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Sachpekidis C, Enqvist O, Ulén J, Kopp-Schneider A, Pan L, Mai EK, Hajiyianni M, Merz M, Raab MS, Jauch A, Goldschmidt H, Edenbrandt L, Dimitrakopoulou-Strauss A. Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [ 18F]FDG PET/CT predicts survival in multiple myeloma. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06668-z. [PMID: 38456971 DOI: 10.1007/s00259-024-06668-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 02/25/2024] [Indexed: 03/09/2024]
Abstract
PURPOSE Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [18F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group has recently demonstrated the feasibility of automated, volumetric assessment of bone marrow (BM) metabolic activity on PET/CT using a novel artificial intelligence (AI)-based tool. Accordingly, the aim of the current study is to investigate the prognostic role of whole-body calculations of BM metabolism in patients with newly diagnosed MM using this AI tool. MATERIALS AND METHODS Forty-four, previously untreated MM patients underwent whole-body [18F]FDG PET/CT. Automated PET/CT image segmentation and volumetric quantification of BM metabolism were based on an initial CT-based segmentation of the skeleton, its transfer to the standardized uptake value (SUV) PET images, subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, ten different uptake thresholds (AI approaches), based on reference organs or absolute SUV values, were applied for definition of pathological tracer uptake and subsequent calculation of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Correlation analysis was performed between the automated PET values and histopathological results of the BM as well as patients' progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic (ROC) curve analysis was used to investigate the discrimination performance of MTV and TLG for prediction of 2-year PFS. The prognostic performance of the new Italian Myeloma criteria for PET Use (IMPeTUs) was also investigated. RESULTS Median follow-up [95% CI] of the patient cohort was 110 months [105-123 months]. AI-based BM segmentation and calculation of MTV and TLG were feasible in all patients. A significant, positive, moderate correlation was observed between the automated quantitative whole-body PET/CT parameters, MTV and TLG, and BM plasma cell infiltration for all ten [18F]FDG uptake thresholds. With regard to PFS, univariable analysis for both MTV and TLG predicted patient outcome reasonably well for all AI approaches. Adjusting for cytogenetic abnormalities and BM plasma cell infiltration rate, multivariable analysis also showed prognostic significance for high MTV, which defined pathological [18F]FDG uptake in the BM via the liver. In terms of OS, univariable and multivariable analysis showed that whole-body MTV, again mainly using liver uptake as reference, was significantly associated with shorter survival. In line with these findings, ROC curve analysis showed that MTV and TLG, assessed using liver-based cut-offs, could predict 2-year PFS rates. The application of IMPeTUs showed that the number of focal hypermetabolic BM lesions and extramedullary disease had an adverse effect on PFS. CONCLUSIONS The AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions.
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Affiliation(s)
- Christos Sachpekidis
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69210, Heidelberg, Germany.
| | - Olof Enqvist
- Eigenvision AB, Malmö, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | | | | | - Leyun Pan
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69210, Heidelberg, Germany
| | - Elias K Mai
- Department of Internal Medicine V, University Hospital Heidelberg and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Marina Hajiyianni
- Department of Internal Medicine V, University Hospital Heidelberg and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Maximilian Merz
- Department of Hematology and Cell Therapy, University of Leipzig, Leipzig, Germany
| | - Marc S Raab
- Department of Internal Medicine V, University Hospital Heidelberg and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Anna Jauch
- Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany
| | - Hartmut Goldschmidt
- Department of Internal Medicine V, University Hospital Heidelberg and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Lars Edenbrandt
- Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Antonia Dimitrakopoulou-Strauss
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69210, Heidelberg, Germany
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2
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Sachpekidis C, Enqvist O, Ulén J, Kopp-Schneider A, Pan L, Jauch A, Hajiyianni M, John L, Weinhold N, Sauer S, Goldschmidt H, Edenbrandt L, Dimitrakopoulou-Strauss A. Application of an artificial intelligence-based tool in [ 18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma. Eur J Nucl Med Mol Imaging 2023; 50:3697-3708. [PMID: 37493665 PMCID: PMC10547616 DOI: 10.1007/s00259-023-06339-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/09/2023] [Indexed: 07/27/2023]
Abstract
PURPOSE [18F]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients. MATERIALS AND METHODS Whole-body [18F]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [18F]FDG-avid lesions as well as the degree of diffuse [18F]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1-6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian × 1.1 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 2: liver SUVmedian × 1.5 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 3: liver SUVmedian × 2 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton), ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients. RESULTS BM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [18F]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of β2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5. CONCLUSIONS The automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts.
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Affiliation(s)
- Christos Sachpekidis
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69210, Heidelberg, Germany.
| | - Olof Enqvist
- Eigenvision AB, Malmö, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | | | | | - Leyun Pan
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69210, Heidelberg, Germany
| | - Anna Jauch
- Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany
| | - Marina Hajiyianni
- Department of Internal Medicine V, University Hospital Heidelberg and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Lukas John
- Department of Internal Medicine V, University Hospital Heidelberg and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Niels Weinhold
- Department of Internal Medicine V, University Hospital Heidelberg and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Sandra Sauer
- Department of Internal Medicine V, University Hospital Heidelberg and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Hartmut Goldschmidt
- Department of Internal Medicine V, University Hospital Heidelberg and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Lars Edenbrandt
- Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Antonia Dimitrakopoulou-Strauss
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69210, Heidelberg, Germany
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3
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Nikulin P, Zschaeck S, Maus J, Cegla P, Lombardo E, Furth C, Kaźmierska J, Rogasch JMM, Holzgreve A, Albert NL, Ferentinos K, Strouthos I, Hajiyianni M, Marschner SN, Belka C, Landry G, Cholewinski W, Kotzerke J, Hofheinz F, van den Hoff J. A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [Formula: see text]F]FDG PET/CT. Eur J Nucl Med Mol Imaging 2023; 50:2751-2766. [PMID: 37079128 PMCID: PMC10317885 DOI: 10.1007/s00259-023-06197-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/14/2023] [Indexed: 04/21/2023]
Abstract
PURPOSE PET-derived metabolic tumor volume (MTV) and total lesion glycolysis of the primary tumor are known to be prognostic of clinical outcome in head and neck cancer (HNC). Including evaluation of lymph node metastases can further increase the prognostic value of PET but accurate manual delineation and classification of all lesions is time-consuming and prone to interobserver variability. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in PET/CT investigations of HNC patients. METHODS Automated lesion delineation was performed with a residual 3D U-Net convolutional neural network (CNN) incorporating a multi-head self-attention block. 698 [Formula: see text]F]FDG PET/CT scans from 3 different sites and 5 public databases were used for network training and testing. An external dataset of 181 [Formula: see text]F]FDG PET/CT scans from 2 additional sites was employed to assess the generalizability of the network. In these data, primary tumor and lymph node (LN) metastases were interactively delineated and labeled by two experienced physicians. Performance of the trained network models was assessed by 5-fold cross-validation in the main dataset and by pooling results from the 5 developed models in the external dataset. The Dice similarity coefficient (DSC) for individual delineation tasks and the primary tumor/metastasis classification accuracy were used as evaluation metrics. Additionally, a survival analysis using univariate Cox regression was performed comparing achieved group separation for manual and automated delineation, respectively. RESULTS In the cross-validation experiment, delineation of all malignant lesions with the trained U-Net models achieves DSC of 0.885, 0.805, and 0.870 for primary tumor, LN metastases, and the union of both, respectively. In external testing, the DSC reaches 0.850, 0.724, and 0.823 for primary tumor, LN metastases, and the union of both, respectively. The voxel classification accuracy was 98.0% and 97.9% in cross-validation and external data, respectively. Univariate Cox analysis in the cross-validation and the external testing reveals that manually and automatically derived total MTVs are both highly prognostic with respect to overall survival, yielding essentially identical hazard ratios (HR) ([Formula: see text]; [Formula: see text] vs. [Formula: see text]; [Formula: see text] in cross-validation and [Formula: see text]; [Formula: see text] vs. [Formula: see text]; [Formula: see text] in external testing). CONCLUSION To the best of our knowledge, this work presents the first CNN model for successful MTV delineation and lesion classification in HNC. In the vast majority of patients, the network performs satisfactory delineation and classification of primary tumor and lymph node metastases and only rarely requires more than minimal manual correction. It is thus able to massively facilitate study data evaluation in large patient groups and also does have clear potential for supervised clinical application.
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Affiliation(s)
- Pavel Nikulin
- Helmholtz-Zentrum Dresden-Rossendorf, PET Center, Institute of Radiopharmaceutical Cancer Research, Bautzner Landstrasse 400, 01328, Dresden, Germany.
| | - Sebastian Zschaeck
- Department of Radiation Oncology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jens Maus
- Helmholtz-Zentrum Dresden-Rossendorf, PET Center, Institute of Radiopharmaceutical Cancer Research, Bautzner Landstrasse 400, 01328, Dresden, Germany
| | - Paulina Cegla
- Department of Nuclear Medicine, Greater Poland Cancer Centre, Poznan, Poland
| | - Elia Lombardo
- Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Christian Furth
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Joanna Kaźmierska
- Electroradiology Department, University of Medical Sciences, Poznan, Poland
- Radiotherapy Department II, Greater Poland Cancer Centre, Poznan, Poland
| | - Julian M M Rogasch
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Nathalie L Albert
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Konstantinos Ferentinos
- Department of Radiation Oncology, German Oncology Center, European University Cyprus, Limassol, Cyprus
| | - Iosif Strouthos
- Department of Radiation Oncology, German Oncology Center, European University Cyprus, Limassol, Cyprus
| | - Marina Hajiyianni
- Department of Radiation Oncology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian N Marschner
- Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Witold Cholewinski
- Department of Nuclear Medicine, Greater Poland Cancer Centre, Poznan, Poland
- Electroradiology Department, University of Medical Sciences, Poznan, Poland
| | - Jörg Kotzerke
- Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Frank Hofheinz
- Helmholtz-Zentrum Dresden-Rossendorf, PET Center, Institute of Radiopharmaceutical Cancer Research, Bautzner Landstrasse 400, 01328, Dresden, Germany
| | - Jörg van den Hoff
- Helmholtz-Zentrum Dresden-Rossendorf, PET Center, Institute of Radiopharmaceutical Cancer Research, Bautzner Landstrasse 400, 01328, Dresden, Germany
- Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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4
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Zschaeck S, Weingärtner J, Lombardo E, Marschner S, Hajiyianni M, Beck M, Zips D, Li Y, Lin Q, Amthauer H, Troost EGC, van den Hoff J, Budach V, Kotzerke J, Ferentinos K, Karagiannis E, Kaul D, Gregoire V, Holzgreve A, Albert NL, Nikulin P, Bachmann M, Kopka K, Krause M, Baumann M, Kazmierska J, Cegla P, Cholewinski W, Strouthos I, Zöphel K, Majchrzak E, Landry G, Belka C, Stromberger C, Hofheinz F. 18F-Fluorodeoxyglucose Positron Emission Tomography of Head and Neck Cancer: Location and HPV Specific Parameters for Potential Treatment Individualization. Front Oncol 2022; 12:870319. [PMID: 35756665 PMCID: PMC9213669 DOI: 10.3389/fonc.2022.870319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 04/29/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is utilized for staging and treatment planning of head and neck squamous cell carcinomas (HNSCC). Some older publications on the prognostic relevance showed inconclusive results, most probably due to small study sizes. This study evaluates the prognostic and potentially predictive value of FDG-PET in a large multi-center analysis. Methods Original analysis of individual FDG-PET and patient data from 16 international centers (8 institutional datasets, 8 public repositories) with 1104 patients. All patients received curative intent radiotherapy/chemoradiation (CRT) and pre-treatment FDG-PET imaging. Primary tumors were semi-automatically delineated for calculation of SUVmax, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Cox regression analyses were performed for event-free survival (EFS), overall survival (OS), loco-regional control (LRC) and freedom from distant metastases (FFDM). Results FDG-PET parameters were associated with patient outcome in the whole cohort regarding clinical endpoints (EFS, OS, LRC, FFDM), in uni- and multivariate Cox regression analyses. Several previously published cut-off values were successfully validated. Subgroup analyses identified tumor- and human papillomavirus (HPV) specific parameters. In HPV positive oropharynx cancer (OPC) SUVmax was well suited to identify patients with excellent LRC for organ preservation. Patients with SUVmax of 14 or less were unlikely to develop loco-regional recurrence after definitive CRT. In contrast FDG PET parameters deliver only limited prognostic information in laryngeal cancer. Conclusion FDG-PET parameters bear considerable prognostic value in HNSCC and potential predictive value in subgroups of patients, especially regarding treatment de-intensification and organ-preservation. The potential predictive value needs further validation in appropriate control groups. Further research on advanced imaging approaches including radiomics or artificial intelligence methods should implement the identified cut-off values as benchmark routine imaging parameters.
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Affiliation(s)
- Sebastian Zschaeck
- Department of Radiation Oncology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ) Heidelberg, Germany, Germany.,OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Julian Weingärtner
- Department of Radiation Oncology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Elia Lombardo
- Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Sebastian Marschner
- Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Marina Hajiyianni
- Department of Radiation Oncology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Marcus Beck
- Department of Radiation Oncology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Daniel Zips
- Department of Radiation Oncology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.,German Cancer Consortium (DKTK), Partner Site Tübingen, and German Cancer Research Center (DKFZ) Heidelberg, Germany, Germany.,Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Yimin Li
- Department of Radiation Oncology, Xiamen Cancer Center, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Qin Lin
- Department of Radiation Oncology, Xiamen Cancer Center, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Holger Amthauer
- Department of Nuclear Medicine, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Esther G C Troost
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ) Heidelberg, Germany, Germany.,OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Institute of Radiooncology - OncoRay, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Jörg van den Hoff
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Volker Budach
- Department of Radiation Oncology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jörg Kotzerke
- German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ) Heidelberg, Germany, Germany.,OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Department of Nuclear Medicine, Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden, Germany
| | - Konstantinos Ferentinos
- Department of Radiation Oncology, German Oncology Center, European University Cyprus, Limassol, Cyprus
| | - Efstratios Karagiannis
- Department of Radiation Oncology, German Oncology Center, European University Cyprus, Limassol, Cyprus
| | - David Kaul
- Department of Radiation Oncology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Vincent Gregoire
- Radiation Oncology Department, Leon Bérard Cancer Center, Lyon, France
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Germany
| | - Nathalie L Albert
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Germany
| | - Pavel Nikulin
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Michael Bachmann
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Klaus Kopka
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Mechthild Krause
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ) Heidelberg, Germany, Germany.,OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Institute of Radiooncology - OncoRay, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Michael Baumann
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ) Heidelberg, Germany, Germany.,OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Institute of Radiooncology - OncoRay, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Joanna Kazmierska
- Electroradiology Department, University of Medical Sciences, Poznan, Poland.,Radiotherapy Department II, Greater Poland Cancer Centre, Poznan, Poland
| | - Paulina Cegla
- Department of Nuclear Medicine, Greater Poland Cancer Centre, Poznan, Poland
| | - Witold Cholewinski
- Electroradiology Department, University of Medical Sciences, Poznan, Poland.,Department of Nuclear Medicine, Greater Poland Cancer Centre, Poznan, Poland
| | - Iosif Strouthos
- Department of Radiation Oncology, German Oncology Center, European University Cyprus, Limassol, Cyprus
| | - Klaus Zöphel
- German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ) Heidelberg, Germany, Germany.,OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Department of Nuclear Medicine, Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden, Germany.,Department of Nuclear Medicine, Klinikum Chemnitz gGmbH, Chemnitz, Germany
| | - Ewa Majchrzak
- Department of Head and Neck Surgery, Poznan University of Medical Sciences, Greater Poland Cancer Centre, Poznan, Poland
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Carmen Stromberger
- Department of Radiation Oncology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Frank Hofheinz
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
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Zschaeck S, Li Y, Lin Q, Beck M, Amthauer H, Bauersachs L, Hajiyianni M, Rogasch J, Ehrhardt VH, Kalinauskaite G, Weingärtner J, Hartmann V, van den Hoff J, Budach V, Stromberger C, Hofheinz F. Prognostic value of baseline [18F]-fluorodeoxyglucose positron emission tomography parameters MTV, TLG and asphericity in an international multicenter cohort of nasopharyngeal carcinoma patients. PLoS One 2020; 15:e0236841. [PMID: 32730364 PMCID: PMC7392321 DOI: 10.1371/journal.pone.0236841] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/14/2020] [Indexed: 01/02/2023] Open
Abstract
Purpose [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) parameters have shown prognostic value in nasopharyngeal carcinomas (NPC), mostly in monocenter studies. The aim of this study was to assess the prognostic impact of standard and novel PET parameters in a multicenter cohort of patients. Methods The established PET parameters metabolic tumor volume (MTV), total lesion glycolysis (TLG) and maximal standardized uptake value (SUVmax) as well as the novel parameter tumor asphericity (ASP) were evaluated in a retrospective multicenter cohort of 114 NPC patients with FDG-PET staging, treated with (chemo)radiation at 8 international institutions. Uni- and multivariable Cox regression and Kaplan-Meier analysis with respect to overall survival (OS), event-free survival (EFS), distant metastases-free survival (FFDM), and locoregional control (LRC) was performed for clinical and PET parameters. Results When analyzing metric PET parameters, ASP showed a significant association with EFS (p = 0.035) and a trend for OS (p = 0.058). MTV was significantly associated with EFS (p = 0.026), OS (p = 0.008) and LRC (p = 0.012) and TLG with LRC (p = 0.019). TLG and MTV showed a very high correlation (Spearman’s rho = 0.95), therefore TLG was subesequently not further analysed. Optimal cutoff values for defining high and low risk groups were determined by maximization of the p-value in univariate Cox regression considering all possible cutoff values. Generation of stable cutoff values was feasible for MTV (p<0.001), ASP (p = 0.023) and combination of both (MTV+ASP = occurrence of one or both risk factors, p<0.001) for OS and for MTV regarding the endpoints OS (p<0.001) and LRC (p<0.001). In multivariable Cox (age >55 years + one binarized PET parameter), MTV >11.1ml (hazard ratio (HR): 3.57, p<0.001) and ASP > 14.4% (HR: 3.2, p = 0.031) remained prognostic for OS. MTV additionally remained prognostic for LRC (HR: 4.86 p<0.001) and EFS (HR: 2.51 p = 0.004). Bootstrapping analyses showed that a combination of high MTV and ASP improved prognostic value for OS compared to each single variable significantly (p = 0.005 and p = 0.04, respectively). When using the cohort from China (n = 57 patients) for establishment of prognostic parameters and all other patients for validation (n = 57 patients), MTV could be successfully validated as prognostic parameter regarding OS, EFS and LRC (all p-values <0.05 for both cohorts). Conclusions In this analysis, PET parameters were associated with outcome of NPC patients. MTV showed a robust association with OS, EFS and LRC. Our data suggest that combination of MTV and ASP may potentially further improve the risk stratification of NPC patients.
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Affiliation(s)
- Sebastian Zschaeck
- Charité –Universitätsmedizin Berlin, Berlin, Germany
- Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Radiation Oncology, Berlin Institute of Health, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Yimin Li
- Department of Radiation Oncology, Xiamen Cancer Center, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Qin Lin
- Department of Radiation Oncology, Xiamen Cancer Center, The First Affiliated Hospital of Xiamen University, Xiamen, China
- * E-mail:
| | - Marcus Beck
- Charité –Universitätsmedizin Berlin, Berlin, Germany
- Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Radiation Oncology, Berlin Institute of Health, Berlin, Germany
| | - Holger Amthauer
- Department of Nuclear Medicine, Charité—Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
| | - Laura Bauersachs
- Charité –Universitätsmedizin Berlin, Berlin, Germany
- Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Radiation Oncology, Berlin Institute of Health, Berlin, Germany
| | - Marina Hajiyianni
- Charité –Universitätsmedizin Berlin, Berlin, Germany
- Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Radiation Oncology, Berlin Institute of Health, Berlin, Germany
| | - Julian Rogasch
- Department of Nuclear Medicine, Charité—Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
| | - Vincent H. Ehrhardt
- Charité –Universitätsmedizin Berlin, Berlin, Germany
- Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Radiation Oncology, Berlin Institute of Health, Berlin, Germany
| | - Goda Kalinauskaite
- Charité –Universitätsmedizin Berlin, Berlin, Germany
- Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Radiation Oncology, Berlin Institute of Health, Berlin, Germany
| | - Julian Weingärtner
- Charité –Universitätsmedizin Berlin, Berlin, Germany
- Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Radiation Oncology, Berlin Institute of Health, Berlin, Germany
| | - Vivian Hartmann
- Charité –Universitätsmedizin Berlin, Berlin, Germany
- Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Radiation Oncology, Berlin Institute of Health, Berlin, Germany
| | - Jörg van den Hoff
- Department of Positron Emission Tomography, Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Volker Budach
- Charité –Universitätsmedizin Berlin, Berlin, Germany
- Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Radiation Oncology, Berlin Institute of Health, Berlin, Germany
| | - Carmen Stromberger
- Charité –Universitätsmedizin Berlin, Berlin, Germany
- Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Radiation Oncology, Berlin Institute of Health, Berlin, Germany
| | - Frank Hofheinz
- Department of Positron Emission Tomography, Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
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