- Hematology, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Methodology, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Klinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Leipzig, Leipzig, Germany
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
- King’s College London and Guy’s and St Thomas’ PET Centre, School of Biomedical Engineering and Imaging Sciences, King’s Health Partners, King’s College London, London, United Kingdom
- Department of Clinical Oncology, Guy’s Cancer Centre and School of Cancer and Pharmaceutical Sciences, King’s College London University, London, United Kingdom
- Department of Nuclear Medicine and PET/CT Centre, Imaging Institute of Southern Switzerland, Università della Svizzera Italiana, Bellinzona, Switzerland
- SAKK Swiss Group for Clinical Cancer Research, Bern, Switzerland
- Department of Oncology, IOSI - Oncology Institute of Southern Switzerland, Università della Svizzera Italiana, Bellinzona, Switzerland
- Department of Nuclear Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| |
Collapse
|
19
|
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: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [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.
Collapse
Affiliation(s)
- Sally F. Barrington
- School of Biomedical Engineering and Imaging SciencesSt Thomas' Campus, Kings College LondonLondonUK
| |
Collapse
|
20
|
Zanoni L, Bezzi D, Nanni C, Paccagnella A, Farina A, Broccoli A, Casadei B, Zinzani PL, Fanti S. PET/CT in Non-Hodgkin Lymphoma: An Update. Semin Nucl Med 2023; 53:320-351. [PMID: 36522191 DOI: 10.1053/j.semnuclmed.2022.11.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 12/15/2022]
Abstract
Non-Hodgkin lymphomas represents a heterogeneous group of lymphoproliferative disorders characterized by different clinical courses, varying from indolent to highly aggressive. 18F-FDG-PET/CT is the current state-of-the-art diagnostic imaging, for the staging, restaging and evaluation of response to treatment in lymphomas with avidity for 18F-FDG, despite it is not routinely recommended for surveillance. PET-based response criteria (using five-point Deauville Score) are nowadays uniformly applied in FDG-avid lymphomas. In this review, a comprehensive overview of the role of 18F-FDG-PET in Non-Hodgkin lymphomas is provided, at each relevant point of patient management, particularly focusing on recent advances on diffuse large B-cell lymphoma and follicular lymphoma, with brief updates also on other histotypes (such as marginal zone, mantle cell, primary mediastinal- B cell lymphoma and T cell lymphoma). PET-derived semiquantitative factors useful for patient stratification and prognostication and emerging radiomics research are also presented.
Collapse
Affiliation(s)
- Lucia Zanoni
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
| | - Davide Bezzi
- Nuclear Medicine, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Cristina Nanni
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Andrea Paccagnella
- Nuclear Medicine, Alma Mater Studiorum University of Bologna, Bologna, Italy; Nuclear Medicine Unit, AUSL Romagna, Cesena, Italy
| | - Arianna Farina
- Nuclear Medicine, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Alessandro Broccoli
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli," Bologna, Italy; Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale, Università di Bologna, Bologna, Italy
| | - Beatrice Casadei
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli," Bologna, Italy; Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale, Università di Bologna, Bologna, Italy
| | - Pier Luigi Zinzani
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli," Bologna, Italy; Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale, Università di Bologna, Bologna, Italy
| | - Stefano Fanti
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy; Nuclear Medicine, Alma Mater Studiorum University of Bologna, Bologna, Italy
| |
Collapse
|
21
|
Role of 18F-fluorodeoxyglucose PET/computed tomography in the diagnosis and treatment response assessment of primary bone lymphoma. Nucl Med Commun 2023; 44:318-329. [PMID: 36722755 PMCID: PMC9994810 DOI: 10.1097/mnm.0000000000001668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Primary bone lymphoma (PBL) is a rare type of extranodal lymphoma, and the clinical application value of 18F-fluorodeoxyglucose PET/computed tomography ( 18 F-FDG PET/CT) in PBL has not been fully evaluated. This study aimed to determine the imaging characteristics of PBL and investigate the value of 18 F-FDG PET/CT parameters. METHODS A total of 25 patients with PBL who underwent PET/CT examination before treatment were included in this study. The clinicopathological parameters and PET/CT parameters were analyzed. RESULTS Among the 25 patients, 7 patients had single lesions, 15 patients had nonsingle lesions (≥2) and 3 patients had diffuse distribution in the medullary cavity. The bone destruction types included osteolytic, osteogenic, normal density, mixed lytic and osteogenic. All patients showed increased FDG uptake, and the CT detection rate was 88%. Five patients underwent PET/CT assessment mid-treatment, and when assessed using the Deauville five-point scale, four patients were PET-negative and one patient was PET-positive. There were two PET-positive and three PET-negative patients when assessed using the Δ maximum standardized uptake value (SUV max ) method. Six patients underwent PET/CT imaging at the end of treatment. When assessed using the Deauville five-point scale, five patients (83%) were PET-negative and one patient (17%) was PET-positive. The same results were obtained when evaluated by the ΔSUV max method. CONCLUSION PET/CT plays a substantial role in the diagnosis and treatment efficacy evaluation of PBL, and it should be recognized by clinicians and radiologists. Changes in metabolic parameters such as SUV, metabolic tumor volume and total lesion glycolysis have considerable potential for application in PBL diagnostics and treatment efficacy evaluation.
Collapse
|
22
|
Quinn E, Olson C, Jain MK, Sullivan J, Thorpe MP, Johnson GB, Young JR. Technologist-Based Implementation of Total Metabolic Tumor Volume into Clinical Practice. J Nucl Med Technol 2023; 51:57-59. [PMID: 36351799 DOI: 10.2967/jnmt.122.264714] [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: 07/26/2022] [Revised: 10/27/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022] Open
Abstract
Metabolic tumor volume (MTV) is defined as the total metabolically active tumor volume seen on 18F-FDG PET/CT examinations. Calculating MTV is often time-consuming, requiring a high degree of manual input. In this study, the MTV calculations of a board-certified nuclear radiologist were compared with those of 2 nuclear medicine technologists. As part of the technologists' educational program, after their classroom time they were trained by the radiologist for 30 min. The technologists calculated MTV within 7.5% of the radiologist's calculations in a set of patients who had diffuse large B-cell lymphoma and were undergoing initial staging 18F-FDG PET/CT. These findings suggest that nuclear medicine technologists may help accelerate implementation of MTV into clinical practice with favorable accuracy, possibly as an initial step followed by validation by the interpreting physician. The aim of this study was to explore whether efficiency is improved by integrating nuclear medicine technologists into a semiautomated workflow to calculate total MTV.
Collapse
Affiliation(s)
- Erina Quinn
- Lake Erie College of Osteopathic Medicine, Bradenton, Florida;
| | - Claire Olson
- Department of Radiology, Mayo Clinic, Jacksonville, Florida
| | - Manoj K Jain
- Department of Radiology, Mayo Clinic, Jacksonville, Florida
| | - Jaiden Sullivan
- Department of Radiology, Mayo Clinic, Rochester, Minnesota; and
| | | | - Geoffrey B Johnson
- Department of Radiology, Mayo Clinic, Rochester, Minnesota; and.,Department of Immunology, Mayo Clinic, Rochester, Minnesota
| | - Jason R Young
- Department of Radiology, Mayo Clinic, Jacksonville, Florida
| |
Collapse
|
23
|
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] [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.
Collapse
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
Collapse
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.
| |
Collapse
|
24
|
Karimdjee M, Delaby G, Huglo D, Baillet C, Willaume A, Dujardin S, Bailliez A. Evaluation of a convolution neural network for baseline total tumor metabolic volume on [ 18F]FDG PET in diffuse large B cell lymphoma. Eur Radiol 2023; 33:3386-3395. [PMID: 36600126 DOI: 10.1007/s00330-022-09375-1] [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: 07/15/2022] [Revised: 10/20/2022] [Accepted: 12/09/2022] [Indexed: 01/06/2023]
Abstract
OBJECTIVES New PET data-processing tools allow for automatic lesion selection and segmentation by a convolution neural network using artificial intelligence (AI) to obtain total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) routinely at the clinical workstation. Our objective was to evaluate an AI implemented in a new version of commercial software to verify reproducibility of results and time savings in a daily workflow. METHODS Using the software to obtain TMTV and TLG, two nuclear physicians applied five methods to retrospectively analyze data for 51 patients. Methods 1 and 2 were fully automated with exclusion of lesions ≤ 0.5 mL and ≤ 0.1 mL, respectively. Methods 3 and 4 were fully automated with physician review. Method 5 was semi-automated and used as reference. Time and number of clicks to complete the measurement were recorded for each method. Inter-instrument and inter-observer variation was assessed by the intra-class coefficient (ICC) and Bland-Altman plots. RESULTS Between methods 3 and 5, for the main user, the ICC was 0.99 for TMTV and 1.0 for TLG. Between the two users applying method 3, ICC was 0.97 for TMTV and 0.99 for TLG. Mean processing time (± standard deviation) was 20 s ± 9.0 for method 1, 178 s ± 125.7 for method 3, and 326 s ± 188.6 for method 5 (p < 0.05). CONCLUSION AI-enabled lesion detection software offers an automated, fast, reliable, and consistently performing tool for obtaining TMTV and TLG in a daily workflow. KEY POINTS • Our study shows that artificial intelligence lesion detection software is an automated, fast, reliable, and consistently performing tool for obtaining total metabolic tumor volume and total lesion glycolysis in a daily workflow.
Collapse
Affiliation(s)
- Mourtaza Karimdjee
- Nuclear Medicine Department, CHU Lille University Hospital, Lille, France.
| | - Gauthier Delaby
- Nuclear Medicine Department, CHU Lille University Hospital, Lille, France
| | - Damien Huglo
- Nuclear Medicine Department, CHU Lille University Hospital, Lille, France
| | - Clio Baillet
- Nuclear Medicine Department, CHU Lille University Hospital, Lille, France
| | - Alexandre Willaume
- Hematology Department, Group of Hospitals of the Catholic Institute of Lille, Lille, France
| | - Simon Dujardin
- Nuclear Medicine Department, CHU Lille University Hospital, Lille, France
| | - Alban Bailliez
- Nuclear Medicine Department, Group of Hospitals of the Catholic Institute of Lille, Lille, France
| |
Collapse
|
25
|
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] [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.
Collapse
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
| |
Collapse
|
26
|
Outcome Prediction in Patients With Large B-cell Lymphoma Undergoing Chimeric Antigen Receptor T-cell Therapy. Hemasphere 2023; 7:e817. [PMID: 36698613 PMCID: PMC9829285 DOI: 10.1097/hs9.0000000000000817] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 11/17/2022] [Indexed: 01/27/2023] Open
Abstract
The introduction of chimeric antigen receptor (CAR) T-cell therapy has led to a fundamental shift in the management of relapsed and refractory large B-cell lymphoma. However, our understanding of risk factors associated with non-response is still insufficient and the search for predictive biomarkers continues. Some parameters measurable on 18F-fluorodeoxyglucose positron emission tomography (PET) may be of additional value in this context. A total of 47 individuals from three German university centers who underwent re-staging with PET prior to CAR T-cell therapy were enrolled into the present study. After multivariable analysis considering tumor characteristics and patient factors that might affect progression-free survival (PFS), we investigated whether metabolic tumor volume (MTV) or maximum standardized uptake value (SUVmax) further improve risk stratification. Their most suitable cut-offs were determined by Cox and logistic regression. Forward selection identified extra-nodal disease as the most predictive factor of those routinely available, and we found it to be associated with significantly inferior overall survival after CAR T-cell treatment (P = 0.012). Furthermore, patients with MTV and SUVmax higher than the optimal threshold of 11 mL and 16.7, respectively, experienced shorter PFS (P = 0.016 and 0.002, respectively). Hence, these risk factors might be useful for selection of individuals likely to benefit from CAR T-cell therapy and their management.
Collapse
|
27
|
Zwezerijnen GJC, Eertink JJ, Ferrández MC, Wiegers SE, Burggraaff CN, Lugtenburg PJ, Heymans MW, de Vet HCW, Zijlstra JM, Boellaard R. Reproducibility of [18F]FDG PET/CT liver SUV as reference or normalisation factor. Eur J Nucl Med Mol Imaging 2023; 50:486-493. [PMID: 36166080 PMCID: PMC9816285 DOI: 10.1007/s00259-022-05977-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 09/15/2022] [Indexed: 01/11/2023]
Abstract
INTRODUCTION Although visual and quantitative assessments of [18F]FDG PET/CT studies typically rely on liver uptake value as a reference or normalisation factor, consensus or consistency in measuring [18F]FDG uptake is lacking. Therefore, we evaluate the variation of several liver standardised uptake value (SUV) measurements in lymphoma [18F]FDG PET/CT studies using different uptake metrics. METHODS PET/CT scans from 34 lymphoma patients were used to calculate SUVmaxliver, SUVpeakliver and SUVmeanliver as a function of (1) volume-of-interest (VOI) size, (2) location, (3) imaging time point and (4) as a function of total metabolic tumour volume (MTV). The impact of reconstruction protocol on liver uptake is studied on 15 baseline lymphoma patient scans. The effect of noise on liver SUV was assessed using full and 25% count images of 15 lymphoma scans. RESULTS Generally, SUVmaxliver and SUVpeakliver were 38% and 16% higher compared to SUVmeanliver. SUVmaxliver and SUVpeakliver increased up to 31% and 15% with VOI size while SUVmeanliver remained unchanged with the lowest variability for the largest VOI size. Liver uptake metrics were not affected by VOI location. Compared to baseline, liver uptake metrics were 15-18% and 9-18% higher at interim and EoT PET, respectively. SUVliver decreased with larger total MTVs. SUVmaxliver and SUVpeakliver were affected by reconstruction protocol up to 62%. SUVmax and SUVpeak moved 22% and 11% upward between full and 25% count images. CONCLUSION SUVmeanliver was most robust against VOI size, location, reconstruction protocol and image noise level, and is thus the most reproducible metric for liver uptake. The commonly recommended 3 cm diameter spherical VOI-based SUVmeanliver values were only slightly more variable than those seen with larger VOI sizes and are sufficient for SUVmeanliver measurements in future studies. TRIAL REGISTRATION EudraCT: 2006-005,174-42, 01-08-2008.
Collapse
Affiliation(s)
- Gerben J C Zwezerijnen
- Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Hematology, Amsterdam, The Netherlands
| | - Maria C Ferrández
- Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Sanne E Wiegers
- Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Coreline N Burggraaff
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Hematology, Amsterdam, The Netherlands
| | | | - Martijn W Heymans
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Methodology, Amsterdam, The Netherlands
| | - Henrica C W de Vet
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Methodology, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Hematology, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
| |
Collapse
|
28
|
Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma. Blood Adv 2022; 7:214-223. [PMID: 36306337 PMCID: PMC9841040 DOI: 10.1182/bloodadvances.2022008629] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/20/2022] [Accepted: 09/26/2022] [Indexed: 01/21/2023] Open
Abstract
We investigated whether the outcome prediction of patients with aggressive B-cell lymphoma can be improved by combining clinical, molecular genotype, and radiomics features. MYC, BCL2, and BCL6 rearrangements were assessed using fluorescence in situ hybridization. Seventeen radiomics features were extracted from the baseline positron emission tomography-computed tomography of 323 patients, which included maximum standardized uptake value (SUVmax), SUVpeak, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis, and 12 dissemination features pertaining to distance, differences in uptake and volume between lesions, respectively. Logistic regression with backward feature selection was used to predict progression after 2 years. The predictive value of (1) International Prognostic Index (IPI); (2) IPI plus MYC; (3) IPI, MYC, and MTV; (4) radiomics; and (5) MYC plus radiomics models were tested using the cross-validated area under the curve (CV-AUC) and positive predictive values (PPVs). IPI yielded a CV-AUC of 0.65 ± 0.07 with a PPV of 29.6%. The IPI plus MYC model yielded a CV-AUC of 0.68 ± 0.08. IPI, MYC, and MTV yielded a CV-AUC of 0.74 ± 0.08. The highest model performance of the radiomics model was observed for MTV combined with the maximum distance between the largest lesion and another lesion, the maximum difference in SUVpeak between 2 lesions, and the sum of distances between all lesions, yielding an improved CV-AUC of 0.77 ± 0.07. The same radiomics features were retained when adding MYC (CV-AUC, 0.77 ± 0.07). PPV was highest for the MYC plus radiomics model (50.0%) and increased by 20% compared with the IPI (29.6%). Adding radiomics features improved model performance and PPV and can, therefore, aid in identifying poor prognosis patients.
Collapse
|
29
|
18F-FDG PET-Based Combined Baseline and End-Of-Treatment Radiomics Model Improves the Prognosis Prediction in Diffuse Large B Cell Lymphoma After First-Line Therapy. Acad Radiol 2022:S1076-6332(22)00548-7. [DOI: 10.1016/j.acra.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/22/2022] [Accepted: 10/11/2022] [Indexed: 11/27/2022]
|
30
|
Husby T, Johansen H, Bogsrud TV, Hustad KV, Evensen BV, Boellaard R, Giskeødegård GF, Fagerli UM, Eikenes L. Prognostic value of combined MTV and ADC derived from baseline FDG PET/MRI in aggressive non-Hodgkins lymphoma. BMC Cancer 2022; 22:1117. [PMID: 36319985 PMCID: PMC9623965 DOI: 10.1186/s12885-022-10194-2] [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: 06/26/2022] [Accepted: 10/18/2022] [Indexed: 01/24/2023] Open
Abstract
PURPOSE The aim of this prospective study was to investigate the prognostic value of metabolic tumor volume (MTV) and apparent diffusion coefficient (ADC) from baseline FDG PET/MRI compared to established clinical risk factors in terms of progression free survival (PFS) at 2 years in a cohort of diffuse large B-cell Lymphoma (DLBCL) and high-grade-B-cell lymphoma (HGBCL). METHODS Thirty-three patients and their baseline PET/MRI examinations were included. Images were read by two pairs of nuclear medicine physicians and radiologists for defining lymphoma lesions. MTV was computed on PET, and up to six lymphoma target lesions with restricted diffusion was defined for each PET/MRI examination. Minimum ADC (ADCmin) and the corresponding mean ADC (ADCmean) from the target lesion with the lowest ADCmin were included in the analyses. For the combined PET/MRI parameters, the ratio between MTV and the target lesion with the lowest ADCmin (MTV/ADCmin) and the corresponding ADCmean (MTV/ADCmean) was calculated for each patient. Clinical, histological, and PET/MRI parameters were compared between the treatment failure and treatment response group, while survival analyses for each variable was performed by using univariate Cox regression. In case of significant variables in the Cox regression analyses, Kaplan-Meier survival analyses with log-rank test was used to study the effect of the variables on PFS. RESULTS ECOC PS scale ≥2 (p = 0.05) and ADCmean (p = 0.05) were significantly different between the treatment failure group (n = 6) and those with treatment response (n = 27). Survival analyses showed that ADCmean was associated with PFS (p = 0.02, [HR 2.3 for 1 SD increase]), while combining MTV and ADC did not predict outcome. In addition, ECOG PS ≥2 (p = 0.01, [HR 13.3]) and histology of HGBCL (p = 0.02 [HR 7.6]) was significantly associated with PFS. CONCLUSIONS ADCmean derived from baseline MRI could be a prognostic imaging biomarker for DLBCL and HGBCL. Baseline staging with PET/MRI could therefore give supplementary prognostic information compared to today's standard PET/CT.
Collapse
Affiliation(s)
- Trine Husby
- grid.5947.f0000 0001 1516 2393Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks, 8905 Trondheim, Norway ,grid.52522.320000 0004 0627 3560Department of Oncology, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway
| | - Håkon Johansen
- grid.52522.320000 0004 0627 3560Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway
| | - Trond Velde Bogsrud
- grid.412244.50000 0004 4689 5540PET-Centre, University Hospital of North Norway, Tromsø, Norway ,grid.154185.c0000 0004 0512 597XPET-Centre, Aarhus University Hospital, Aarhus, Denmark
| | - Kari Vekseth Hustad
- grid.52522.320000 0004 0627 3560Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway
| | - Birte Veslemøy Evensen
- grid.52522.320000 0004 0627 3560Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ronald Boellaard
- grid.4494.d0000 0000 9558 4598Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands ,grid.16872.3a0000 0004 0435 165XDepartment of Radiology and Nuclear Medicine, Cancer Center Amsterdam, University Medical Centers Amsterdam, VUMC, Amsterdam, The Netherlands
| | - Guro F. Giskeødegård
- grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Unn-Merete Fagerli
- grid.52522.320000 0004 0627 3560Department of Oncology, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway ,grid.5947.f0000 0001 1516 2393Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Live Eikenes
- grid.5947.f0000 0001 1516 2393Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks, 8905 Trondheim, Norway
| |
Collapse
|
31
|
Driessen J, Zwezerijnen GJ, Schöder H, Drees EE, Kersten MJ, Moskowitz AJ, Moskowitz CH, Eertink JJ, de Vet HC, Hoekstra OS, Zijlstra JM, Boellaard R. The Impact of Semiautomatic Segmentation Methods on Metabolic Tumor Volume, Intensity, and Dissemination Radiomics in 18F-FDG PET Scans of Patients with Classical Hodgkin Lymphoma. J Nucl Med 2022; 63:1424-1430. [PMID: 34992152 PMCID: PMC9454468 DOI: 10.2967/jnumed.121.263067] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 12/28/2021] [Indexed: 01/26/2023] Open
Abstract
Consensus about a standard segmentation method to derive metabolic tumor volume (MTV) in classical Hodgkin lymphoma (cHL) is lacking, and it is unknown how different segmentation methods influence quantitative PET features. Therefore, we aimed to evaluate the delineation and completeness of lesion selection and the need for manual adaptation with different segmentation methods, and to assess the influence of segmentation methods on the prognostic value of MTV, intensity, and dissemination radiomics features in cHL patients. Methods: We analyzed a total of 105 18F-FDG PET/CT scans from patients with newly diagnosed (n = 35) and relapsed/refractory (n = 70) cHL with 6 segmentation methods: 2 fixed thresholds on SUV4.0 and SUV2.5, 2 relative methods of 41% of SUVmax (41max) and a contrast-corrected 50% of SUVpeak (A50P), and 2 combination majority vote (MV) methods (MV2, MV3). Segmentation quality was assessed by 2 reviewers on the basis of predefined quality criteria: completeness of selection, the need for manual adaptation, and delineation of lesion borders. Correlations and prognostic performance of resulting radiomics features were compared among the methods. Results: SUV4.0 required the least manual adaptation but tended to underestimate MTV and often missed small lesions with low 18F-FDG uptake. SUV2.5 most frequently included all lesions but required minor manual adaptations and generally overestimated MTV. In contrast, few lesions were missed when using 41max, A50P, MV2, and MV3, but these segmentation methods required extensive manual adaptation and overestimated MTV in most cases. MTV and dissemination features significantly differed among the methods. However, correlations among methods were high for MTV and most intensity and dissemination features. There were no significant differences in prognostic performance for all features among the methods. Conclusion: A high correlation existed between MTV, intensity, and most dissemination features derived with the different segmentation methods, and the prognostic performance is similar. Despite frequently missing small lesions with low 18F-FDG avidity, segmentation with a fixed threshold of SUV4.0 required the least manual adaptation, which is critical for future research and implementation in clinical practice. However, the importance of small, low 18F-FDG-avidity lesions should be addressed in a larger cohort of cHL patients.
Collapse
Affiliation(s)
- Julia Driessen
- Department of Hematology, Amsterdam UMC, University of Amsterdam, LYMMCARE (Lymphoma and Myeloma Center, Amsterdam), Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Gerben J.C. Zwezerijnen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Heiko Schöder
- Department of Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Esther E.E. Drees
- Department of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Marie José Kersten
- Department of Hematology, Amsterdam UMC, University of Amsterdam, LYMMCARE (Lymphoma and Myeloma Center, Amsterdam), Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Alison J. Moskowitz
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Craig H. Moskowitz
- Department of Medicine, Sylvester Comprehensive Cancer Center, Miami, Florida
| | - Jakoba J. Eertink
- Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands; and
| | - Henrica C.W. de Vet
- Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Amsterdam, Netherlands
| | - Otto S. Hoekstra
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Josée M. Zijlstra
- Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands; and
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands;
| |
Collapse
|
32
|
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] [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.
Collapse
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
| |
Collapse
|
33
|
Eertink JJ, Zwezerijnen GJC, Cysouw MCF, Wiegers SE, Pfaehler EAG, Lugtenburg PJ, van der Holt B, Hoekstra OS, de Vet HCW, Zijlstra JM, Boellaard R. Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features. Eur J Nucl Med Mol Imaging 2022; 49:4642-4651. [PMID: 35925442 PMCID: PMC9606052 DOI: 10.1007/s00259-022-05916-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 07/14/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE Biomarkers that can accurately predict outcome in DLBCL patients are urgently needed. Radiomics features extracted from baseline [18F]-FDG PET/CT scans have shown promising results. This study aims to investigate which lesion- and feature-selection approaches/methods resulted in the best prediction of progression after 2 years. METHODS A total of 296 patients were included. 485 radiomics features (n = 5 conventional PET, n = 22 morphology, n = 50 intensity, n = 408 texture) were extracted for all individual lesions and at patient level, where all lesions were aggregated into one VOI. 18 features quantifying dissemination were extracted at patient level. Several lesion selection approaches were tested (largest or hottest lesion, patient level [all with/without dissemination], maximum or median of all lesions) and compared to the predictive value of our previously published model. Several data reduction methods were applied (principal component analysis, recursive feature elimination (RFE), factor analysis, and univariate selection). The predictive value of all models was tested using a fivefold cross-validation approach with 50 repeats with and without oversampling, yielding the mean cross-validated AUC (CV-AUC). Additionally, the relative importance of individual radiomics features was determined. RESULTS Models with conventional PET and dissemination features showed the highest predictive value (CV-AUC: 0.72-0.75). Dissemination features had the highest relative importance in these models. No lesion selection approach showed significantly higher predictive value compared to our previous model. Oversampling combined with RFE resulted in highest CV-AUCs. CONCLUSION Regardless of the applied lesion selection or feature selection approach and feature reduction methods, patient level conventional PET features and dissemination features have the highest predictive value. Trial registration number and date: EudraCT: 2006-005174-42, 01-08-2008.
Collapse
Affiliation(s)
- Jakoba J Eertink
- Department of Hematology, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. .,Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Matthijs C F Cysouw
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sanne E Wiegers
- Department of Hematology, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | | | - Pieternella J Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Wytemaweg 80, 3015 CN, Rotterdam, the Netherlands
| | - Bronno van der Holt
- Department of Hematology, HOVON Data Center, Erasmus MC Cancer Institute, Dr. Molewaterplein 40, 3015 GD, Rotterdam, the Netherlands
| | - Otto S Hoekstra
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Henrica C W de Vet
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health Research Institute, Methodology, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Department of Hematology, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
34
|
Ferrández MC, Eertink JJ, Golla SSV, Wiegers SE, Zwezerijnen GJC, Pieplenbosch S, Zijlstra JM, Boellaard R. Combatting the effect of image reconstruction settings on lymphoma [ 18F]FDG PET metabolic tumor volume assessment using various segmentation methods. EJNMMI Res 2022; 12:44. [PMID: 35904645 PMCID: PMC9338209 DOI: 10.1186/s13550-022-00916-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/18/2022] [Indexed: 11/15/2022] Open
Abstract
Background [18F]FDG PET-based metabolic tumor volume (MTV) is a promising prognostic marker for lymphoma patients. The aim of this study is to assess the sensitivity of several MTV segmentation methods to variations in image reconstruction methods and the ability of ComBat to improve MTV reproducibility. Methods Fifty-six lesions were segmented from baseline [18F]FDG PET scans of 19 lymphoma patients. For each scan, EARL1 and EARL2 standards and locally clinically preferred reconstruction protocols were applied. Lesions were delineated using 9 semiautomatic segmentation methods: fixed threshold based on standardized uptake value (SUV), (SUV = 4, SUV = 2.5), relative threshold (41% of SUVmax [41M], 50% of SUVpeak [A50P]), majority vote-based methods that select voxels detected by at least 2 (MV2) and 3 (MV3) out of the latter 4 methods, Nestle thresholding, and methods that identify the optimal method based on SUVmax (L2A, L2B). MTVs from EARL2 and locally clinically preferred reconstructions were compared to those from EARL1. Finally, different versions of ComBat were explored to harmonize the data.
Results MTVs from the SUV4.0 method were least sensitive to the use of different reconstructions (MTV ratio: median = 1.01, interquartile range = [0.96–1.10]). After ComBat harmonization, an improved agreement of MTVs among different reconstructions was found for most segmentation methods. The regular implementation of ComBat (‘Regular ComBat’) using non-transformed distributions resulted in less accurate and precise MTV alignments than a version using log-transformed datasets (‘Log-transformed ComBat’). Conclusion MTV depends on both segmentation method and reconstruction methods. ComBat reduces reconstruction dependent MTV variability, especially when log-transformation is used to account for the non-normal distribution of MTVs. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-022-00916-9.
Collapse
Affiliation(s)
- Maria C Ferrández
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Sanne E Wiegers
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| |
Collapse
|
35
|
Mikhaeel NG, Heymans MW, Eertink JJ, de Vet HC, Boellaard R, Dührsen U, Ceriani L, Schmitz C, Wiegers SE, Hüttmann A, Lugtenburg PJ, Zucca E, Zwezerijnen GJ, Hoekstra OS, Zijlstra JM, Barrington SF. Proposed New Dynamic Prognostic Index for Diffuse Large B-Cell Lymphoma: International Metabolic Prognostic Index. J Clin Oncol 2022; 40:2352-2360. [PMID: 35357901 PMCID: PMC9287279 DOI: 10.1200/jco.21.02063] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/23/2022] [Accepted: 02/09/2022] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Baseline metabolic tumor volume (MTV) is a promising biomarker in diffuse large B-cell lymphoma (DLBCL). Our aims were to determine the best statistical relationship between MTV and survival and to compare MTV with the International Prognostic Index (IPI) and its individual components to derive the best prognostic model. METHODS PET scans and clinical data were included from five published studies in newly diagnosed diffuse large B-cell lymphoma. Transformations of MTV were compared with the primary end points of 3-year progression-free survival (PFS) and overall survival (OS) to derive the best relationship for further analyses. MTV was compared with IPI categories and individual components to derive the best model. Patients were grouped into three groups for survival analysis using Kaplan-Meier analysis; 10% at highest risk, 30% intermediate risk, and 60% lowest risk, corresponding with expected clinical outcome. Validation of the best model was performed using four studies as a test set and the fifth study for validation and repeated five times. RESULTS The best relationship for MTV and survival was a linear spline model with one knot located at the median MTV value of 307.9 cm3. MTV was a better predictor than IPI for PFS and OS. The best model combined MTV with age as continuous variables and individual stage as I-IV. The MTV-age-stage model performed better than IPI and was also better at defining a high-risk group (3-year PFS 46.3% v 58.0% and 3-year OS 51.5% v 66.4% for the new model and IPI, respectively). A regression formula was derived to estimate individual patient survival probabilities. CONCLUSION A new prognostic index is proposed using MTV, age, and stage, which outperforms IPI and enables individualized estimates of patient outcome.
Collapse
Affiliation(s)
- N. George Mikhaeel
- Department of Clinical Oncology, Guy's Cancer Centre and School of Cancer and Pharmaceutical Sciences, King's College London University, London, United Kingdom
| | - Martijn W. Heymans
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Jakoba J. Eertink
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Hematology, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Henrica C.W. de Vet
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Ronald Boellaard
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Ulrich Dührsen
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Luca Ceriani
- Department of Oncology, IOSI—Oncology Institute of Southern Switzerland, Bellinzona; Università della Svizzera Italiana, Bellinzona, Switzerland
- SAKK—Swiss Group for Clinical Cancer Research, Bern, Switzerland
| | - Christine Schmitz
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Sanne E. Wiegers
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Hematology, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Andreas Hüttmann
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Pieternella J. Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, the Netherlands
| | - Emanuele Zucca
- Department of Oncology, IOSI—Oncology Institute of Southern Switzerland, Bellinzona; Università della Svizzera Italiana, Bellinzona, Switzerland
- SAKK—Swiss Group for Clinical Cancer Research, Bern, Switzerland
| | - Gerben J.C. Zwezerijnen
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Otto S. Hoekstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Josée M. Zijlstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Hematology, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Sally F. Barrington
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's Health Partners, Kings College London, London, United Kingdom
| |
Collapse
|
36
|
Mikhaeel NG, Heymans MW, Eertink JJ, de Vet HCW, Boellaard R, Dührsen U, Ceriani L, Schmitz C, Wiegers SE, Hüttmann A, Lugtenburg PJ, Zucca E, Zwezerijnen GJC, Hoekstra OS, Zijlstra JM, Barrington SF. Proposed New Dynamic Prognostic Index for Diffuse Large B-Cell Lymphoma: International Metabolic Prognostic Index. J Clin Oncol 2022; 40:2352-2360. [PMID: 35357901 DOI: 10.1200/jco.21.02063:jco2102063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023] Open
Abstract
PURPOSE Baseline metabolic tumor volume (MTV) is a promising biomarker in diffuse large B-cell lymphoma (DLBCL). Our aims were to determine the best statistical relationship between MTV and survival and to compare MTV with the International Prognostic Index (IPI) and its individual components to derive the best prognostic model. METHODS PET scans and clinical data were included from five published studies in newly diagnosed diffuse large B-cell lymphoma. Transformations of MTV were compared with the primary end points of 3-year progression-free survival (PFS) and overall survival (OS) to derive the best relationship for further analyses. MTV was compared with IPI categories and individual components to derive the best model. Patients were grouped into three groups for survival analysis using Kaplan-Meier analysis; 10% at highest risk, 30% intermediate risk, and 60% lowest risk, corresponding with expected clinical outcome. Validation of the best model was performed using four studies as a test set and the fifth study for validation and repeated five times. RESULTS The best relationship for MTV and survival was a linear spline model with one knot located at the median MTV value of 307.9 cm3. MTV was a better predictor than IPI for PFS and OS. The best model combined MTV with age as continuous variables and individual stage as I-IV. The MTV-age-stage model performed better than IPI and was also better at defining a high-risk group (3-year PFS 46.3% v 58.0% and 3-year OS 51.5% v 66.4% for the new model and IPI, respectively). A regression formula was derived to estimate individual patient survival probabilities. CONCLUSION A new prognostic index is proposed using MTV, age, and stage, which outperforms IPI and enables individualized estimates of patient outcome.
Collapse
Affiliation(s)
- N George Mikhaeel
- Department of Clinical Oncology, Guy's Cancer Centre and School of Cancer and Pharmaceutical Sciences, King's College London University, London, United Kingdom
| | - Martijn W Heymans
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Jakoba J Eertink
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Hematology, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Henrica C W de Vet
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Ronald Boellaard
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Ulrich Dührsen
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Luca Ceriani
- Department of Oncology, IOSI-Oncology Institute of Southern Switzerland, Bellinzona; Università della Svizzera Italiana, Bellinzona, Switzerland
- SAKK-Swiss Group for Clinical Cancer Research, Bern, Switzerland
| | - Christine Schmitz
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Sanne E Wiegers
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Hematology, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Andreas Hüttmann
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Pieternella J Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, the Netherlands
| | - Emanuele Zucca
- Department of Oncology, IOSI-Oncology Institute of Southern Switzerland, Bellinzona; Università della Svizzera Italiana, Bellinzona, Switzerland
- SAKK-Swiss Group for Clinical Cancer Research, Bern, Switzerland
| | - Gerben J C Zwezerijnen
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Otto S Hoekstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Josée M Zijlstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Hematology, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Sally F Barrington
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's Health Partners, Kings College London, London, United Kingdom
| |
Collapse
|
37
|
Burggraaff CN, Eertink JJ, Lugtenburg PJ, Hoekstra OS, Arens AI, de Keizer B, Heymans MW, van der Holt B, Wiegers SE, Pieplenbosch S, Boellaard R, de Vet HC, Zijlstra JM. 18F-FDG PET Improves Baseline Clinical Predictors of Response in Diffuse Large B-Cell Lymphoma: The HOVON-84 Study. J Nucl Med 2022; 63:1001-1007. [PMID: 34675112 PMCID: PMC9258573 DOI: 10.2967/jnumed.121.262205] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 09/29/2021] [Indexed: 01/03/2023] Open
Abstract
We aimed to determine the added value of baseline metabolic tumor volume (MTV) and interim PET (I-PET) to the age-adjusted international prognostic index (aaIPI) to predict 2-y progression-free survival (PFS) in diffuse large B-cell lymphoma. Secondary objectives were to investigate optimal I-PET response criteria (using Deauville score [DS] or quantitative change in SUVmax [ΔSUVmax] between baseline and I-PET4 [observational I-PET scans after 4 cycles of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone administered in 2-wk intervals with intensified rituximab in the first 4 cycles [R(R)-CHOP14]). Methods: I-PET4 scans in the HOVON-84 (Hemato-Oncologie voor Volwassenen Nederland [Haemato Oncology Foundation for Adults in the Netherlands]) randomized clinical trial (EudraCT 2006-005174-42) were centrally reviewed using DS (cutoff, 4-5). Additionally, ΔSUVmax (prespecified cutoff, 70%) and baseline MTV were measured. Multivariable hazard ratio (HR), positive predictive value (PPV), and negative predictive value (NPV) were obtained for 2-y PFS. Results: In total, 513 I-PET4 scans were reviewed according to DS, and ΔSUVmax and baseline MTV were available for 367 and 296 patients. The NPV of I-PET ranged between 82% and 86% for all PET response criteria. Univariate HR and PPV were better for ΔSUVmax (4.8% and 53%, respectively) than for DS (3.1% and 38%, respectively). aaIPI and ΔSUVmax independently predicted 2-y PFS (HR, 3.2 and 5.0, respectively); adding MTV brought about a slight improvement. Low or low-intermediate aaIPI combined with a ΔSUVmax of more than 70% (37% of patients) yielded an NPV of 93%, and the combination of high-intermediate or high aaIPI and a ΔSUVmax of 70% or less yielded a PPV of 65%. Conclusion: In this study on diffuse large B-cell lymphoma, I-PET after 4 cycles of R(R)-CHOP14 added predictive value to aaIPI for 2-y PFS, and both were independent response biomarkers in a multivariable Cox model. We externally validated that ΔSUVmax outperformed DS in 2-y PFS prediction.
Collapse
Affiliation(s)
- Coreline N. Burggraaff
- Department of Hematology, Amsterdam UMC, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jakoba J. Eertink
- Department of Hematology, Amsterdam UMC, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Pieternella J. Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Otto S. Hoekstra
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anne I.J. Arens
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Bart de Keizer
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martijn W. Heymans
- Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; and
| | - Bronno van der Holt
- Department of Hematology, HOVON Data Center, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Sanne E. Wiegers
- Department of Hematology, Amsterdam UMC, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Department of Hematology, Amsterdam UMC, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Henrica C.W. de Vet
- Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; and
| | - Josée M. Zijlstra
- Department of Hematology, Amsterdam UMC, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | |
Collapse
|
38
|
SAKK 35/15: a phase 1 trial of obinutuzumab in combination with venetoclax in patients with previously untreated follicular lymphoma. Blood Adv 2022; 6:3911-3920. [PMID: 35537101 PMCID: PMC9278307 DOI: 10.1182/bloodadvances.2021006520] [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: 11/02/2021] [Accepted: 04/29/2022] [Indexed: 11/20/2022] Open
Abstract
This phase I study evaluated safety, tolerability and preliminary efficacy of obinutuzumab in combination with venetoclax in patients with previously untreated grade 1-3a follicular lymphoma in need of systemic therapy. Two dose levels of venetoclax were evaluated with an expansion cohort at the recommended phase 2 dose. Twenty-five patients were enrolled. The recommended phase 2 dose was venetoclax 800mg once daily continuously for 6 cycles starting on day 2 of cycle 1 with obinutuzumab 1000mg on days 1,8,15 of cycle 1 and on day 1 of cycles 2-6, followed by obinutuzumab maintenance every 2 months for two years. Only one patient had a dose-limiting toxicity consisting of grade 4 thrombocytopenia after the first obinutuzumab infusion. Neutropenia was the most common adverse event of grade ≥3 at least possibly attributed to study treatment. Twenty-four patients were evaluable for response after cycle 6 by CT and 19 by PET/CT: overall and complete response rates were 87.5% (95% CI, 67.6-97.3%) and 25% (95% CI, 9.8-46.7%) in the CT evaluated patients and 84.2% (95% CI, 60.4-96.6%) and 68.4% (95% CI, 43.4-87.4%) respectively in the PET/CT evaluated patients. One-year progression-free survival was 77.8% (95% CI, 54.6-90.1%) and 79% (95% CI, 47.9-92.7%) for CT and PET/CT evaluable patients, respectively, while progression-free survival at 30 months was 73.2% (95% CI, 49.8%, 87.0%) as assessed by CT and 79.0% (95% CI, 47.9%, 92.7%) by PET/CT. Despite the activity observed our results do not support further development of the combination in this patient population. This trial is registered at www.clinicaltrials.gov as NCT02877550.
Collapse
|
39
|
Yhim H, Eshet Y, Metser U, Lajkosz K, Cooper M, Prica A, Kukreti V, Bhella S, Lang N, Xu W, Rodin D, Hodgson D, Tsang R, Crump M, Kuruvilla J, Kridel R. Risk stratification for relapsed/refractory classical Hodgkin lymphoma integrating pretransplant Deauville score and residual metabolic tumor volume. Am J Hematol 2022; 97:583-591. [PMID: 35170780 DOI: 10.1002/ajh.26500] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/31/2021] [Accepted: 02/07/2022] [Indexed: 11/09/2022]
Abstract
Pretransplant Deauville score (DS) is an imaging biomarker used for risk stratification in relapsed/refractory classical Hodgkin lymphoma (cHL). However, the prognostic value of residual metabolic tumor volume (rMTV) in patients with DS 4-5 has been less well characterized. We retrospectively assessed 106 patients with relapsed/refractory cHL who underwent autologous stem cell transplantation. Pretransplant DS was determined as 1-3 (59%) and 4-5 (41%), with a markedly inferior event-free survival (EFS) in patients with DS 4-5 (hazard ratio [HR], 3.14; p = .002). High rMTV41% (rMTVhigh , ≥4.4 cm3 ) predicted significantly poorer EFS in patients with DS 4-5 (HR, 3.70; p = .014). In a multivariable analysis, we identified two independent factors predicting treatment failure: pretransplant DS combined with rMTV41% and disease status (primary refractory vs. relapsed). These two factors allow to stratify patients into three groups with divergent 2-year EFS: 89% for low-risk (51%; relapsed disease and either pretransplant DS 1-3 or DS 4-5/rMTVlow ; HR 1), 65% for intermediate-risk (28%; refractory disease and either DS 1-3 or DS 4-5/rMTVlow ; HR 3.26), and 45% for high-risk (21%; DS 4-5/rMTVhigh irrespective of disease status; HR 7.61) groups. Pretransplant DS/rMTV41% combination and disease status predict the risk of post-transplant treatment failure and will guide risk-stratified approaches in relapsed/refractory cHL.
Collapse
Affiliation(s)
- Ho‐Young Yhim
- Division of Medical Oncology and Hematology Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
- Department of Internal Medicine Jeonbuk National University Medical School and Research Institute of Clinical Medicine of Jeonbuk National University‐Biomedical Research Institute of Jeonbuk National University Hospital Jeonju Republic of Korea
| | - Yael Eshet
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto Toronto Ontario Canada
| | - Ur Metser
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto Toronto Ontario Canada
| | - Katherine Lajkosz
- Department of Biostatistics, Princess Margaret Cancer Centre, Dalla Lana School of Public Health University of Toronto Toronto Ontario Canada
| | - Matthew Cooper
- Division of Medical Oncology and Hematology Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
- Faculty of Medicine Dalhousie University Halifax Nova Scotia Canada
| | - Anca Prica
- Division of Medical Oncology and Hematology Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
| | - Vishal Kukreti
- Division of Medical Oncology and Hematology Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
| | - Sita Bhella
- Division of Medical Oncology and Hematology Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
| | - Noémie Lang
- Division of Medical Oncology and Hematology Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Centre, Dalla Lana School of Public Health University of Toronto Toronto Ontario Canada
| | - Danielle Rodin
- Radiation Medicine Program Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
- Department of Radiation Oncology University of Toronto Toronto Ontario Canada
| | - David Hodgson
- Radiation Medicine Program Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
- Department of Radiation Oncology University of Toronto Toronto Ontario Canada
| | - Richard Tsang
- Radiation Medicine Program Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
- Department of Radiation Oncology University of Toronto Toronto Ontario Canada
| | - Michael Crump
- Division of Medical Oncology and Hematology Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
| | - John Kuruvilla
- Division of Medical Oncology and Hematology Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
| | - Robert Kridel
- Division of Medical Oncology and Hematology Princess Margaret Cancer Centre – University Health Network Toronto Ontario Canada
| |
Collapse
|
40
|
Automatic classification of lymphoma lesions in FDG-PET–Differentiation between tumor and non-tumor uptake. PLoS One 2022; 17:e0267275. [PMID: 35436321 PMCID: PMC9015138 DOI: 10.1371/journal.pone.0267275] [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: 01/31/2022] [Accepted: 04/05/2022] [Indexed: 11/27/2022] Open
Abstract
Introduction The automatic classification of lymphoma lesions in PET is a main topic of ongoing research. An automatic algorithm would enable the swift evaluation of PET parameters, like texture and heterogeneity markers, concerning their prognostic value for patients outcome in large datasets. Moreover, the determination of the metabolic tumor volume would be facilitated. The aim of our study was the development and evaluation of an automatic algorithm for segmentation and classification of lymphoma lesions in PET. Methods Pre-treatment PET scans from 60 Hodgkin lymphoma patients from the EuroNet-PHL-C1 trial were evaluated. A watershed algorithm was used for segmentation. For standardization of the scan length, an automatic cropping algorithm was developed. All segmented volumes were manually classified into one of 14 categories. The random forest method and a nested cross-validation was used for automatic classification and evaluation. Results Overall, 853 volumes were segmented and classified. 203/246 tumor lesions and 554/607 non-tumor volumes were classified correctly by the automatic algorithm, corresponding to a sensitivity, a specificity, a positive and a negative predictive value of 83%, 91%, 79% and 93%. In 44/60 (73%) patients, all tumor lesions were correctly classified. In ten out of the 16 patients with misclassified tumor lesions, only one false-negative tumor lesion occurred. The automatic classification of focal gastrointestinal uptake, brown fat tissue and composed volumes consisting of more than one tissue was challenging. Conclusion Our algorithm, trained on a small number of patients and on PET information only, showed a good performance and is suitable for automatic lymphoma classification.
Collapse
|
41
|
Husby T, Johansen H, Bogsrud T, Hustad KV, Evensen BV, Boellard R, Giskeødegård GF, Fagerli UM, Eikenes L. A comparison of FDG PET/MR and PET/CT for staging, response assessment, and prognostic imaging biomarkers in lymphoma. Ann Hematol 2022; 101:1077-1088. [PMID: 35174405 PMCID: PMC8993743 DOI: 10.1007/s00277-022-04789-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/08/2022] [Indexed: 12/16/2022]
Abstract
The aim of the current study was to investigate the diagnostic performance of FDG PET/MR compared to PET/CT in a patient cohort including Hodgkins lymphoma, diffuse large B-cell lymphoma, and high-grade B-cell lymphoma at baseline and response assessment. Sixty-one patients were examined with FDG PET/CT directly followed by PET/MR. Images were read by two pairs of nuclear medicine physicians and radiologists. Concordance for lymphoma involvement between PET/MR and the reference standard PET/CT was assessed at baseline and response assessment. Correlation of prognostic biomarkers Deauville score, criteria of response, SUVmax, SUVpeak, and MTV was performed between PET/MR and PET/CT. Baseline FDG PET/MR showed a sensitivity of 92.5% and a specificity 97.9% compared to the reference standard PET/CT (κ 0.91) for nodal sites. For extranodal sites, a sensitivity of 80.4% and a specificity of 99.5% were found (κ 0.84). Concordance in Ann Arbor was found in 57 of 61 patients (κ 0.92). Discrepancies were due to misclassification of region and not lesion detection. In response assessment, a sensitivity of 100% and a specificity 99.9% for all sites combined were found (κ 0.92). There was a perfect agreement on Deauville scores 4 and 5 and criteria of response between the two modalities. Intraclass correlation coefficient (ICC) for SUVmax, SUVpeak, and MTV values showed excellent reliability (ICC > 0.9). FDG PET/MR is a reliable alternative to PET/CT in this patient population, both in terms of lesion detection at baseline staging and response assessment, and for quantitative prognostic imaging biomarkers.
Collapse
Affiliation(s)
- Trine Husby
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks 8905, Trondheim, Norway.,Department of Oncology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Håkon Johansen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Trond Bogsrud
- PET-Centre, University Hospital of North Norway, Tromsø, Norway.,Aarhus University Hosipital, Aarhus, Denmark
| | - Kari Vekseth Hustad
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Birte Veslemøy Evensen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ronald Boellard
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, University Medical Centers Amsterdam, VUMC, Amsterdam, The Netherlands
| | - Guro F Giskeødegård
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Unn-Merete Fagerli
- Department of Oncology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Live Eikenes
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks 8905, Trondheim, Norway.
| |
Collapse
|
42
|
Revailler W, Cottereau AS, Rossi C, Noyelle R, Trouillard T, Morschhauser F, Casasnovas O, Thieblemont C, Le Gouill S, André M, Ghesquieres H, Ricci R, Meignan M, Kanoun S. Deep Learning Approach to Automatize TMTV Calculations Regardless of Segmentation Methodology for Major FDG-Avid Lymphomas. Diagnostics (Basel) 2022; 12:diagnostics12020417. [PMID: 35204515 PMCID: PMC8870809 DOI: 10.3390/diagnostics12020417] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 11/16/2022] Open
Abstract
The total metabolic tumor volume (TMTV) is a new prognostic factor in lymphomas that could benefit from automation with deep learning convolutional neural networks (CNN). Manual TMTV segmentations of 1218 baseline 18FDG-PET/CT have been used for training. A 3D V-NET model has been trained to generate segmentations with soft dice loss. Ground truth segmentation has been generated using a combination of different thresholds (TMTVprob), applied to the manual region of interest (Otsu, relative 41% and SUV 2.5 and 4 cutoffs). In total, 407 and 405 PET/CT were used for test and validation datasets, respectively. The training was completed in 93 h. In comparison with the TMTVprob, mean dice reached 0.84 in the training set, 0.84 in the validation set and 0.76 in the test set. The median dice scores for each TMTV methodology were 0.77, 0.70 and 0.90 for 41%, 2.5 and 4 cutoff, respectively. Differences in the median TMTV between manual and predicted TMTV were 32, 147 and 5 mL. Spearman’s correlations between manual and predicted TMTV were 0.92, 0.95 and 0.98. This generic deep learning model to compute TMTV in lymphomas can drastically reduce computation time of TMTV.
Collapse
Affiliation(s)
- Wendy Revailler
- Centre de Recherche Clinique de Toulouse, Team 9, 31100 Toulouse, France; (W.R.); (T.T.)
- Institut Universitaire du Cancer de Toulouse, Institut Claudius Regaud, Nuclear Medicine, 1 avenue Joliot Curie, 31000 Toulouse, France
| | - Anne Ségolène Cottereau
- Assistance Publique-Hôpitaux de Paris, Hôpital Cochin, Nuclear Medecine, René Descartes University, 75014 Paris, France;
| | - Cedric Rossi
- CHU Dijon, Hematology, 10 Boulevard Maréchal De Lattre De Tassigny, 21000 Dijon, France; (C.R.); (O.C.)
| | | | - Thomas Trouillard
- Centre de Recherche Clinique de Toulouse, Team 9, 31100 Toulouse, France; (W.R.); (T.T.)
- Institut Universitaire du Cancer de Toulouse, Institut Claudius Regaud, Nuclear Medicine, 1 avenue Joliot Curie, 31000 Toulouse, France
| | - Franck Morschhauser
- ULR 7365—GRITA—Groupe de Recherche sur les formes Injectables et les Technologies Associées, University of Lille, CHU Lille, 59000 Lille, France;
| | - Olivier Casasnovas
- CHU Dijon, Hematology, 10 Boulevard Maréchal De Lattre De Tassigny, 21000 Dijon, France; (C.R.); (O.C.)
| | - Catherine Thieblemont
- Hemato-Oncology Unit, Saint-Louis University Hospital Center, Public Hospital Network of Paris, 75010 Paris, France;
| | - Steven Le Gouill
- Department of Hematology, Nantes University Hospital, INSERM CRCINA Nantes-Angers, NeXT Université de Nantes, 44000 Nantes, France;
| | - Marc André
- Department of Hematology, Université catholique de Louvain, CHU UcL Namur, 5530 Yvoir, Belgium;
| | - Herve Ghesquieres
- Department of Hematology, Hôpital Lyon Sud, Hospices Civils de Lyon, 69310 Pierre-Bénite, France;
| | - Romain Ricci
- LYSARC, Centre Hospitalier Lyon-Sud, 165 Chemin du Grand Revoyet Bâtiment 2D, 69310 Pierre-Bénite, France;
| | - Michel Meignan
- LYSA Imaging, Henri Mondor University Hospital, AP-HP, University Paris East, 94000 Créteil, France;
| | - Salim Kanoun
- Centre de Recherche Clinique de Toulouse, Team 9, 31100 Toulouse, France; (W.R.); (T.T.)
- Institut Universitaire du Cancer de Toulouse, Institut Claudius Regaud, Nuclear Medicine, 1 avenue Joliot Curie, 31000 Toulouse, France
- Correspondence: ; Tel.: +33-6-88-62-81-18
| |
Collapse
|
43
|
El-Galaly TC, Villa D, Cheah CY, Gormsen LC. Pre-treatment total metabolic tumour volumes in lymphoma: Does quantity matter? Br J Haematol 2022; 197:139-155. [PMID: 35037240 DOI: 10.1111/bjh.18016] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 11/23/2021] [Accepted: 12/10/2021] [Indexed: 11/28/2022]
Abstract
Positron emission tomography/computed tomography (PET/CT) is used for the staging of lymphomas. Clinical information, such as Ann Arbor stage and number of involved sites, is derived from baseline staging and correlates with tumour volume. With modern imaging software, exact measures of total metabolic tumour volumes (tMTV) can be determined, in a semi- or fully-automated manner. Several technical factors, such as tumour segmentation and PET/CT technology influence tMTV and there is no consensus on a standardized uptake value (SUV) thresholding method, or how to include the volumes in the bone marrow and spleen. In diffuse large B-cell lymphoma, follicular lymphoma, peripheral T-cell lymphoma, and Hodgkin lymphoma, tMTV has been shown to predict progression-free survival and/or overall survival, after adjustments for clinical risk scores. However, most studies have used receiver operating curves to determine the optimal cut-off for tMTV and many studies did not include a training-validation approach, which led to the risk of overestimation of the independent prognostic value of tMTV. The identified cut-off values are heterogeneous, even when the same SUV thresholding method is used. Future studies should focus on testing tMTV in homogeneously-treated cohorts and seek to validate identified cut-off values externally so that a prognostic value can be documented, over and above currently used clinical surrogates for tumour volume.
Collapse
Affiliation(s)
- Tarec Christoffer El-Galaly
- Department of Haematology, Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark.,Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Diego Villa
- BC Cancer Centre for Lymphoid Cancer and University of British Columbia, Vancouver, British Columbia, Canada
| | - Chan Yoon Cheah
- Department of Haematology, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia.,Medical School, University of Western Australia, Perth, Western Australia, Australia
| | - Lars C Gormsen
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
| |
Collapse
|
44
|
Minson A, Hofman M, Dickinson M. A PET in a time of need: toward early PET-adapted therapy in DLBCL in first relapse. Leuk Lymphoma 2021; 63:1-4. [PMID: 34915805 DOI: 10.1080/10428194.2021.2015345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Salvage chemotherapy and autologous stem cell transplant remain a standard of care in the management of diffuse large B cell lymphoma (DLBCL) at first relapse. However, this paradigm is increasingly being challenged by novel immunotherapies, such as chimeric antigen receptor T-cells (CART-cells). Traditional positron emission tomography-based (PET) prognostication takes place after salvage and before autologous stem cell transplant (ASCT), and while useful, for many patients this information comes too late and at the expense of unnecessary toxicity. In this edition of Leukemia & Lymphoma, two groups present their findings on the use of early quantitative PET markers and the correlation with outcomes in patients embarking on second line salvage chemotherapy. These approaches have the potential to better identify patients who are destined for treatment failure and help guide appropriate sequencing of alternative therapies or the development of PET-adapted clinical trials.
Collapse
Affiliation(s)
- Adrian Minson
- Department of Clinical Haematology, Peter MacCallum Cancer Centre, Melbourne, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Michael Hofman
- Centre for Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia.,Department of Medicine, University of Melbourne, Melbourne, Australia
| | - Michael Dickinson
- Department of Clinical Haematology, Peter MacCallum Cancer Centre, Melbourne, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| |
Collapse
|
45
|
Abstract
PURPOSE OF REVIEW Functional imaging with 18FDG-PET-CT has transformed the staging and response assessment of patients with Hodgkin (HL) and non-Hodgkin lymphoma (NHL). Herein, we review the current role and future directions for functional imaging in the management of patients with lymphoma. RECENT FINDINGS Because of its increased sensitivity, PET-CT is the preferred modality for staging of FDG-avid lymphomas. It appears to have a role for interim assessment in patients with HL with adaptive strategies that reduce toxicity in lower risk patients and increase efficacy in those at high risk. Such a role has yet to be demonstrated in other histologies. FDG-PET-CT is also the gold standard for response assessment posttreatment. Newer uses include assessment of total metabolic tumor volume and radiomics in pretreatment prognosis. Whereas PET-CT is more sensitive than other current modalities for staging and response assessment, the future of PET-CT will be in conjunction with other modalities, notably assessment of minimal residual disease and microenvironmental markers to develop risk adaptive strategies to improve the outcome of patients with lymphoma.
Collapse
|
46
|
Eertink JJ, Arens AIJ, Huijbregts JE, Celik F, de Keizer B, Stroobants S, de Jong D, Wiegers SE, Zwezerijnen GJC, Burggraaff CN, Boellaard R, de Vet HCW, Hoekstra OS, Lugtenburg PJ, Chamuleau MED, Zijlstra JM. Aberrant patterns of PET response during treatment for DLBCL patients with MYC gene rearrangements. Eur J Nucl Med Mol Imaging 2021; 49:943-952. [PMID: 34476551 PMCID: PMC8803795 DOI: 10.1007/s00259-021-05498-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/13/2021] [Indexed: 11/29/2022]
Abstract
Purpose MYC gene rearrangements in diffuse large B-cell lymphoma (DLBCL) patients are associated with poor prognosis. Our aim was to compare patterns of 2[18F]fluoro-2-deoxy-D-glucose positron emission tomography computed tomography (PET/CT) response in MYC + and MYC- DLBCL patients. Methods Interim PET/CT (I-PET) and end of treatment PET/CT (EoT-PET) scans of 81 MYC + and 129 MYC- DLBCL patients from 2 HOVON trials were reviewed using the Deauville 5-point scale (DS). DS1-3 was regarded as negative and DS4-5 as positive. Standardized uptake values (SUV) and metabolic tumor volume (MTV) were quantified at baseline, I-PET, and EoT-PET. Negative (NPV) and positive predictive values (PPV) were calculated using 2-year overall survival. Results MYC + DLBCL patients had significantly more positive EoT-PET scans than MYC- patients (32.5 vs 15.7%, p = 0.004). I-PET positivity rates were comparable (28.8 vs 23.8%). In MYC + patients 23.2% of the I-PET negative patients converted to positive at EoT-PET, vs only 2% for the MYC- patients (p = 0.002). Nine (34.6%) MYC + DLBCL showed initially uninvolved localizations at EoT-PET, compared to one (5.3%) MYC- patient. A total of 80.8% of EoT-PET positive MYC + patients showed both increased lesional SUV and MTV compared to I-PET. In MYC- patients, 31.6% showed increased SUV and 42.1% showed increased MTV. NPV of I-PET and EoT-PET was high for both MYC subgroups (81.8–94.1%). PPV was highest at EoT-PET for MYC + patients (61.5%). Conclusion MYC + DLBCL patients demonstrate aberrant PET response patterns compared to MYC- patients with more frequent progression during treatment after I-PET negative assessment and new lesions at sites that were not initially involved. Trial registration number and date of registration HOVON-84: EudraCT: 2006–005,174-42, retrospectively registered 01–08-2008. HOVON-130: EudraCT: 2014–002,654-39, registered 26–01-2015 Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05498-7.
Collapse
Affiliation(s)
- J J Eertink
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - A I J Arens
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, The Netherlands
| | - J E Huijbregts
- Department of Radiology and Nuclear Medicine, Gelre Ziekenhuizen, Albert Schweitzerlaan 31, Apeldoorn, The Netherlands
| | - F Celik
- Department of Radiology and Nuclear Medicine, Deventer Ziekenhuis, Nico Bolkesteinlaan 75, Deventer, The Netherlands
| | - B de Keizer
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, The Netherlands
| | - S Stroobants
- Department of Nuclear Medicine, Antwerp University Hospital (UZA), Antwerp, Belgium
| | - D de Jong
- Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - S E Wiegers
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - G J C Zwezerijnen
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - C N Burggraaff
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - R Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - H C W de Vet
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - O S Hoekstra
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - P J Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Wytemaweg 80, Rotterdam, The Netherlands
| | - M E D Chamuleau
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - J M Zijlstra
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
| | | |
Collapse
|
47
|
18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma. Eur J Nucl Med Mol Imaging 2021; 49:932-942. [PMID: 34405277 PMCID: PMC8803694 DOI: 10.1007/s00259-021-05480-3] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/17/2021] [Indexed: 12/21/2022]
Abstract
Purpose Accurate prognostic markers are urgently needed to identify diffuse large B-Cell lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to investigate the potential added value of baseline radiomics features to the international prognostic index (IPI) in predicting outcome after first-line treatment. Methods Three hundred seventeen newly diagnosed DLBCL patients were included. Lesions were delineated using a semi-automated segmentation method (standardized uptake value ≥ 4.0), and 490 radiomics features were extracted. We used logistic regression with backward feature selection to predict 2-year time to progression (TTP). The area under the curve (AUC) of the receiver operator characteristic curve was calculated to assess model performance. High-risk groups were defined based on prevalence of events; diagnostic performance was assessed using positive and negative predictive values. Results The IPI model yielded an AUC of 0.68. The optimal radiomics model comprised the natural logarithms of metabolic tumor volume (MTV) and of SUVpeak and the maximal distance between the largest lesion and any other lesion (Dmaxbulk, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUVpeak and Dmaxbulk) and patient-related parameters (WHO performance status and age > 60 years) performed best (AUC 0.79). Adding radiomics features to clinical predictors increased PPV with 15%, with more accurate selection of high-risk patients compared to the IPI model (progression at 2-year TTP, 44% vs 28%, respectively). Conclusion Prediction models using baseline radiomics combined with currently used clinical predictors identify patients at risk of relapse at baseline and significantly improved model performance. Trial registration number and date EudraCT: 2006–005,174-42, 01–08-2008. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05480-3.
Collapse
|
48
|
Eertink JJ, Pfaehler EAG, Wiegers SE, van de Brug T, Lugtenburg PJ, Hoekstra OS, Zijlstra JM, de Vet HCW, Boellaard R. Quantitative radiomics features in diffuse large B-cell lymphoma: does segmentation method matter? J Nucl Med 2021; 63:389-395. [PMID: 34272315 DOI: 10.2967/jnumed.121.262117] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 06/03/2021] [Indexed: 11/16/2022] Open
Abstract
INTRODUCTION Radiomics features may predict outcome in diffuse large B-cell lymphoma (DLBCL). Currently, multiple segmentation methods are used to calculate metabolic tumor volume (MTV). We assessed the influence of segmentation method on the discriminative power of radiomics features in DLBCL for patient level and for the largest lesion. Methods: 50 baseline 18F-fluorodeoxyglucose positron emission tomography computed tomography (PET/CT) scans of DLBCL patients who progressed or relapsed within 2 years after diagnosis were matched on uptake time and reconstruction method with 50 baseline PET/CT scans of DLBCL patients without progression. Scans were analysed using 6 semi-automatic segmentation methods (standardized uptake value (SUV)4.0, SUV2.5, 41% of the maximum SUV, 50% of the SUVpeak, majority vote (MV)2 and MV3, respectively). Based on these segmentations, 490 radiomics features were extracted at patient level and 486 features for the largest lesion. To quantify the agreement between features extracted from different segmentation methods, the intra-class correlation (ICC) agreement was calculated for each method compared to SUV4.0. The feature space was reduced by deleting features that had high Pearson correlations (≥0.7) with the previously established predictors MTV and/or SUVpeak. Model performance was assessed using stratified repeated cross-validation with 5 folds and 2000 repeats yielding the mean receiver-operating characteristics curve integral (CV-AUC) for all segmentation methods using logistic regression with backward feature selection. Results: The percentage of features yielding an ICC ≥0.75 compared to the SUV4.0 segmentation was lowest for A50P both at patient level and for the largest lesion, with 77.3% and 66.7% of the features yielding an ICC ≥0.75, respectively. Features were not highly correlated with MTV, with at least 435 features at patient level and 409 features for the largest lesion for all segmentation methods with a correlation coefficient <0.7. Features were highly correlated with SUVpeak (at least 190 and 134 were uncorrelated, respectively). CV-AUCs ranged between 0.69±0.11 and 0.84±0.09 for patient level, and between 0.69±0.11 and 0.73±0.10 for lesion level. Conclusion: Even though there are differences in the actual radiomics feature values derived and selected features between segmentation methods, there is no substantial difference in the discriminative power of radiomics features between segmentation methods.
Collapse
Affiliation(s)
- Jakoba J Eertink
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center Amsterdam, Netherlands
| | | | - Sanne E Wiegers
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center Amsterdam, Netherlands
| | - Tim van de Brug
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Epidemiology and Data Science, Amsterdam Public Health research institute, Netherlands
| | - Pieternella J Lugtenburg
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, department of Hematology, Netherlands
| | - Otto S Hoekstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Netherlands
| | - Josee M Zijlstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center Amsterdam, Netherlands
| | - Henrica C W de Vet
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Epidemiology and Data Science, Amsterdam Public Health research institute, Netherlands
| | - Ronald Boellaard
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Netherlands
| |
Collapse
|
49
|
Zwezerijnen GJ, Eertink JJ, Burggraaff CN, Wiegers SE, Shaban EA, Pieplenbosch S, Oprea-Lager DE, Lugtenburg PJ, Hoekstra OS, de Vet HC, Zijlstra JM, Boellaard R. Interobserver agreement in automated metabolic tumor volume measurements of Deauville score 4 and 5 lesions at interim 18F-FDG PET in DLBCL. J Nucl Med 2021; 62:1531-1536. [PMID: 33674403 DOI: 10.2967/jnumed.120.258673] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 02/16/2021] [Indexed: 11/16/2022] Open
Abstract
Introduction: Metabolic tumor volume (MTV) on interim-PET (I-PET) is a potential prognostic biomarker for diffuse large B-cell lymphoma (DLBCL). Implementation of MTV on I-PET requires consensus which semi-automated segmentation method delineates lesions most successfully with least user interaction. Methods used for baseline PET are not necessarily optimal for I-PET due to lower lesional standardized uptake values (SUV) at I-PET. Therefore, we aimed to evaluate which method provides the best delineation quality of Deauville-score (DS) 4-5 DLBCL lesions on I-PET at best interobserver agreement on delineation quality and, secondly, to assess the effect of lesional SUVmax on delineation quality and performance agreements. Methods: DS4-5 lesions from 45 I-PET scans were delineated using six semi-automated methods i) SUV 2.5, ii) SUV 4.0, iii) adaptive threshold [A50%peak], iv) 41% of maximum SUV [41%max], v) majority vote including voxels detected by ≥2 methods [MV2] and vi) detected by ≥3 methods [MV3]. Delineation quality per MTV was rated by three independent observers as acceptable or non-acceptable. For each method, observer scores on delineation quality, specific agreements and MTV were assessed for all lesions, and per category of lesional SUVmax (<5, 5-10, >10). Results: In 60 DS4-5 lesions on I-PET, MV3 performed best, with acceptable delineation in 90% of lesions, with a positive agreement (PA) of 93%. Delineation quality scores and agreements per method strongly depended on lesional SUV: the best delineation quality scores were obtained using MV3 in lesions with SUVmax<10 and SUV4.0 in more FDG-avid lesions. Consequently, overall delineation quality and PA improved by applying the most preferred method per SUV category instead of using MV3 as single best method. MV3- and SUV4.0-derived MTVs of lesions with SUVmax>10, were comparable after excluding visually failed MV3 contouring. For lesions with SUVmax<10, MTVs using different methods correlated poorly. Conclusion: On I-PET, MV3 performed best and provided the highest interobserver agreement regarding acceptable delineations of DS4-5 DLBCL lesions. However, delineation method preference strongly depended on lesional SUV. Therefore, we suggest to explore an approach that identifies the optimal delineation method per lesion as function of tumor FDG uptake characteristics, i.e. SUVmax.
Collapse
Affiliation(s)
- Gerben Jc Zwezerijnen
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Netherlands
| | - Jakoba J Eertink
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center Amsterdam, Netherlands
| | - Coreline N Burggraaff
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center Amsterdam, Netherlands
| | - Sanne E Wiegers
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center Amsterdam, Netherlands
| | - Ekhlas A Shaban
- Radiodiagnosis and medical imaging department, Faculty of Medicine, Tanta University, Egypt
| | - Simone Pieplenbosch
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Netherlands
| | - Daniela E Oprea-Lager
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Netherlands
| | | | - Otto S Hoekstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Netherlands
| | - Henrica Cw de Vet
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Epidemiology and Data Science, Amsterdam Public Health research institute, Netherlands
| | - Josee M Zijlstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center Amsterdam, Netherlands
| | - Ronald Boellaard
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Netherlands
| |
Collapse
|
50
|
Barrington S, Zwezerijnen BG, de Vet HC, Heymans MW, Boellaard R. Reply to LTE: Automated segmentation of TMTV in DLBCL patients: what about method measurement uncertainty? J Nucl Med 2020; 62:jnumed.120.257030. [PMID: 33127620 DOI: 10.2967/jnumed.120.257030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 10/14/2020] [Indexed: 11/16/2022] Open
Affiliation(s)
- Sally Barrington
- Kings College London and Guy's and St Thomas' PET Imaging Centre, School of Biomedical Engineering and Imaging Sciences, United Kingdom
| | - Ben Gjc Zwezerijnen
- Department of Radiology and Nuclear Medicine at Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands
| | - Henrica Cw de Vet
- Department of Epidemiology & Data Science at Amsterdam UMC, Vrije Universiteit Amsterdam
| | - Martijn W Heymans
- Department of Epidemiology & Data Science at Amsterdam UMC, Vrije Universiteit Amsterdam
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine at Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands
| |
Collapse
|