1
|
Zhuang M, Li X, Qiu Z, Guan J. Does consensus contour improve robustness and accuracy in 18F-FDG PET radiomic features? EJNMMI Phys 2024; 11:48. [PMID: 38839641 PMCID: PMC11153434 DOI: 10.1186/s40658-024-00652-0] [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: 03/14/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024] Open
Abstract
PURPOSE The purpose of our study is to validate the robustness and accuracy of consensus contour in 2-deoxy-2-[18 F]fluoro-D-glucose (18 F-FDG) PET radiomic features. METHODS 225 nasopharyngeal carcinoma (NPC) and 13 extended cardio-torso (XCAT) simulated data were enrolled. All segmentation were performed with four segmentation methods under two different initial masks, respectively. Consensus contour (ConSeg) was then developed using the majority vote rule. 107 radiomic features were extracted by Pyradiomics based on segmentation and the intraclass correlation coefficient (ICC) was calculated for each feature between masks or among segmentation, respectively. In XCAT ICC between segmentation and simulated ground truth were also calculated to access the accuracy. RESULTS ICC varied with the dataset, segmentation method, initial mask and feature type. ConSeg presented higher ICC for radiomic features in robustness tests and similar ICC in accuracy tests, compared with the average of four segmentation results. Higher ICC were also generally observed in irregular initial masks compared with rectangular masks in both robustness and accuracy tests. Furthermore, 19 features (17.76%) had ICC ≥ 0.75 in both robustness and accuracy tests for any of the segmentation methods or initial masks. The dataset was observed to have a large impact on the correlation relationships between radiomic features, but not the segmentation method or initial mask. CONCLUSIONS The consensus contour combined with irregular initial mask could improve the robustness and accuracy in radiomic analysis to some extent. The correlation relationships between radiomic features and feature clusters largely depended on the dataset, but not segmentation method or initial mask.
Collapse
Affiliation(s)
- Mingzan Zhuang
- Department of Nuclear Medicine, Meizhou People's Hospital, Meizhou, China.
- Guangdong Engineering Technological Research Center of Clinical Molecular Diagnosis and Antibody Drugs, Meizhou People's Hospital, Meizhou, China.
| | - Xianru Li
- Department of Nuclear Medicine, Meizhou People's Hospital, Meizhou, China
| | - Zhifen Qiu
- Department of Nuclear Medicine, Meizhou People's Hospital, Meizhou, China
| | - Jitian Guan
- Department of Radiology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| |
Collapse
|
2
|
Schell M, Foltyn-Dumitru M, Bendszus M, Vollmuth P. Automated hippocampal segmentation algorithms evaluated in stroke patients. Sci Rep 2023; 13:11712. [PMID: 37474622 PMCID: PMC10359355 DOI: 10.1038/s41598-023-38833-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 07/16/2023] [Indexed: 07/22/2023] Open
Abstract
Deep learning segmentation algorithms can produce reproducible results in a matter of seconds. However, their application to more complex datasets is uncertain and may fail in the presence of severe structural abnormalities-such as those commonly seen in stroke patients. In this investigation, six recent, deep learning-based hippocampal segmentation algorithms were tested on 641 stroke patients of a multicentric, open-source dataset ATLAS 2.0. The comparisons of the volumes showed that the methods are not interchangeable with concordance correlation coefficients from 0.266 to 0.816. While the segmentation algorithms demonstrated an overall good performance (volumetric similarity [VS] 0.816 to 0.972, DICE score 0.786 to 0.921, and Hausdorff distance [HD] 2.69 to 6.34), no single out-performing algorithm was identified: FastSurfer performed best in VS, QuickNat in DICE and average HD, and Hippodeep in HD. Segmentation performance was significantly lower for ipsilesional segmentation, with a decrease in performance as a function of lesion size due to the pathology-based domain shift. Only QuickNat showed a more robust performance in volumetric similarity. Even though there are many pre-trained segmentation methods, it is important to be aware of the possible decrease in performance for the segmentation results on the lesion side due to the pathology-based domain shift. The segmentation algorithm should be selected based on the research question and the evaluation parameter needed. More research is needed to improve current hippocampal segmentation methods.
Collapse
Affiliation(s)
- Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
| |
Collapse
|
3
|
Zhuang M, Qiu Z, Lou Y. Does consensus contours improve robustness and accuracy on [Formula: see text]F-FDG PET imaging tumor delineation? EJNMMI Phys 2023; 10:18. [PMID: 36913000 PMCID: PMC10011254 DOI: 10.1186/s40658-023-00538-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 03/01/2023] [Indexed: 03/14/2023] Open
Abstract
PURPOSE The aim of this study is to explore the robustness and accuracy of consensus contours with 225 nasopharyngeal carcinoma (NPC) clinical cases and 13 extended cardio-torso simulated lung tumors (XCAT) based on 2-deoxy-2-[[Formula: see text]F]fluoro-D-glucose ([Formula: see text]F-FDG) PET imaging. METHODS Primary tumor segmentation was performed with two different initial masks on 225 NPC [Formula: see text]F-FDG PET datasets and 13 XCAT simulations using methods of automatic segmentation with active contour, affinity propagation (AP), contrast-oriented thresholding (ST), and 41% maximum tumor value (41MAX), respectively. Consensus contours (ConSeg) were subsequently generated based on the majority vote rule. The metabolically active tumor volume (MATV), relative volume error (RE), Dice similarity coefficient (DSC) and their respective test-retest (TRT) metrics between different masks were adopted to analyze the results quantitatively. The nonparametric Friedman and post hoc Wilcoxon tests with Bonferroni adjustment for multiple comparisons were performed with [Formula: see text] 0.05 considered to be significant. RESULTS AP presented the highest variability for MATV in different masks, and ConSeg presented much better TRT performances in MATV compared with AP, and slightly poorer TRT in MATV compared with ST or 41MAXin most cases. Similar trends were also found in RE and DSC with the simulated data. The average of four segmentation results (AveSeg) showed better or comparable results in accuracy for most cases with respect to ConSeg. AP, AveSeg and ConSeg presented better RE and DSC in irregular masks as compared with rectangle masks. Additionally, all methods underestimated the tumour boundaries in relation to the ground truth for XCAT including respiratory motion. CONCLUSIONS The consensus method could be a robust approach to alleviate segmentation variabilities, but did not seem to improve the accuracy of segmentation results on average. Irregular initial masks might be at least in some cases attributable to mitigate the segmentation variability as well.
Collapse
Affiliation(s)
- Mingzan Zhuang
- Department of Nuclear Medicine, Meizhou People’s Hospital, Meizhou, China
| | - Zhifen Qiu
- Department of Nuclear Medicine, Meizhou People’s Hospital, Meizhou, China
| | - Yunlong Lou
- Department of Nuclear Medicine, Meizhou People’s Hospital, Meizhou, China
| |
Collapse
|
4
|
Kriwanek F, Ulbrich L, Lechner W, Lütgendorf-Caucig C, Konrad S, Waldstein C, Herrmann H, Georg D, Widder J, Traub-Weidinger T, Rausch I. Impact of SSTR PET on Inter-Observer Variability of Target Delineation of Meningioma and the Possibility of Using Threshold-Based Segmentations in Radiation Oncology. Cancers (Basel) 2022; 14:cancers14184435. [PMID: 36139596 PMCID: PMC9497299 DOI: 10.3390/cancers14184435] [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: 05/10/2022] [Revised: 08/31/2022] [Accepted: 09/08/2022] [Indexed: 11/24/2022] Open
Abstract
Aim: The aim of this study was to assess the effects of including somatostatin receptor agonist (SSTR) PET imaging in meningioma radiotherapy planning by means of changes in inter-observer variability (IOV). Further, the possibility of using threshold-based delineation approaches for semiautomatic tumor volume definition was assessed. Patients and Methods: Sixteen patients with meningioma undergoing fractionated radiotherapy were delineated by five radiation oncologists. IOV was calculated by comparing each delineation to a consensus delineation, based on the simultaneous truth and performance level estimation (STAPLE) algorithm. The consensus delineation was used to adapt a threshold-based delineation, based on a maximization of the mean Dice coefficient. To test the threshold-based approach, seven patients with SSTR-positive meningioma were additionally evaluated as a validation group. Results: The average Dice coefficients for delineations based on MRI alone was 0.84 ± 0.12. For delineation based on MRI + PET, a significantly higher dice coefficient of 0.87 ± 0.08 was found (p < 0.001). The Hausdorff distance decreased from 10.96 ± 11.98 mm to 8.83 ± 12.21 mm (p < 0.001) when adding PET for the lesion delineation. The best threshold value for a threshold-based delineation was found to be 14.0% of the SUVmax, with an average Dice coefficient of 0.50 ± 0.19 compared to the consensus delineation. In the validation cohort, a Dice coefficient of 0.56 ± 0.29 and a Hausdorff coefficient of 27.15 ± 21.54 mm were found for the threshold-based approach. Conclusions: SSTR-PET added to standard imaging with CT and MRI reduces the IOV in radiotherapy planning for patients with meningioma. When using a threshold-based approach for PET-based delineation of meningioma, a relatively low threshold of 14.0% of the SUVmax was found to provide the best agreement with a consensus delineation.
Collapse
Affiliation(s)
- Florian Kriwanek
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Leo Ulbrich
- Department of Radiation Oncology, Medical University of Vienna, 1090 Vienna, Austria
| | - Wolfgang Lechner
- Department of Radiation Oncology, Medical University of Vienna, 1090 Vienna, Austria
| | | | - Stefan Konrad
- Department of Radiation Oncology, Medical University of Vienna, 1090 Vienna, Austria
| | - Cora Waldstein
- Department of Radiation Oncology, Medical University of Vienna, 1090 Vienna, Austria
| | - Harald Herrmann
- Department of Radiation Oncology, Medical University of Vienna, 1090 Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna, 1090 Vienna, Austria
| | - Joachim Widder
- Department of Radiation Oncology, Medical University of Vienna, 1090 Vienna, Austria
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Correspondence:
| | - Ivo Rausch
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria
| |
Collapse
|
5
|
Rodríguez De Dios N, Navarro-Martin A, Cigarral C, Chicas-Sett R, García R, Garcia V, Gonzalez JA, Gonzalo S, Murcia-Mejía M, Robaina R, Sotoca A, Vallejo C, Valtueña G, Couñago F. GOECP/SEOR radiotheraphy guidelines for non-small-cell lung cancer. World J Clin Oncol 2022; 13:237-266. [PMID: 35582651 PMCID: PMC9052073 DOI: 10.5306/wjco.v13.i4.237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 08/27/2021] [Accepted: 04/09/2022] [Indexed: 02/06/2023] Open
Abstract
Non-small cell lung cancer (NSCLC) is a heterogeneous disease accounting for approximately 85% of all lung cancers. Only 17% of patients are diagnosed at an early stage. Treatment is multidisciplinary and radiotherapy plays a key role in all stages of the disease. More than 50% of patients with NSCLC are treated with radiotherapy (curative-intent or palliative). Technological advances-including highly conformal radiotherapy techniques, new immobilization and respiratory control systems, and precision image verification systems-allow clinicians to individualize treatment to maximize tumor control while minimizing treatment-related toxicity. Novel therapeutic regimens such as moderate hypofractionation and advanced techniques such as stereotactic body radiotherapy (SBRT) have reduced the number of radiotherapy sessions. The integration of SBRT into routine clinical practice has radically altered treatment of early-stage disease. SBRT also plays an increasingly important role in oligometastatic disease. The aim of the present guidelines is to review the role of radiotherapy in the treatment of localized, locally-advanced, and metastatic NSCLC. We review the main radiotherapy techniques and clarify the role of radiotherapy in routine clinical practice. These guidelines are based on the best available evidence. The level and grade of evidence supporting each recommendation is provided.
Collapse
Affiliation(s)
- Núria Rodríguez De Dios
- Department of Radiation Oncology, Hospital del Mar, Barcelona 08003, Spain
- Radiation Oncology Research Group, Hospital Del Mar Medical Research Institution, Barcelona 08003, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona 08003, Spain
| | - Arturo Navarro-Martin
- Department of Radiation Oncology, Thoracic Malignancies Unit, Hospital Duran i Reynals. ICO, L´Hospitalet de L, Lobregat 08908, Spain
| | - Cristina Cigarral
- Department of Radiation Oncology, Hospital Clínico de Salamanca, Salamanca 37007, Spain
| | - Rodolfo Chicas-Sett
- Department of Radiation Oncology, ASCIRES Grupo Biomédico, Valencia 46004, Spain
| | - Rafael García
- Department of Radiation Oncology, Hospital Ruber Internacional, Madrid 28034, Spain
| | - Virginia Garcia
- Department of Radiation Oncology, Hospital Universitario Arnau de Vilanova, Lleida 25198, Spain
| | | | - Susana Gonzalo
- Department of Radiation Oncology, Hospital Universitario La Princesa, Madrid 28006, Spain
| | - Mauricio Murcia-Mejía
- Department of Radiation Oncology, Hospital Universitario Sant Joan de Reus, Reus 43204, Tarragona, Spain
| | - Rogelio Robaina
- Department of Radiation Oncology, Hospital Universitario Arnau de Vilanova, Lleida 25198, Spain
| | - Amalia Sotoca
- Department of Radiation Oncology, Hospital Ruber Internacional, Madrid 28034, Spain
| | - Carmen Vallejo
- Department of Radiation Oncology, Hospital Universitario Ramón y Cajal, Madrid 28034, Spain
| | - German Valtueña
- Department of Radiation Oncology, Hospital Clínico Universitario Lozano Blesa, Zaragoza 50009, Spain
| | - Felipe Couñago
- Department of Radiation Oncology, Hospital Universitario Quirónsalud, Madrid 28223, Spain
- Department of Radiation Oncology, Hospital La Luz, Madrid 28003, Spain
- Department of Clinical, Universidad Europea, Madrid 28670, Spain
| |
Collapse
|
6
|
Vaz SC, Adam JA, Bolton RCD, Vera P, van Elmpt W, Herrmann K, Hicks RJ, Lievens Y, Santos A, Schöder H, Dubray B, Visvikis D, Troost EGC, de Geus-Oei LF. Joint EANM/SNMMI/ESTRO practice recommendations for the use of 2-[ 18F]FDG PET/CT external beam radiation treatment planning in lung cancer V1.0. Eur J Nucl Med Mol Imaging 2022; 49:1386-1406. [PMID: 35022844 PMCID: PMC8921015 DOI: 10.1007/s00259-021-05624-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 11/15/2021] [Indexed: 12/16/2022]
Abstract
Purpose 2-[18F]FDG
PET/CT is of utmost importance for radiation treatment (RT) planning and response monitoring in lung cancer patients, in both non-small and small cell lung cancer (NSCLC and SCLC). This topic has been addressed in guidelines composed by experts within the field of radiation oncology. However, up to present, there is no procedural guideline on this subject, with involvement of the nuclear medicine societies. Methods A literature review was performed, followed by a discussion between a multidisciplinary team of experts in the different fields involved in the RT planning of lung cancer, in order to guide clinical management. The project was led by experts of the two nuclear medicine societies (EANM and SNMMI) and radiation oncology (ESTRO). Results and conclusion This guideline results from a joint and dynamic collaboration between the relevant disciplines for this topic. It provides a worldwide, state of the art, and multidisciplinary guide to 2-[18F]FDG PET/CT RT planning in NSCLC and SCLC. These practical recommendations describe applicable updates for existing clinical practices, highlight potential flaws, and provide solutions to overcome these as well. Finally, the recent developments considered for future application are also reviewed.
Collapse
Affiliation(s)
- Sofia C Vaz
- Nuclear Medicine Radiopharmacology, Champalimaud Centre for the Unkown, Champalimaud Foundation, Lisbon, Portugal.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Judit A Adam
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Roberto C Delgado Bolton
- Department of Diagnostic Imaging (Radiology) and Nuclear Medicine, University Hospital San Pedro and Centre for Biomedical Research of La Rioja (CIBIR), Logroño (La Rioja), Spain
| | - Pierre Vera
- Henri Becquerel Cancer Center, QuantIF-LITIS EA 4108, Université de Rouen, Rouen, France
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
| | - Rodney J Hicks
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Yolande Lievens
- Radiation Oncology Department, Ghent University Hospital and Ghent University, Ghent, Belgium
| | - Andrea Santos
- Nuclear Medicine Department, CUF Descobertas Hospital, Lisbon, Portugal
| | - Heiko Schöder
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Bernard Dubray
- Department of Radiotherapy and Medical Physics, Centre Henri Becquerel, Rouen, France.,QuantIF-LITIS EA4108, University of Rouen, Rouen, France
| | | | - Esther G C Troost
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, 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.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, 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.,German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| |
Collapse
|
7
|
Optimizing Workflows for Fast and Reliable Metabolic Tumor Volume Measurements in Diffuse Large B Cell Lymphoma. Mol Imaging Biol 2021; 22:1102-1110. [PMID: 31993925 PMCID: PMC7343740 DOI: 10.1007/s11307-020-01474-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
PURPOSE This pilot study aimed to determine interobserver reliability and ease of use of three workflows for measuring metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in diffuse large B cell lymphoma (DLBCL). PROCEDURES Twelve baseline [18F]FDG PET/CT scans from DLBCL patients with wide variation in number and size of involved organs and lymph nodes were selected from the international PETRA consortium database. Three observers analyzed scans using three workflows. Workflow A: user-defined selection of individual lesions followed by four automated segmentations (41%SUVmax, A50%SUVpeak, SUV≥2.5, SUV≥4.0). For each lesion, observers indicated their "preferred segmentation." Individually selected lesions were summed to yield total MTV and TLG. Workflow B: fully automated preselection of [18F]FDG-avid structures (SUV≥4.0 and volume≥3ml), followed by removing non-tumor regions with single mouse clicks. Workflow C: preselected volumes based on Workflow B modified by manually adding lesions or removing physiological uptake, subsequently checked by experienced nuclear medicine physicians. Workflow C was performed 3 months later to avoid recall bias from the initial Workflow B analysis. Interobserver reliability was expressed as intraclass correlation coefficients (ICC). RESULTS Highest interobserver reliability in Workflow A was found for SUV≥2.5 and SUV≥4.0 methods (ICCs for MTV 0.96 and 0.94, respectively). SUV≥4.0 and A50%Peak were most and SUV≥2.5 was the least preferred segmentation method. Workflow B had an excellent interobserver reliability (ICC = 1.00) for MTV and TLG. Workflow C reduced the ICC for MTV and TLG to 0.92 and 0.97, respectively. Mean workflow analysis time per scan was 29, 7, and 22 min for A, B, and C, respectively. CONCLUSIONS Improved interobserver reliability and ease of use occurred using fully automated preselection (using SUV≥4.0 and volume≥3ml, Workflow B) compared with individual lesion selection by observers (Workflow A). Subsequent manual modification was necessary for some patients but reduced interobserver reliability which may need to be balanced against potential improvement on prognostic accuracy.
Collapse
|
8
|
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
|
9
|
Abstract
The assessment of tumor parameters derived from F-FDG PET/CT in oncology provides valuable information in non-small cell lung cancer. A proper segmentation should delineate tumor with high accuracy, being the most important step to measure metabolic parameters. However, there is still no consensus about the optimal methodology. Additionally, some clinical conditions inherently tied to tumor and imaging can limit the proper tumor delineation. We present some practical cases that represent different aspects to consider during segmentation of primary non-small cell lung cancer by using F-FDG-PET/CT and some possible solutions to tackle with the most common limitations in clinical practice.
Collapse
|
10
|
Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomics: a combined analysis of two independent prospective European trials. Eur J Nucl Med Mol Imaging 2020; 48:1005-1015. [PMID: 33006656 DOI: 10.1007/s00259-020-05049-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 09/20/2020] [Indexed: 01/15/2023]
Abstract
PURPOSE Fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) is included in the International Myeloma Working Group (IMWG) imaging guidelines for the work-up at diagnosis and the follow-up of multiple myeloma (MM) notably because it is a reliable tool as a predictor of prognosis. Nevertheless, none of the published studies focusing on the prognostic value of PET-derived features at baseline consider tumor heterogeneity, which could be of high importance in MM. The aim of this study was to evaluate the prognostic value of baseline PET-derived features in transplant-eligible newly diagnosed (TEND) MM patients enrolled in two prospective independent European randomized phase III trials using an innovative statistical random survival forest (RSF) approach. METHODS Imaging ancillary studies of IFM/DFCI2009 and EMN02/HO95 trials formed part of the present analysis (IMAJEM and EMN02/HO95, respectively). Among all patients initially enrolled in these studies, those with a positive baseline FDG-PET/CT imaging and focal bone lesions (FLs) and/or extramedullary disease (EMD) were included in the present analysis. A total of 17 image features (visual and quantitative, reflecting whole imaging characteristics) and 5 clinical/histopathological parameters were collected. The statistical analysis was conducted using two RSF approaches (train/validation + test and additional nested cross-validation) to predict progression-free survival (PFS). RESULTS One hundred thirty-nine patients were considered for this study. The final model based on the first RSF (train/validation + test) approach selected 3 features (treatment arm, hemoglobin, and SUVmaxBone Marrow (BM)) among the 22 involved initially, and two risk groups of patients (good and poor prognosis) could be defined with a mean hazard ratio of 4.3 ± 1.5 and a mean log-rank p value of 0.01 ± 0.01. The additional RSF (nested cross-validation) analysis highlighted the robustness of the proposed model across different splits of the dataset. Indeed, the first features selected using the train/validation + test approach remained the first ones over the folds with the nested approach. CONCLUSION We proposed a new prognosis model for TEND MM patients at diagnosis based on two RSF approaches. TRIAL REGISTRATION IMAJEM: NCT01309334 and EMN02/HO95: NCT01134484.
Collapse
|
11
|
Gardin I. Methods to delineate tumour for radiotherapy by fluorodeoxyglucose positron emission tomography. Cancer Radiother 2020; 24:418-422. [DOI: 10.1016/j.canrad.2020.04.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 04/24/2020] [Indexed: 12/26/2022]
|
12
|
Barrington SF, Zwezerijnen BGJC, de Vet HCW, Heymans MW, Mikhaeel NG, Burggraaff CN, Eertink JJ, Pike LC, Hoekstra OS, Zijlstra JM, Boellaard R. Automated Segmentation of Baseline Metabolic Total Tumor Burden in Diffuse Large B-Cell Lymphoma: Which Method Is Most Successful? A Study on Behalf of the PETRA Consortium. J Nucl Med 2020; 62:332-337. [PMID: 32680929 DOI: 10.2967/jnumed.119.238923] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 06/17/2020] [Indexed: 12/22/2022] Open
Abstract
Metabolic tumor volume (MTV) is a promising biomarker of pretreatment risk in diffuse large B-cell lymphoma (DLBCL). Different segmentation methods can be used that predict prognosis equally well but give different optimal cutoffs for risk stratification. Segmentation can be cumbersome; a fast, easy, and robust method is needed. Our aims were to evaluate the best automated MTV workflow in DLBCL; determine whether uptake time, compliance or noncompliance with standardized recommendations for 18F-FDG scanning, and subsequent disease progression influence the success of segmentation; and assess differences in MTVs and discriminatory power of segmentation methods. Methods: One hundred forty baseline 18F-FDG PET/CT scans were selected from U.K. and Dutch studies on DLBCL to provide a balance between scans at 60 and 90 min of uptake, parameters compliant and noncompliant with standardized recommendations for scanning, and patients with and without progression. An automated tool was applied for segmentation using an SUV of 2.5 (SUV2.5), an SUV of 4.0 (SUV4.0), adaptive thresholding (A50P), 41% of SUVmax (41%), a majority vote including voxels detected by at least 2 methods (MV2), and a majority vote including voxels detected by at least 3 methods (MV3). Two independent observers rated the success of the tool to delineate MTV. Scans that required minimal interaction were rated as a success; scans that missed more than 50% of the tumor or required more than 2 editing steps were rated as a failure. Results: One hundred thirty-eight scans were evaluable, with significant differences in success and failure ratings among methods. The best performing was SUV4.0, with higher success and lower failure rates than any other method except MV2, which also performed well. SUV4.0 gave a good approximation of MTV in 105 (76%) scans, with simple editing for a satisfactory result in additionally 20% of cases. MTV was significantly different for all methods between patients with and without progression. The 41% segmentation method performed slightly worse, with longer uptake times; otherwise, scanning conditions and patient outcome did not influence the tool's performance. The discriminative power was similar among methods, but MTVs were significantly greater using SUV4.0 and MV2 than using other thresholds, except for SUV2.5. Conclusion: SUV4.0 and MV2 are recommended for further evaluation. Automated estimation of MTV is feasible.
Collapse
Affiliation(s)
- Sally F Barrington
- King's College London and Guy's and St. Thomas' PET Center, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Ben G J C Zwezerijnen
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Henrica C W de Vet
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Martijn W Heymans
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - N George Mikhaeel
- Department of Clinical Oncology, Guy's and St. Thomas' NHS Foundation Trust and School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom; and
| | - Coreline N Burggraaff
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jakoba J Eertink
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Lucy C Pike
- King's College London and Guy's and St. Thomas' PET Center, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Otto S Hoekstra
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Josée M Zijlstra
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| |
Collapse
|
13
|
Pfaehler E, Burggraaff C, Kramer G, Zijlstra J, Hoekstra OS, Jalving M, Noordzij W, Brouwers AH, Stevenson MG, de Jong J, Boellaard R. PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability. PLoS One 2020; 15:e0230901. [PMID: 32226030 PMCID: PMC7105134 DOI: 10.1371/journal.pone.0230901] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 03/11/2020] [Indexed: 12/26/2022] Open
Abstract
Background PET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of user-interaction. Methods Twenty PET images of bulky tumors were delineated independently by six observers using four strategies: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (Select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences. Results The results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other segmentation strategies (p-value<0.01). Interactive threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation. Conclusions FDG PET segmentations of bulky tumors using strategies with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the Select-the-best workflow did outperform the other strategies tested and may be a good candidate for fast and reliable labeling of bulky and heterogeneous tumors.
Collapse
Affiliation(s)
- Elisabeth Pfaehler
- Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- * E-mail:
| | - Coreline Burggraaff
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Gem Kramer
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Josée Zijlstra
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Otto S. Hoekstra
- Department of Oncology Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Mathilde Jalving
- Department of Oncology Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Walter Noordzij
- Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Adrienne H. Brouwers
- Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Marc G. Stevenson
- Department of Surgical Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Johan de Jong
- Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ronald Boellaard
- Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
14
|
Ahmadi SA, Bötzel K, Levin J, Maiostre J, Klein T, Wein W, Rozanski V, Dietrich O, Ertl-Wagner B, Navab N, Plate A. Analyzing the co-localization of substantia nigra hyper-echogenicities and iron accumulation in Parkinson's disease: A multi-modal atlas study with transcranial ultrasound and MRI. NEUROIMAGE-CLINICAL 2020; 26:102185. [PMID: 32050136 PMCID: PMC7013333 DOI: 10.1016/j.nicl.2020.102185] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 01/12/2020] [Accepted: 01/14/2020] [Indexed: 12/23/2022]
Abstract
Volumetric 3D analysis of hyper-echogenicities from transcranial ultrasound (TCS). First multi-modal analysis of TCS and QSM-MRI in Parkinson's disease. Computations of TCS-MRI registration and a novel multi-modal anatomical template. TCS hyper-echogenicities are co-localized with QSM iron accumulations. Co-localizations occur in the SNc and VTA, but nowhere else in the midbrain.
Background Transcranial B-mode sonography (TCS) can detect hyperechogenic speckles in the area of the substantia nigra (SN) in Parkinson's disease (PD). These speckles correlate with iron accumulation in the SN tissue, but an exact volumetric localization in and around the SN is still unknown. Areas of increased iron content in brain tissue can be detected in vivo with magnetic resonance imaging, using quantitative susceptibility mapping (QSM). Methods In this work, we i) acquire, co-register and transform TCS and QSM imaging from a cohort of 23 PD patients and 27 healthy control subjects into a normalized atlas template space and ii) analyze and compare the 3D spatial distributions of iron accumulation in the midbrain, as detected by a signal increase (TCS+ and QSM+) in both modalities. Results We achieved sufficiently accurate intra-modal target registration errors (TRE<1 mm) for all MRI volumes and multi-modal TCS-MRI co-localization (TRE<4 mm) for 66.7% of TCS scans. In the caudal part of the midbrain, enlarged TCS+ and QSM+ areas were located within the SN pars compacta in PD patients in comparison to healthy controls. More cranially, overlapping TCS+ and QSM+ areas in PD subjects were found in the area of the ventral tegmental area (VTA). Conclusion Our findings are concordant with several QSM-based studies on iron-related alterations in the area SN pars compacta. They substantiate that TCS+ is an indicator of iron accumulation in Parkinson's disease within and in the vicinity of the SN. Furthermore, they are in favor of an involvement of the VTA and thereby the mesolimbic system in Parkinson's disease.
Collapse
Affiliation(s)
- Seyed-Ahmad Ahmadi
- Department of Neurology, Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany; German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany; Chair for Computer Aided Medical Procedures (CAMP), Technical University of Munich, Boltzmannstr. 3, Garching 85748, Germany
| | - Kai Bötzel
- Department of Neurology, Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany
| | - Juliana Maiostre
- Department of Neurology, Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany
| | | | - Wolfgang Wein
- ImFusion GmbH, Agnes-Pockels-Bogen 1, München 80992, Germany
| | | | - Olaf Dietrich
- Department of Radiology, Ludwig-Maximilians University, Marchioninistr. 15, Munich 81377, Germany
| | - Birgit Ertl-Wagner
- Department of Radiology, Ludwig-Maximilians University, Marchioninistr. 15, Munich 81377, Germany; The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1 × 8, Canada
| | - Nassir Navab
- Chair for Computer Aided Medical Procedures (CAMP), Technical University of Munich, Boltzmannstr. 3, Garching 85748, Germany
| | - Annika Plate
- Department of Neurology, Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany.
| |
Collapse
|
15
|
Das SK, McGurk R, Miften M, Mutic S, Bowsher J, Bayouth J, Erdi Y, Mawlawi O, Boellaard R, Bowen SR, Xing L, Bradley J, Schoder H, Yin FF, Sullivan DC, Kinahan P. Task Group 174 Report: Utilization of [ 18 F]Fluorodeoxyglucose Positron Emission Tomography ([ 18 F]FDG-PET) in Radiation Therapy. Med Phys 2019; 46:e706-e725. [PMID: 31230358 DOI: 10.1002/mp.13676] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 04/30/2019] [Accepted: 06/06/2019] [Indexed: 02/03/2023] Open
Abstract
The use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is 18 F-fluorodeoxyglucose ([18 F]FDG), a glucose analog with demonstrated efficacy in cancer diagnosis and staging. However, diagnosis and RT planning are different endeavors with unique requirements, and very little literature is available for guiding physicists and clinicians in the utilization of [18 F]FDG-PET in RT. The two goals of this report are to educate and provide recommendations. The report provides background and education on current PET imaging systems, PET tracers, intensity quantification, and current utilization in RT (staging, segmentation, image registration, treatment planning, and therapy response assessment). Recommendations are provided on acceptance testing, annual and monthly quality assurance, scanning protocols to ensure consistency between interpatient scans and intrapatient longitudinal scans, reporting of patient and scan parameters in literature, requirements for incorporation of [18 F]FDG-PET in treatment planning systems, and image registration. The recommendations provided here are minimum requirements and are not meant to cover all aspects of the use of [18 F]FDG-PET for RT.
Collapse
Affiliation(s)
- Shiva K Das
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Ross McGurk
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Sasa Mutic
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - James Bowsher
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - John Bayouth
- Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Yusuf Erdi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Osama Mawlawi
- Department of Imaging Physics, University of Texas, M D Anderson Cancer Center, Houston, TX, USA
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Stephen R Bowen
- Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeffrey Bradley
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Heiko Schoder
- Molecular Imaging and Therapy Service, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Daniel C Sullivan
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Paul Kinahan
- Department of Radiology, University of Washington, Seattle, WA, USA
| |
Collapse
|
16
|
« Définition des volumes cibles : quand et comment l’oncologue radiothérapeute peut-il utiliser la TEP ? ». Cancer Radiother 2019; 23:745-752. [DOI: 10.1016/j.canrad.2019.07.133] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 07/28/2019] [Indexed: 12/12/2022]
|
17
|
Parkinson C, Evans M, Guerrero-Urbano T, Michaelidou A, Pike L, Barrington S, Jayaprakasam V, Rackley T, Palaniappan N, Staffurth J, Marshall C, Spezi E. Machine-learned target volume delineation of 18F-FDG PET images after one cycle of induction chemotherapy. Phys Med 2019; 61:85-93. [PMID: 31151585 DOI: 10.1016/j.ejmp.2019.04.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 04/04/2019] [Accepted: 04/23/2019] [Indexed: 12/18/2022] Open
Abstract
Biological tumour volume (GTVPET) delineation on 18F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptake and volume of the GTVPET. Automatic segmentation algorithms applied to 18F-FDG PET (PET-AS) imaging have been used for GTVPET delineation on 18F-FDG PET imaging acquired before ICT. However, their role has not been investigated in 18F-FDG PET imaging acquired after ICT. In this study we investigate PET-AS techniques, including ATLAAS a machine learned method, for accurate delineation of the GTVPET after ICT. Twenty patients were enrolled onto a prospective phase I study (FiGaRO). PET/CT imaging was acquired at baseline and 3 weeks following 1 cycle of induction chemotherapy. The GTVPET was manually delineated by a nuclear medicine physician and clinical oncologist. The resulting GTVPET was used as the reference contour. The ATLAAS original statistical model was expanded to include images of reduced metabolic activity and the ATLAAS algorithm was re-trained on the new reference dataset. Estimated GTVPET contours were derived using sixteen PET-AS methods and compared to the GTVPET using the Dice Similarity Coefficient (DSC). The mean DSC for ATLAAS, 60% Peak Thresholding (PT60), Adaptive Thresholding (AT) and Watershed Thresholding (WT) was 0.72, 0.61, 0.63 and 0.60 respectively. The GTVPET generated by ATLAAS compared favourably with manually delineated volumes and in comparison, to other PET-AS methods, was more accurate for GTVPET delineation after ICT. ATLAAS would be a feasible method to reduce inter-observer variability in multi-centre trials.
Collapse
Affiliation(s)
- Craig Parkinson
- School of Engineering, Cardiff University, Queen's Buildings, 14-17 The Parade, Cardiff CF24 3AA, UK.
| | - Mererid Evans
- Velindre Cancer Centre, Velindre Rd, Cardiff CF14 2TL, UK
| | | | | | - Lucy Pike
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, UK
| | - Sally Barrington
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, UK
| | | | - Thomas Rackley
- Velindre Cancer Centre, Velindre Rd, Cardiff CF14 2TL, UK
| | | | - John Staffurth
- Velindre Cancer Centre, Velindre Rd, Cardiff CF14 2TL, UK; School of Medicine, UHW Main Building, Heath Park, Cardiff CF14 4XN, UK
| | - Christopher Marshall
- Wales Research & Diagnostic PET Imaging Centre, Cardiff University, School of Medicine, Ground Floor, C Block, UHW Main Building, Heath Park, Cardiff CF14 4XN, UK
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Queen's Buildings, 14-17 The Parade, Cardiff CF24 3AA, UK; Velindre Cancer Centre, Velindre Rd, Cardiff CF14 2TL, UK
| |
Collapse
|
18
|
Barrington SF, Meignan M. Time to Prepare for Risk Adaptation in Lymphoma by Standardizing Measurement of Metabolic Tumor Burden. J Nucl Med 2019; 60:1096-1102. [PMID: 30954945 DOI: 10.2967/jnumed.119.227249] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 03/28/2019] [Indexed: 12/26/2022] Open
Abstract
Increased tumor burden is associated with inferior outcomes in many lymphoma subtypes. Surrogates of tumor burden that are easy to measure, such as the maximum tumor dimension of the bulkiest lesion on CT, have been used as prognostic indices for many years. Recently, total metabolic tumor volume (MTV) and tumor lesion glycolysis have emerged as promising and robust biomarkers of outcome in various lymphomas. The median MTV and the optimal cutoffs to separate patients into risk groups in a study population are, however, highly dependent on the population characteristics and the delineation method used to outline tumor on the PET image. This issue has precluded the use of MTV for risk stratification in trials and clinical practice. Standardization of the methodology is timely to allow the potential for risk adaptation to be explored in addition to response adaptation using PET. Meetings between representatives from research groups active in the field were held under the auspices of the PET International Lymphoma and Myeloma Workshop. A summary of those discussions, which included a review of the literature and a practical assessment of methods used for outlining, including various software options, is presented. Finally, a proposal is made to perform a technical validation of MTV measurement enabling benchmark reference ranges to be derived for published delineation approaches used for outlining with various software. This process would require collation of representative imaging data sets of the most common lymphoma subtypes; agreement on pragmatic criteria for the selection of lesions; generation of a range of MTVs, with consensus to be reached on final contours in a training set; and development of automated software solutions with a set of minimum functionalities to reduce measurement variability. Methods developed in the above training exercise could then be applied to another data set, with a final set of contours and values generated. This final data set would provide a benchmark against which end-users could test their ability to measure MTVs that are consistent with expected values. The data set and automated software solutions could be shared with manufacturers with the aim of including these in standard workflows to allow standardization of MTV measurement across the world.
Collapse
Affiliation(s)
- Sally F Barrington
- Guy's and St. Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom; and
| | - Michel Meignan
- Lymphoma Study Association-Imaging (LYSA-IM), Functional Imaging and Therapeutics Department, Henri Mondor University Hospitals, University Paris Est Créteil, Créteil, France
| |
Collapse
|
19
|
Kolinger GD, Vállez García D, Kramer GM, Frings V, Smit EF, de Langen AJ, Dierckx RAJO, Hoekstra OS, Boellaard R. Repeatability of [ 18F]FDG PET/CT total metabolic active tumour volume and total tumour burden in NSCLC patients. EJNMMI Res 2019; 9:14. [PMID: 30734113 PMCID: PMC6367490 DOI: 10.1186/s13550-019-0481-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 01/25/2019] [Indexed: 12/15/2022] Open
Abstract
Background Total metabolic active tumour volume (TMATV) and total tumour burden (TTB) are increasingly studied as prognostic and predictive factors in non-small cell lung cancer (NSCLC) patients. In this study, we investigated the repeatability of TMATV and TTB as function of uptake interval, positron emission tomography/computed tomography (PET/CT) image reconstruction settings, and lesion delineation method. We used six lesion delineation methods, four direct PET image-derived delineations and two based on a majority vote approach, i.e. intersection between two or more delineations (MV2) and between three or more delineations (MV3). To evaluate the accuracy of those methods, they were compared with a reference delineation obtained from the consensus of the segmentations performed by three experienced observers. Ten NSCLC patients underwent two baseline whole-body [18F]2-Fluoro-2-deoxy-2-D-glucose ([18F]FDG) PET/CT studies on separate days, within 3 days. Two scans were obtained on each day at 60 and 90 min post-injection to assess the influence of tracer uptake interval. PET/CT images were reconstructed following the European Association of Nuclear Medicine Research Ltd. (EARL) compliant settings and with point-spread-function (PSF) modelling. Repeatability between the measurements of each day was determined and the influence of uptake interval, reconstruction settings, and lesion delineation method was assessed using the generalized estimating equations model. Results Based on the Jaccard index with the reference delineation, the MV2 lesion delineation method was the most successful method for automated lesion segmentation. The best overall repeatability (lowest repeatability coefficient, RC) was found for TTB from 90 min of tracer uptake scans reconstructed with EARL compliant settings and delineated with 41% of lesion’s maximum SUV method (RC = 11%). In most cases, TMATV and TTB repeatability were not significantly affected by changes in tracer uptake time or reconstruction settings. However, some lesion delineation methods had significantly different repeatability when applied to the same images. Conclusions This study suggests that under some circumstances TMATV and TTB repeatability are significantly affected by the lesion delineation method used. Performing the delineation with a majority vote approach improves reliability and does not hamper repeatability, regardless of acquisition and reconstruction settings. It is therefore concluded that by using a majority vote based tumour segmentation approach, TMATV and TTB in NSCLC patients can be measured with high reliability and precision. Electronic supplementary material The online version of this article (10.1186/s13550-019-0481-1) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Guilherme D Kolinger
- University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - David Vállez García
- University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Gerbrand M Kramer
- Amsterdam University Medical Centers, location VU Medical Center, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Virginie Frings
- Amsterdam University Medical Centers, location VU Medical Center, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Egbert F Smit
- Amsterdam University Medical Centers, location VU Medical Center, Department of Pulmonary Disease, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,Netherlands Cancer Institute, Department of Thoracic Oncology, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Adrianus J de Langen
- Amsterdam University Medical Centers, location VU Medical Center, Department of Pulmonary Disease, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,Netherlands Cancer Institute, Department of Thoracic Oncology, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Rudi A J O Dierckx
- University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Otto S Hoekstra
- Amsterdam University Medical Centers, location VU Medical Center, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Ronald Boellaard
- University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands. .,Amsterdam University Medical Centers, location VU Medical Center, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
| |
Collapse
|
20
|
Delineation of lung cancer with FDG PET/CT during radiation therapy. Radiat Oncol 2018; 13:219. [PMID: 30419929 PMCID: PMC6233287 DOI: 10.1186/s13014-018-1163-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 10/28/2018] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVES To propose an easily applicable segmentation method (perPET-RT) for delineation of tumour volume during radiotherapy on interim fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in patients with non-small cell lung cancer (NSCLC). MATERIAL AND METHODS Sixty-seven patients (51 primary tumours, 60 lymph nodes), from 4 prospective studies, underwent an FDG PET/CT scan during the fifth week of radiation therapy, using different generations of PET/CT. Per-therapeutic PET/CT scans were delineated in consensus by two experienced physicians leading to the gold standard threshold to be applied. The mathematical expression of Thopt, the optimal threshold to be applied as a function of the maximum standard uptake value (SUVmax), was determined. The performance of this method (perPET-RT) was assessed by computing the DICE similarity coefficient (DSC) and was compared with 8 fixed threshold values and 3 adaptive thresholding methods. RESULTS Thopt verified the following expression: Thopt = A.ln(1/SUVmax) + B where A and B were 2 constants. A and B were independent from the generation of PET/CT, but depended on the type of lesions (primary lung tumours vs. lymph nodes). PerPET-RT showed good to very good agreement in comparison to the gold standard. The mean and standard deviation of DSC value was 0.81 ± 0.13 for lung lesions and 0.78 ± 0.15 for lymph nodes. PerPET-RT showed a significant better agreement than the other segmentation methods (p < 0.001), except for one of the adaptive thresholding method ADT (p = 0.11). CONCLUSION On the database used, perPET-RT has proven its reliability and accuracy for tumour delineation on per-therapeutic FDG PET/CT using only SUVmax measurement. This method may be used to delineate tumour volume for dose-escalation planning. TRIAL REGISTRATION NCT01261598 , NCT01261585 , NCT01576796 .
Collapse
|
21
|
Bibault JE, Denis F, Roué A, Gibon D, Fumagalli I, Hennequin C, Barillot I, Quéro L, Paumier A, Mahé MA, Servagi Vernat S, Créhange G, Lapeyre M, Blanchard P, Pointreau Y, Lafond C, Huguet F, Mornex F, Latorzeff I, de Crevoisier R, Martin V, Kreps S, Durdux C, Antoni D, Noël G, Giraud P. [Siriade 2.0: An e-learning platform for radiation oncology contouring]. Cancer Radiother 2018; 22:773-777. [PMID: 30360973 DOI: 10.1016/j.canrad.2018.02.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 01/23/2018] [Accepted: 02/08/2018] [Indexed: 12/26/2022]
Abstract
PURPOSE In 2008, the French national society of radiation oncology (SFRO) and the association for radiation oncology continued education (AFCOR) created Siriade, an e-learning website dedicated to contouring. MATERIAL AND METHODS Between 2015 and 2017, this platform was updated using the latest digital online tools available. Two main sections were needed: a theoretical part and another section of online workshops. RESULTS Teaching courses are available as online commented videos, available on demand. The practical section of the website is an online contouring workshop that automatically generates a report quantifying the quality of the user's delineation compared with the experts'. CONCLUSION Siriade 2.0 is an innovating digital tool for radiation oncology initial and continuous education.
Collapse
Affiliation(s)
- J-E Bibault
- Service d'oncologie radiothérapie, hôpital européen Georges-Pompidou, 20, rue Leblanc, 75015 Paris, France; Université Paris Descartes, Paris Sorbonne Cité, 20, rue Leblanc, 75015 Paris, France
| | - F Denis
- Service de radiothérapie, centre Jean-Bernard, 9, rue Beauverger, 72000 Le Mans, France
| | - A Roué
- Institut national des sciences et techniques nucléaires, centre CEA de Saclay, D36, 91191 Gif-sur-Yvette, France
| | - D Gibon
- Aquilab, parc Eurasanté, biocentre Fleming, 250, rue Salvador-Allende, 59120 Loos, France
| | - I Fumagalli
- Service d'oncologie radiothérapie, hôpital Saint-Louis, 1, avenue Claude-Vellefau, 75010 Paris, France
| | - C Hennequin
- Service d'oncologie radiothérapie, hôpital Saint-Louis, 1, avenue Claude-Vellefau, 75010 Paris, France
| | - I Barillot
- Service d'oncologie radiothérapie, centre universitaire de cancérologie Henry-S.-Kaplan, 2, boulevard Tonnellé, 37044 Tours, France; Université François-Rabelais, 2, boulevard Tonnellé, 37044 Tours, France
| | - L Quéro
- Service d'oncologie radiothérapie, hôpital Saint-Louis, 1, avenue Claude-Vellefau, 75010 Paris, France
| | - A Paumier
- Service d'oncologie radiothérapie, institut de cancérologie de l'Ouest René-Gauducheau, boulevard Professeur-Jacques-Monod, 44805 Saint-Herblain, France
| | - M-A Mahé
- Service d'oncologie radiothérapie, institut de cancérologie de l'Ouest René-Gauducheau, boulevard Professeur-Jacques-Monod, 44805 Saint-Herblain, France
| | - S Servagi Vernat
- Service d'oncologie radiothérapie, institut Jean-Godinot, 1, rue Koenig, 51100 Reims, France
| | - G Créhange
- Service d'oncologie radiothérapie, centre Georges-François-Leclerc, 1, rue du Professeur-Marion, 21000 Dijon, France
| | - M Lapeyre
- Service d'oncologie radiothérapie, centre Jean-Perrin, 58, rue Montalembert, 63011 Clermont-Ferrand, France
| | - P Blanchard
- Service d'oncologie radiothérapie Gustave-Roussy, 114, rue Édouard-Vaillant, 94805 Villejuif, France
| | - Y Pointreau
- Service de radiothérapie, centre Jean-Bernard, 9, rue Beauverger, 72000 Le Mans, France
| | - C Lafond
- Service de radiothérapie, centre Jean-Bernard, 9, rue Beauverger, 72000 Le Mans, France
| | - F Huguet
- Service d'oncologie radiothérapie, hôpital Tenon, Hôpitaux universitaires de l'Est parisien, 4, rue de la Chine, 75020 Paris, France; Université Pierre-et-Marie-Curie, 4, rue de la Chine, 75020 Paris, France
| | - F Mornex
- Service d'oncologie radiothérapie, CHU Lyon Sud, 65, chemin du Grand-Revoyet, 69495 Pierre-Bénite, France
| | - I Latorzeff
- Service d'oncologie radiothérapie, clinique Pasteur, 1, rue de la Petite-Vitesse, 31300 Toulouse, France
| | - R de Crevoisier
- Service d'oncologie radiothérapie, centre Eugène-Marquis, avenue de la Bataille-Flandre-Dunkerque, 35700 Rennes, France
| | - V Martin
- Service d'oncologie radiothérapie, hôpital Saint-Louis, 1, avenue Claude-Vellefau, 75010 Paris, France
| | - S Kreps
- Service d'oncologie radiothérapie, hôpital européen Georges-Pompidou, 20, rue Leblanc, 75015 Paris, France; Université Paris Descartes, Paris Sorbonne Cité, 20, rue Leblanc, 75015 Paris, France
| | - C Durdux
- Service d'oncologie radiothérapie, hôpital européen Georges-Pompidou, 20, rue Leblanc, 75015 Paris, France; Université Paris Descartes, Paris Sorbonne Cité, 20, rue Leblanc, 75015 Paris, France
| | - D Antoni
- Département universitaire de radiothérapie, centre Paul-Strauss, 3, rue de la Porte-de-l'Hôpital, 67065 Strasbourg, France
| | - G Noël
- Département universitaire de radiothérapie, centre Paul-Strauss, 3, rue de la Porte-de-l'Hôpital, 67065 Strasbourg, France
| | - P Giraud
- Service d'oncologie radiothérapie, hôpital européen Georges-Pompidou, 20, rue Leblanc, 75015 Paris, France; Université Paris Descartes, Paris Sorbonne Cité, 20, rue Leblanc, 75015 Paris, France.
| |
Collapse
|
22
|
Konert T, van de Kamer JB, Sonke JJ, Vogel WV. The developing role of FDG PET imaging for prognostication and radiotherapy target volume delineation in non-small cell lung cancer. J Thorac Dis 2018; 10:S2508-S2521. [PMID: 30206495 DOI: 10.21037/jtd.2018.07.101] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Advancements in functional imaging technology have allowed new possibilities in contouring of target volumes, monitoring therapy, and predicting treatment outcome in non-small cell lung cancer (NSCLC). Consequently, the role of 18F-fluorodeoxyglucose positron emission tomography (FDG PET) has expanded in the last decades from a stand-alone diagnostic tool to a versatile instrument integrated with computed tomography (CT), with a prominent role in lung cancer radiotherapy. This review outlines the most recent literature on developments in FDG PET imaging for prognostication and radiotherapy target volume delineation (TVD) in NSCLC. We also describe the challenges facing the clinical implementation of these developments and present new ideas for future research.
Collapse
Affiliation(s)
- Tom Konert
- Nuclear Medicine Department, Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jeroen B van de Kamer
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Wouter V Vogel
- Nuclear Medicine Department, Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| |
Collapse
|
23
|
Besson FL, Henry T, Meyer C, Chevance V, Roblot V, Blanchet E, Arnould V, Grimon G, Chekroun M, Mabille L, Parent F, Seferian A, Bulifon S, Montani D, Humbert M, Chaumet-Riffaud P, Lebon V, Durand E. Rapid Contour-based Segmentation for 18F-FDG PET Imaging of Lung Tumors by Using ITK-SNAP: Comparison to Expert-based Segmentation. Radiology 2018; 288:277-284. [DOI: 10.1148/radiol.2018171756] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
|
24
|
Parkinson C, Foley K, Whybra P, Hills R, Roberts A, Marshall C, Staffurth J, Spezi E. Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods. EJNMMI Res 2018; 8:29. [PMID: 29644499 PMCID: PMC5895559 DOI: 10.1186/s13550-018-0379-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Accepted: 03/23/2018] [Indexed: 12/25/2022] Open
Abstract
Background Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated. Results Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group. Conclusion Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used. Electronic supplementary material The online version of this article (10.1186/s13550-018-0379-3) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Craig Parkinson
- School of Engineering, Cardiff University, Queen's Buildings, 14-17 The Parade, Cardiff, CF24 3AA, UK
| | - Kieran Foley
- Division of Cancer and Genetics, School of Medicine, UHW Main Building, Heath Park, Cardiff, CF14 4XN, UK.
| | - Philip Whybra
- School of Engineering, Cardiff University, Queen's Buildings, 14-17 The Parade, Cardiff, CF24 3AA, UK
| | - Robert Hills
- Clinical Trials Unit, Cardiff University, Cardiff, CF10 3AT, UK
| | - Ashley Roberts
- Clinical Radiology, University Hospital of Wales, Heath Park, Cardiff, CF14 4XW, UK
| | - Chris Marshall
- Wales Research and Diagnostic PET Imaging Centre, Cardiff University, School of Medicine, Ground Floor, C Block, UHW Main Building, Heath Park, Cardiff, CF14 4XN, UK
| | - John Staffurth
- Division of Cancer and Genetics, School of Medicine, UHW Main Building, Heath Park, Cardiff, CF14 4XN, UK.,Velindre Cancer Centre, Velindre Rd, Cardiff, CF14 2TL, UK
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Queen's Buildings, 14-17 The Parade, Cardiff, CF24 3AA, UK.,Velindre Cancer Centre, Velindre Rd, Cardiff, CF14 2TL, UK
| |
Collapse
|
25
|
Nestle U, De Ruysscher D, Ricardi U, Geets X, Belderbos J, Pöttgen C, Dziadiuszko R, Peeters S, Lievens Y, Hurkmans C, Slotman B, Ramella S, Faivre-Finn C, McDonald F, Manapov F, Putora PM, LePéchoux C, Van Houtte P. ESTRO ACROP guidelines for target volume definition in the treatment of locally advanced non-small cell lung cancer. Radiother Oncol 2018; 127:1-5. [PMID: 29605476 DOI: 10.1016/j.radonc.2018.02.023] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 02/22/2018] [Accepted: 02/22/2018] [Indexed: 12/18/2022]
Abstract
Radiotherapy (RT) plays a major role in the curative treatment of locally advanced non-small cell lung cancer (NSCLC). Therefore, the ACROP committee was asked by the ESTRO to provide recommendations on target volume delineation for standard clinical scenarios in definitive (chemo)radiotherapy (RT) and adjuvant RT for locally advanced NSCLC. The guidelines given here are a result of the evaluation of a structured questionnaire followed by a consensus discussion, voting and writing procedure within the committee. Hence, we provide advice for methods and time-points of diagnostics and imaging before the start of treatment planning and for the mandatory and optional imaging to be used for planning itself. Concerning target volumes, recommendations are given for GTV delineation of primary tumour and lymph nodes followed by issues related to the delineation of CTVs for definitive and adjuvant radiotherapy. In the context of PTV delineation, recommendations about the management of geometric uncertainties and target motion are given. We further provide our opinions on normal tissue delineation and organisational and responsibility questions in the process of target volume delineation. This guideline intends to contribute to the standardisation and optimisation of the process of RT treatment planning for clinical practice and prospective studies.
Collapse
Affiliation(s)
- Ursula Nestle
- Department of Radiation Oncology, Kliniken Maria Hilf, Moenchengladbach, Germany; Department of Radiation Oncology, University Hospital Freiburg, Germany.
| | - Dirk De Ruysscher
- Maastricht University Medical Center, Department of Radiation Oncology (Maastro clinic), GROW School for Oncology and Developmental Biology, The Netherlands; KU Leuven, Radiation Oncology, Belgium
| | | | - Xavier Geets
- Department of Radiation Oncology, Cliniques universitaires Saint-Luc, MIRO - IREC Lab, UCL, Belgium
| | - Jose Belderbos
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Christoph Pöttgen
- Department of Radiation Oncology, West German Tumor Centre, University of Duisburg-Essen Medical School, Germany
| | - Rafal Dziadiuszko
- Department of Oncology and Radiotherapy, Medical University of Gdańsk, Poland
| | - Stephanie Peeters
- Maastricht University Medical Center, Department of Radiation Oncology (Maastro clinic), GROW School for Oncology and Developmental Biology, The Netherlands
| | - Yolande Lievens
- Department of Radiation Oncology, Ghent University Hospital, Belgium
| | - Coen Hurkmans
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, The Netherlands
| | - Ben Slotman
- Department of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands
| | - Sara Ramella
- Department of Radiation Oncology, Campus Bio-Medico University, Rome, Italy
| | - Corinne Faivre-Finn
- University of Manchester & The Christie NHS Foundation Trust, Manchester, UK
| | - Fiona McDonald
- Department of Radiotherapy, The Royal Marsden NHS Foundation Trust, London, UK
| | - Farkhad Manapov
- Department of Radiation Oncology, University Hospital, LMU Munich, Germany
| | - Paul Martin Putora
- Department of Radiation Oncology, Kantonsspital St. Gallen, Switzerland; Medical Faculty, University of Bern, Switzerland
| | - Cécile LePéchoux
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Paul Van Houtte
- Department Radiation Oncology, Institut Bordet, Université Libre Bruxelles, Belgium
| |
Collapse
|
26
|
Wang XY, Zhao YF, Liu Y, Yang YK, Zhu Z, Wu N. Comparison of different automated lesion delineation methods for metabolic tumor volume of 18F-FDG PET/CT in patients with stage I lung adenocarcinoma. Medicine (Baltimore) 2017; 96:e9365. [PMID: 29390527 PMCID: PMC5758229 DOI: 10.1097/md.0000000000009365] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
The aim of the study was to investigate the suitable segmentation method in small, low uptake and heterogeneous nodules of stage I lung adenocarcinoma.133 stage I lung adenocarcinoma patients with F-FDG PET/CT scans were enrolled in this retrospective study. All lesions were divided into different groups according to nodule density, nodule size, and the maximum standard uptake value (SUVmax) level. Four different PET segmentation methods were performed, including percentage threshold of SUVmax (T42% and T42% × RC), gradient-based threshold (adaptive iterative algorithm, AT-AIA), and background-related threshold (adaptive thresholding at 40% SUVmax, AT40%) approaches. The MTVs were evaluated and compared with CT volume (CTV). Percentage volume error (%VE) compared to CTV was calculated and the correlations between MTVs and CTV were analyzed.AT-AIA had the highest accuracy in large, high uptake, and solid nodules (72.5%, 72.4%, and 65.6%, respectively). AT40% had the highest accuracy in small, low uptake and nonsolid nodules (56.6%, 56.1%, and 62.6%, respectively). In part-solid nodules, the accuracy of AT-AIA (60.0%) and AT40% (56.7%) were higher than that of T42% and T42% × RC. The MTV of AT-AIA was in excellent correlation with the CTV in solid nodules (R = 0.831, P < .001) and in high uptake nodules (R = 0.830, P < .001). The MTV of AT40% was in good correlation with the CTV in nonsolid nodules (R = 0.686, P = .003) and in part-solid nodules (R = 0.731, P < .001).AT40% showed best performance in small, low uptake, nonsolid and part-solid lesions. AT-AIA was suitable for large, high uptake, and solid lesions.
Collapse
Affiliation(s)
| | | | | | - Yi-kun Yang
- Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | | | - Ning Wu
- PET/CT Center
- Department of Diagnostic Radiology
| |
Collapse
|
27
|
Hatt M, Lee JA, Schmidtlein CR, Naqa IE, Caldwell C, De Bernardi E, Lu W, Das S, Geets X, Gregoire V, Jeraj R, MacManus MP, Mawlawi OR, Nestle U, Pugachev AB, Schöder H, Shepherd T, Spezi E, Visvikis D, Zaidi H, Kirov AS. Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211. Med Phys 2017; 44:e1-e42. [PMID: 28120467 DOI: 10.1002/mp.12124] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 12/09/2016] [Accepted: 01/04/2017] [Indexed: 12/14/2022] Open
Abstract
PURPOSE The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. APPROACH A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed. FINDINGS A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. CONCLUSIONS Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.
Collapse
Affiliation(s)
- Mathieu Hatt
- INSERM, UMR 1101, LaTIM, University of Brest, IBSAM, Brest, France
| | - John A Lee
- Université catholique de Louvain (IREC/MIRO) & FNRS, Brussels, 1200, Belgium
| | | | | | - Curtis Caldwell
- Sunnybrook Health Sciences Center, Toronto, ON, M4N 3M5, Canada
| | | | - Wei Lu
- Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Shiva Das
- University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Xavier Geets
- Université catholique de Louvain (IREC/MIRO) & FNRS, Brussels, 1200, Belgium
| | - Vincent Gregoire
- Université catholique de Louvain (IREC/MIRO) & FNRS, Brussels, 1200, Belgium
| | - Robert Jeraj
- University of Wisconsin, Madison, WI, 53705, USA
| | | | | | - Ursula Nestle
- Universitätsklinikum Freiburg, Freiburg, 79106, Germany
| | - Andrei B Pugachev
- University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Heiko Schöder
- Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | | | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, Wales, United Kingdom
| | | | - Habib Zaidi
- Geneva University Hospital, Geneva, CH-1211, Switzerland
| | - Assen S Kirov
- Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| |
Collapse
|
28
|
Thureau S, Hapdey S, Vera P. [Role of functional imaging in the definition of target volumes for lung cancer radiotherapy]. Cancer Radiother 2016; 20:699-704. [PMID: 27614514 DOI: 10.1016/j.canrad.2016.08.121] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 08/01/2016] [Indexed: 12/23/2022]
Abstract
Functional imaging with positron emission tomography (PET) is interesting to optimize lung radiotherapy planning, and probably to deliver a heterogeneous dose or adapt the radiation dose during treatment. Only fluorodeoxyglucose (FDG) PET-computed tomography (CT) is validated for staging lung cancer and planning radiotherapy. The optimal segmentation methods remain to be defined as well as the interest of "dose painting" from pre-treatment PET (metabolism: FDG) or hypoxia (fluoromisonidazole: FMISO) and the interest of replanning based on pertherapeutic PET.
Collapse
Affiliation(s)
- S Thureau
- Département de médecine nucléaire, centre de lutte contre le cancer Henri-Becquerel, rue d'Amiens, 76000 Rouen, France; Département de radiothérapie et de physique médicale, centre de lutte contre le cancer Henri-Becquerel, rue d'Amiens, 76000 Rouen, France; Laboratoire QuantIF, EA4108-Litis, FR CNRS 3638, 1, rue d'Amiens, 76000 Rouen, France.
| | - S Hapdey
- Département de médecine nucléaire, centre de lutte contre le cancer Henri-Becquerel, rue d'Amiens, 76000 Rouen, France; Laboratoire QuantIF, EA4108-Litis, FR CNRS 3638, 1, rue d'Amiens, 76000 Rouen, France
| | - P Vera
- Département de médecine nucléaire, centre de lutte contre le cancer Henri-Becquerel, rue d'Amiens, 76000 Rouen, France; Laboratoire QuantIF, EA4108-Litis, FR CNRS 3638, 1, rue d'Amiens, 76000 Rouen, France
| |
Collapse
|
29
|
Zhuang M, Dierckx RAJO, Zaidi H. Generic and robust method for automatic segmentation of PET images using an active contour model. Med Phys 2016; 43:4483. [PMID: 27487865 DOI: 10.1118/1.4954844] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. METHODS MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer's thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. RESULTS For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (-8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work with good accuracy, resulting in a mean RE of -13.35% ± 11.87% and -11.15% ± 23.66%, a mean DSC of 0.89 ± 0.05 and 0.71 ± 0.09, and a mean CE of 19.19% ± 7.89% and 53.92% ± 12.65%, for PSs and CSs, respectively. CONCLUSIONS The authors' results demonstrate that the developed novel PET segmentation algorithm is applicable to various types of lesions in the authors' study and is capable of producing accurate and consistent target volume delineations, potentially resulting in reduced intraobserver and interobserver variabilities observed when using manual delineation and improved accuracy in treatment planning and outcome evaluation.
Collapse
Affiliation(s)
- Mingzan Zhuang
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands; Department of Radiation Oncology, Tumor Hospital of Shantou University Medical College, Shantou, Guangdong 515000, China; and The Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou, Guangdong 515000, China
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland; Geneva Neuroscience Center, Geneva University, CH-1205 Geneva, Switzerland; and Department of Nuclear Medicine and Molecular Imaging,University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
| |
Collapse
|
30
|
Berthon B, Marshall C, Evans M, Spezi E. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography. Phys Med Biol 2016; 61:4855-69. [PMID: 27273293 DOI: 10.1088/0031-9155/61/13/4855] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.
Collapse
Affiliation(s)
- Beatrice Berthon
- Wales Research & Diagnostic PET Imaging Centre, Cardiff University, CF14 4XN, Cardiff, UK
| | | | | | | |
Collapse
|