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Chen Y, Kan K, Liu S, Lin H, Lue K. Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner. J Appl Clin Med Phys 2023; 24:e14200. [PMID: 37937706 PMCID: PMC10691638 DOI: 10.1002/acm2.14200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/13/2023] [Accepted: 10/30/2023] [Indexed: 11/09/2023] Open
Abstract
PURPOSE 18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner. MATERIALS AND METHODS A total of 101 patients who underwent oncological 18 F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18 F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability. RESULTS In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18 F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features. CONCLUSION Respiratory motion has a significant impact on 18 F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.
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Affiliation(s)
- Yu‐Hung Chen
- Department of Nuclear MedicineHualien Tzu Chi HospitalBuddhist Tzu Chi Medical FoundationHualienTaiwan
- School of MedicineCollege of MedicineTzu Chi UniversityHualienTaiwan
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
| | - Kuo‐Yi Kan
- Department of Nuclear MedicineFu Jen Catholic University HospitalNew Taipei CityTaiwan
| | - Shu‐Hsin Liu
- Department of Nuclear MedicineHualien Tzu Chi HospitalBuddhist Tzu Chi Medical FoundationHualienTaiwan
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
| | - Hsin‐Hon Lin
- Department of Medical Imaging and Radiological SciencesCollege of MedicineChang Gung UniversityTaoyuanTaiwan
- Department of Nuclear MedicineChang Gung Memorial HospitalLinkouTaiwan
| | - Kun‐Han Lue
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
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Pellegrino S, Fonti R, Hakkak Moghadam Torbati A, Bologna R, Morra R, Damiano V, Matano E, De Placido S, Del Vecchio S. Heterogeneity of Glycolytic Phenotype Determined by 18F-FDG PET/CT Using Coefficient of Variation in Patients with Advanced Non-Small Cell Lung Cancer. Diagnostics (Basel) 2023; 13:2448. [PMID: 37510192 PMCID: PMC10378511 DOI: 10.3390/diagnostics13142448] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
We investigated the role of Coefficient of Variation (CoV), a first-order texture parameter derived from 18F-FDG PET/CT, in the prognosis of Non-Small Cell Lung Cancer (NSCLC) patients. Eighty-four patients with advanced NSCLC who underwent 18F-FDG PET/CT before therapy were retrospectively studied. SUVmax, SUVmean, CoV, total Metabolic Tumor Volume (MTVTOT) and whole-body Total Lesion Glycolysis (TLGWB) were determined by an automated contouring program (SUV threshold at 2.5). We analyzed 194 lesions: primary tumors (n = 84), regional (n = 48) and non-regional (n = 17) lymph nodes and metastases in liver (n = 9), bone (n = 23) and other sites (n = 13); average CoVs were 0.36 ± 0.13, 0.36 ± 0.14, 0.42 ± 0.18, 0.30 ± 0.14, 0.37 ± 0.17, 0.34 ± 0.13, respectively. No significant differences were found between the CoV values among the different lesion categories. Survival analysis included age, gender, histology, stage, MTVTOT, TLGWB and imaging parameters derived from primary tumors. At univariate analysis, CoV (p = 0.0184), MTVTOT (p = 0.0050), TLGWB (p = 0.0108) and stage (p = 0.0041) predicted Overall Survival (OS). At multivariate analysis, age, CoV, MTVTOT and stage were retained in the model (p = 0.0001). Patients with CoV > 0.38 had significantly better OS than those with CoV ≤ 0.38 (p = 0.0143). Patients with MTVTOT ≤ 89.5 mL had higher OS than those with MTVTOT > 89.5 mL (p = 0.0063). Combining CoV and MTVTOT, patients with CoV ≤ 0.38 and MTVTOT > 89.5 mL had the worst prognosis. CoV, by reflecting the heterogeneity of glycolytic phenotype, can predict clinical outcomes in NSCLC patients.
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Affiliation(s)
- Sara Pellegrino
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Rosa Fonti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | | | - Roberto Bologna
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Rocco Morra
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Vincenzo Damiano
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Elide Matano
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Sabino De Placido
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Silvana Del Vecchio
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
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Duff LM, Scarsbrook AF, Ravikumar N, Frood R, van Praagh GD, Mackie SL, Bailey MA, Tarkin JM, Mason JC, van der Geest KSM, Slart RHJA, Morgan AW, Tsoumpas C. An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images. Biomolecules 2023; 13:343. [PMID: 36830712 PMCID: PMC9953018 DOI: 10.3390/biom13020343] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A-RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C-Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.
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Affiliation(s)
- Lisa M. Duff
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Institute of Medical and Biological Engineering, University of Leeds, Leeds LS2 9JT, UK
| | - Andrew F. Scarsbrook
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Radiology, St. James University Hospital, Leeds LS9 7TF, UK
| | - Nishant Ravikumar
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Leeds, Leeds LS2 9JT, UK
| | - Russell Frood
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Radiology, St. James University Hospital, Leeds LS9 7TF, UK
| | - Gijs D. van Praagh
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Sarah L. Mackie
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds MedTech and In Vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds LS7 4SA, UK
| | - Marc A. Bailey
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- The Leeds Vascular Institute, Leeds General Infirmary, Leeds LS2 9NS, UK
| | - Jason M. Tarkin
- Division of Cardiovascular Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Justin C. Mason
- National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK
| | - Kornelis S. M. van der Geest
- Department of Rheumatology and Clinical Immunology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Riemer H. J. A. Slart
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
- Department of Biomedical Photonic Imaging, Faculty of Science and Technology, University of Twente, 7522 NB Enschede, The Netherlands
| | - Ann W. Morgan
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds MedTech and In Vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds LS7 4SA, UK
| | - Charalampos Tsoumpas
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
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Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT. Cancers (Basel) 2023; 15:cancers15030932. [PMID: 36765889 PMCID: PMC9913076 DOI: 10.3390/cancers15030932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on 18F-FDG PET/CT images. METHODS 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. RESULTS There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. CONCLUSION Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
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Ferrández MC, Eertink JJ, Golla SSV, Wiegers SE, Zwezerijnen GJC, Pieplenbosch S, Zijlstra JM, Boellaard R. Combatting the effect of image reconstruction settings on lymphoma [ 18F]FDG PET metabolic tumor volume assessment using various segmentation methods. EJNMMI Res 2022; 12:44. [PMID: 35904645 PMCID: PMC9338209 DOI: 10.1186/s13550-022-00916-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/18/2022] [Indexed: 11/15/2022] Open
Abstract
Background [18F]FDG PET-based metabolic tumor volume (MTV) is a promising prognostic marker for lymphoma patients. The aim of this study is to assess the sensitivity of several MTV segmentation methods to variations in image reconstruction methods and the ability of ComBat to improve MTV reproducibility. Methods Fifty-six lesions were segmented from baseline [18F]FDG PET scans of 19 lymphoma patients. For each scan, EARL1 and EARL2 standards and locally clinically preferred reconstruction protocols were applied. Lesions were delineated using 9 semiautomatic segmentation methods: fixed threshold based on standardized uptake value (SUV), (SUV = 4, SUV = 2.5), relative threshold (41% of SUVmax [41M], 50% of SUVpeak [A50P]), majority vote-based methods that select voxels detected by at least 2 (MV2) and 3 (MV3) out of the latter 4 methods, Nestle thresholding, and methods that identify the optimal method based on SUVmax (L2A, L2B). MTVs from EARL2 and locally clinically preferred reconstructions were compared to those from EARL1. Finally, different versions of ComBat were explored to harmonize the data.
Results MTVs from the SUV4.0 method were least sensitive to the use of different reconstructions (MTV ratio: median = 1.01, interquartile range = [0.96–1.10]). After ComBat harmonization, an improved agreement of MTVs among different reconstructions was found for most segmentation methods. The regular implementation of ComBat (‘Regular ComBat’) using non-transformed distributions resulted in less accurate and precise MTV alignments than a version using log-transformed datasets (‘Log-transformed ComBat’). Conclusion MTV depends on both segmentation method and reconstruction methods. ComBat reduces reconstruction dependent MTV variability, especially when log-transformation is used to account for the non-normal distribution of MTVs. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-022-00916-9.
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Affiliation(s)
- Maria C Ferrández
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Sanne E Wiegers
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
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6
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Bone and Soft Tissue Tumors. Radiol Clin North Am 2022; 60:339-358. [DOI: 10.1016/j.rcl.2021.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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7
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Anan N, Zainon R, Tamal M. A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management. Insights Imaging 2022; 13:22. [PMID: 35124733 PMCID: PMC8817778 DOI: 10.1186/s13244-021-01153-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/23/2021] [Indexed: 02/06/2023] Open
Abstract
Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. 18F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of 18F-FDG PET/CT in the field of oncology is well established. Clinical application of 18F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of 18F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of 18F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of 18F-FDG PET/CT in lung disease management.
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Dondi F, Pasinetti N, Gatta R, Albano D, Giubbini R, Bertagna F. Comparison between Two Different Scanners for the Evaluation of the Role of 18F-FDG PET/CT Semiquantitative Parameters and Radiomics Features in the Prediction of Final Diagnosis of Thyroid Incidentalomas. J Clin Med 2022; 11:jcm11030615. [PMID: 35160067 PMCID: PMC8836668 DOI: 10.3390/jcm11030615] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/19/2022] [Accepted: 01/25/2022] [Indexed: 12/24/2022] Open
Abstract
The aim of this study was to compare two different tomographs for the evaluation of the role of semiquantitative PET/CT parameters and radiomics features (RF) in the prediction of thyroid incidentalomas (TIs) at 18F-FDG imaging. A total of 221 patients with the presence of TIs were retrospectively included. After volumetric segmentation of each TI, semiquantitative parameters and RF were extracted. All of the features were tested for significant differences between the two PET scanners. The performances of all of the features in predicting the nature of TIs were analyzed by testing three classes of final logistic regression predictive models, one for each tomograph and one with both scanners together. Some RF resulted significantly different between the two scanners. PET/CT semiquantitative parameters were not able to predict the final diagnosis of TIs while GLCM-related RF (in particular GLCM entropy_log2 e GLCM entropy_log10) together with some GLRLM-related and GLZLM-related features presented the best predictive performances. In particular, GLCM entropy_log2, GLCM entropy_log10, GLZLM SZHGE, GLRLM HGRE and GLRLM HGZE resulted the RF with best performances. Our study enabled the selection of some RF able to predict the final nature of TIs discovered at 18F-FDG PET/CT imaging. Classic semiquantitative and volumetric PET/CT parameters did not reveal these abilities. Furthermore, a good overlap in the extraction of RF between the two scanners was underlined.
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Affiliation(s)
- Francesco Dondi
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (F.D.); (R.G.); (F.B.)
| | - Nadia Pasinetti
- Radiation Oncology Department, ASST Valcamonica Esine and Università degli Studi di Brescia, 25040 Brescia, Italy;
| | - Roberto Gatta
- Dipartimento di Scienze Cliniche e Sperimentali dell’Università degli Studi di Brescia, 25123 Brescia, Italy;
| | - Domenico Albano
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (F.D.); (R.G.); (F.B.)
- Correspondence:
| | - Raffaele Giubbini
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (F.D.); (R.G.); (F.B.)
| | - Francesco Bertagna
- Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy; (F.D.); (R.G.); (F.B.)
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Ibrahim A, Widaatalla Y, Refaee T, Primakov S, Miclea RL, Öcal O, Fabritius MP, Ingrisch M, Ricke J, Hustinx R, Mottaghy FM, Woodruff HC, Seidensticker M, Lambin P. Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data. Cancers (Basel) 2021; 13:cancers13184638. [PMID: 34572870 PMCID: PMC8468150 DOI: 10.3390/cancers13184638] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/11/2021] [Accepted: 09/13/2021] [Indexed: 12/17/2022] Open
Abstract
Simple Summary Radiomics has been reported to have potential for correlating with clinical outcomes. However, handcrafted radiomic features (HRFs)—the quantitative features extracted from medical images—are limited by their sensitivity to variations in scanning parameters. Furthermore, radiomics analyses require big data with good quality to achieve desirable performances. In this study, we investigated the reproducibility of HRFs between scans acquired with the same scanning parameters except for the imaging phase (arterial and portal venous phases) to assess the possibilities of merging scans from different phases or replacing missing scans from a phase with other phases to increase data entries. Additionally, we assessed the potential of ComBat harmonization to remove batch effects attributed to this variation. Our results show that the majority of HRFs were not reproducible between the arterial and portal venous phases before or after ComBat harmonization. We provide a guide for analyzing scans of different imaging phases. Abstract Handcrafted radiomic features (HRFs) are quantitative imaging features extracted from regions of interest on medical images which can be correlated with clinical outcomes and biologic characteristics. While HRFs have been used to train predictive and prognostic models, their reproducibility has been reported to be affected by variations in scan acquisition and reconstruction parameters, even within the same imaging vendor. In this work, we evaluated the reproducibility of HRFs across the arterial and portal venous phases of contrast-enhanced computed tomography images depicting hepatocellular carcinomas, as well as the potential of ComBat harmonization to correct for this difference. ComBat harmonization is a method based on Bayesian estimates that was developed for gene expression arrays, and has been investigated as a potential method for harmonizing HRFs. Our results show that the majority of HRFs are not reproducible between the arterial and portal venous imaging phases, yet a number of HRFs could be used interchangeably between those phases. Furthermore, ComBat harmonization increased the number of reproducible HRFs across both phases by 1%. Our results guide the pooling of arterial and venous phases from different patients in an effort to increase cohort size, as well as joint analysis of the phases.
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Affiliation(s)
- Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands; (R.L.M.); (F.M.M.)
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege and GIGA CRC-In Vivo Imaging, University of Liege, 4000 Liege, Belgium;
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
- Correspondence: (A.I.); (T.R.)
| | - Yousif Widaatalla
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence: (A.I.); (T.R.)
| | - Sergey Primakov
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Razvan L. Miclea
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands; (R.L.M.); (F.M.M.)
| | - Osman Öcal
- Department of Radiology, University Hospital, LMU Munich, 80336 Munich, Germany; (O.Ö.); (M.P.F.); (M.I.); (J.R.); (M.S.)
| | - Matthias P. Fabritius
- Department of Radiology, University Hospital, LMU Munich, 80336 Munich, Germany; (O.Ö.); (M.P.F.); (M.I.); (J.R.); (M.S.)
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, 80336 Munich, Germany; (O.Ö.); (M.P.F.); (M.I.); (J.R.); (M.S.)
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, 80336 Munich, Germany; (O.Ö.); (M.P.F.); (M.I.); (J.R.); (M.S.)
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege and GIGA CRC-In Vivo Imaging, University of Liege, 4000 Liege, Belgium;
| | - Felix M. Mottaghy
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands; (R.L.M.); (F.M.M.)
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands; (R.L.M.); (F.M.M.)
| | - Max Seidensticker
- Department of Radiology, University Hospital, LMU Munich, 80336 Munich, Germany; (O.Ö.); (M.P.F.); (M.I.); (J.R.); (M.S.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands; (R.L.M.); (F.M.M.)
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10
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Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021; 42:426-440. [PMID: 34309893 DOI: 10.1002/med.21846] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 07/05/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022]
Abstract
Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
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Affiliation(s)
- Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Akshayaa Vaidyanathan
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Louis Deprez
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Fadila Zerka
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Denis Danthine
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | | | | | | | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium.,GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Marta Ferreira
- GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium
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11
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Masson I, Da-Ano R, Lucia F, Doré M, Castelli J, Goislard de Monsabert C, Ramée JF, Sellami S, Visvikis D, Hatt M, Schick U. Statistical harmonization can improve the development of a multicenter CT-based radiomic model predictive of nonresponse to induction chemotherapy in laryngeal cancers. Med Phys 2021; 48:4099-4109. [PMID: 34008178 DOI: 10.1002/mp.14948] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 04/18/2021] [Accepted: 05/06/2021] [Indexed: 12/22/2022] Open
Abstract
PURPOSE To develop a radiomic model predicting nonresponse to induction chemotherapy in laryngeal cancers, from multicenter pretherapeutic contrast-enhanced computed tomography (CE-CT) and evaluate the benefit of feature harmonization in such a context. METHODS Patients (n = 104) eligible for laryngeal preservation chemotherapy were included in five centers. Primary tumor was manually delineated on the CE-CT images. The following radiomic features were extracted with an in-house software (MIRAS v1.1, LaTIM UMR 1101): intensity, shape, and textural features derived from Gray-Level Co-occurrence Matrix (GLCM), Neighborhood Gray Tone Difference Matrix (NGTDM), Gray-Level Run Length Matrix (GLRLM), and Gray-Level Size Zone Matrix (GLSZM). Harmonization was performed using ComBat after unsupervised hierarchical clustering, used to determine labels automatically, given the high heterogeneity of imaging characteristics across and within centers. Patients with similar feature distributions were grouped with unsupervised clustering into an optimal number of clusters (2) determined with "silhouette scoring." Statistical harmonization was then carried out with ComBat on these 2 identified clusters. The cohort was split into training/validation (n = 66) and testing (n = 32) sets. Area under the receiver operating characteristics curves (AUC) were used to evaluate the ability of radiomic features (before and after harmonization) to predict nonresponse to chemotherapy, and specificity (Sp) and sensitivity (Se) were used to quantify their performance in the testing set. RESULTS Without harmonization, none of the features identified as predictive in the training set remained significant in the testing set. After ComBat, one textural feature identified in the training set keeps a predictive trend in the testing set-Zone Percentage, derived from the GLSZM, was predictive of nonresponse in the training set (AUC = 0.62, Se = 70%, Sp = 64%, P = 0.04) and obtained a satisfactory performance in the testing set (Se = 80%, Sp = 67%, P = 0.03), although significance was limited by the size of the testing set. These results are consistent with previously published findings in head and neck cancers. CONCLUSIONS Radiomic features from CE-CT could help in the selection of patients for induction chemotherapy in laryngeal cancers, with relatively good sensitivity and specificity in predicting lack of response. Statistical harmonization with ComBat and unsupervised clustering seems to improve the predictive value of features extracted in such a heterogeneous multicenter setting.
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Affiliation(s)
| | | | - François Lucia
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.,Radiation Oncology Department, University Hospital, Brest, France
| | - Mélanie Doré
- Department of Radiation Oncology, Institut de cancérologie de l'Ouest René-Gauducheau, Saint-Herblain, France
| | - Joel Castelli
- Radiotherapy Department Cancer, Institute Eugène Marquis, Rennes, France.,University of Rennes 1, LTSI, Rennes, France
| | | | - Jean-François Ramée
- Department of Medical Oncology, Centre Hospitalier de Vendée, La Roche sur Yon, France
| | - Selima Sellami
- Radiation Oncology Department, University Hospital, Brest, France.,Radiotherapy Department, Centre Hospitalier de Cornouaille, Quimper, France
| | | | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Ulrike Schick
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.,Radiation Oncology Department, University Hospital, Brest, France
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12
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[ 18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation. Eur J Nucl Med Mol Imaging 2021; 48:3432-3443. [PMID: 33772334 PMCID: PMC8440288 DOI: 10.1007/s00259-021-05303-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 03/07/2021] [Indexed: 02/07/2023]
Abstract
Purpose To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[18F] fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC). Methods One hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value and effect of ComBat-based harmonisation were evaluated across scanners. Results After a median follow-up of 23 months, 29% of the patients recurred. No individual radiomic or clinical features were significantly associated with cancer recurrence. The best model was obtained using 10 TLR features combined with clinical information. The area under the curve (AUC), F1-score, precision and recall were respectively 0.78 (0.67–0.88), 0.49 (0.25–0.67), 0.42 (0.25–0.60) and 0.63 (0.20–0.80). ComBat did not improve the predictive performance of the best models. Both the TLR and the native models performance varied across scanners used in the test set. Conclusion [18F]FDG PET radiomic features combined with ML add relevant information to the standard clinical parameters in terms of LACC patient’s outcome but remain subject to variability across PET/CT devices. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05303-5.
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13
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Hadjiiski L, Samala R, Chan HP. Image Processing Analytics: Enhancements and Segmentation. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00057-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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14
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Liberini V, Huellner MW, Grimaldi S, Finessi M, Thuillier P, Muni A, Pellerito RE, Papotti MG, Piovesan A, Arvat E, Deandreis D. The Challenge of Evaluating Response to Peptide Receptor Radionuclide Therapy in Gastroenteropancreatic Neuroendocrine Tumors: The Present and the Future. Diagnostics (Basel) 2020; 10:E1083. [PMID: 33322819 PMCID: PMC7763988 DOI: 10.3390/diagnostics10121083] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/09/2020] [Accepted: 12/09/2020] [Indexed: 02/07/2023] Open
Abstract
The NETTER-1 study has proven peptide receptor radionuclide therapy (PRRT) to be one of the most effective therapeutic options for metastatic neuroendocrine tumors (NETs), improving progression-free survival and overall survival. However, PRRT response assessment is challenging and no consensus on methods and timing has yet been reached among experts in the field. This issue is owed to the suboptimal sensitivity and specificity of clinical biomarkers, limitations of morphological response criteria in slowly growing tumors and necrotic changes after therapy, a lack of standardized parameters and timing of functional imaging and the heterogeneity of PRRT protocols in the literature. The aim of this article is to review the most relevant current approaches for PRRT efficacy prediction and response assessment criteria in order to provide an overview of suitable tools for safe and efficacious PRRT.
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Affiliation(s)
- Virginia Liberini
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, 10126 Turin, Italy; (S.G.); (M.F.); (P.T.); (D.D.)
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland;
| | - Martin W. Huellner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland;
| | - Serena Grimaldi
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, 10126 Turin, Italy; (S.G.); (M.F.); (P.T.); (D.D.)
| | - Monica Finessi
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, 10126 Turin, Italy; (S.G.); (M.F.); (P.T.); (D.D.)
| | - Philippe Thuillier
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, 10126 Turin, Italy; (S.G.); (M.F.); (P.T.); (D.D.)
- Department of Endocrinology, University Hospital of Brest, 29200 Brest, France
| | - Alfredo Muni
- Department of Nuclear Medicine, S.S. Biagio e Antonio e C. Arrigo Hospital, 15121 Alessandria, Italy;
| | | | - Mauro G. Papotti
- Pathology Unit, City of Health and Science University Hospital, 10126 Turin, Italy;
- Department of Oncology, University of Turin at Molinette Hospital, 10126 Turin, Italy
| | - Alessandro Piovesan
- Department of Endocrinology, A. O. U. Città della Salute della Scienza of Turin, 10126 Turin, Italy;
| | - Emanuela Arvat
- Oncological Endocrinology, Department of Medical Sciences, University of Turin, 10126 Turin, Italy;
| | - Désirée Deandreis
- Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, 10126 Turin, Italy; (S.G.); (M.F.); (P.T.); (D.D.)
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15
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Lecoq P, Morel C, Prior JO, Visvikis D, Gundacker S, Auffray E, Križan P, Turtos RM, Thers D, Charbon E, Varela J, de La Taille C, Rivetti A, Breton D, Pratte JF, Nuyts J, Surti S, Vandenberghe S, Marsden P, Parodi K, Benlloch JM, Benoit M. Roadmap toward the 10 ps time-of-flight PET challenge. Phys Med Biol 2020; 65:21RM01. [PMID: 32434156 PMCID: PMC7721485 DOI: 10.1088/1361-6560/ab9500] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Since the seventies, positron emission tomography (PET) has become an invaluable medical molecular imaging modality with an unprecedented sensitivity at the picomolar level, especially for cancer diagnosis and the monitoring of its response to therapy. More recently, its combination with x-ray computed tomography (CT) or magnetic resonance (MR) has added high precision anatomic information in fused PET/CT and PET/MR images, thus compensating for the modest intrinsic spatial resolution of PET. Nevertheless, a number of medical challenges call for further improvements in PET sensitivity. These concern in particular new treatment opportunities in the context personalized (also called precision) medicine, such as the need to dynamically track a small number of cells in cancer immunotherapy or stem cells for tissue repair procedures. A better signal-to-noise ratio (SNR) in the image would allow detecting smaller size tumours together with a better staging of the patients, thus increasing the chances of putting cancer in complete remission. Moreover, there is an increasing demand for reducing the radioactive doses injected to the patients without impairing image quality. There are three ways to improve PET scanner sensitivity: improving detector efficiency, increasing geometrical acceptance of the imaging device and pushing the timing performance of the detectors. Currently, some pre-localization of the electron-positron annihilation along a line-of-response (LOR) given by the detection of a pair of annihilation photons is provided by the detection of the time difference between the two photons, also known as the time-of-flight (TOF) difference of the photons, whose accuracy is given by the coincidence time resolution (CTR). A CTR of about 10 picoseconds FWHM will ultimately allow to obtain a direct 3D volume representation of the activity distribution of a positron emitting radiopharmaceutical, at the millimetre level, thus introducing a quantum leap in PET imaging and quantification and fostering more frequent use of 11C radiopharmaceuticals. The present roadmap article toward the advent of 10 ps TOF-PET addresses the status and current/future challenges along the development of TOF-PET with the objective to reach this mythic 10 ps frontier that will open the door to real-time volume imaging virtually without tomographic inversion. The medical impact and prospects to achieve this technological revolution from the detection and image reconstruction point-of-views, together with a few perspectives beyond the TOF-PET application are discussed.
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Affiliation(s)
- Paul Lecoq
- CERN, department EP, Geneva, Switzerland
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16
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Pavic M, Bogowicz M, Kraft J, Vuong D, Mayinger M, Kroeze SGC, Friess M, Frauenfelder T, Andratschke N, Huellner M, Weder W, Guckenberger M, Tanadini-Lang S, Opitz I. FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients. EJNMMI Res 2020; 10:81. [PMID: 32661672 PMCID: PMC7359199 DOI: 10.1186/s13550-020-00669-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 07/02/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Careful selection of malignant pleural mesothelioma (MPM) patients for curative treatment is of highest importance, as the multimodal treatment regimen is challenging for patients and harbors a high risk of substantial toxicity. Radiomics-a quantitative method for image analysis-has shown its prognostic ability in different tumor entities and could therefore play an important role in optimizing patient selection for radical cancer treatment. So far, radiomics as a prognostic tool in MPM was not investigated. MATERIALS AND METHODS This study is based on 72 MPM patients treated with surgery in a curative intent at our institution between 2009 and 2017. Pre-treatment Fluorine-18 fluorodeoxyglucose (FDG) PET and CT scans were used for radiomics outcome modeling. After extraction of 1404 CT and 1410 FDG PET features from each image, a preselection by principal component analysis was performed to include only robust, non-redundant features for the cox regression to predict the progression-free survival (PFS) and the overall survival (OS). Results were validated on a separate cohort. Additionally, SUVmax and SUVmean, and volume were tested for their prognostic ability for PFS and OS. RESULTS For the PFS a concordance index (c-index) of 0.67 (95% CI 0.52-0.82) and 0.66 (95% CI 0.57-0.78) for the training cohort (n = 36) and internal validation cohort (n = 36), respectively, were obtained for the PET radiomics model. The PFS advantage of the low-risk group translated also into an OS advantage. On CT images, no radiomics model could be trained. SUV max and SUV mean were also not prognostic in terms of PFS and OS. CONCLUSION We were able to build a successful FDG PET radiomics model for the prediction of PFS in MPM. Radiomics could serve as a tool to aid clinical decision support systems for treatment of MPM in future.
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Affiliation(s)
- M Pavic
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
| | - M Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - J Kraft
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - D Vuong
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - M Mayinger
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - S G C Kroeze
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - M Friess
- Department of Thoracic Surgery, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - T Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - N Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - M Huellner
- Department of Nuclear Medicine, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - W Weder
- Department of Thoracic Surgery, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - M Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - S Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - I Opitz
- Department of Thoracic Surgery, University Hospital Zurich and University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
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17
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2-[ 18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease. Methods 2020; 188:84-97. [PMID: 32497604 DOI: 10.1016/j.ymeth.2020.05.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/22/2020] [Accepted: 05/27/2020] [Indexed: 12/15/2022] Open
Abstract
Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[18F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[18F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.
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18
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Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, Keek SA, Sanduleanu S, Primakov SP, Beuque MPL, Marcus D, van der Wiel AMA, Zerka F, Oberije CJG, van Timmeren JE, Woodruff HC, Lambin P. Radiomics: from qualitative to quantitative imaging. Br J Radiol 2020; 93:20190948. [PMID: 32101448 DOI: 10.1259/bjr.20190948] [Citation(s) in RCA: 144] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.
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Affiliation(s)
- William Rogers
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Thoracic Oncology, IRCCS Foundation National Cancer Institute, Milan, Italy
| | - Sithin Thulasi Seetha
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Thoracic Oncology, IRCCS Foundation National Cancer Institute, Milan, Italy
| | - Turkey A G Refaee
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Relinde I Y Lieverse
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Renée W Y Granzier
- Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Surgery, Maastricht University Medical Centre, Grow-School for Oncology and Developmental Biology, Maastricht, The Netherlands
| | - Abdalla Ibrahim
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Nuclear Medicine and Comprehensive diagnostic center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium
| | - Simon A Keek
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Sebastian Sanduleanu
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Sergey P Primakov
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Manon P L Beuque
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Damiënne Marcus
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Alexander M A van der Wiel
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Fadila Zerka
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Cary J G Oberije
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Janita E van Timmeren
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiation Oncology, University Hospital Zürich, Zürich, Switzerland.,University of Zürich, Zürich, Switzerland
| | - Henry C Woodruff
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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Tang L, Wang XJ, Baba H, Giganti F. Gastric cancer and image-derived quantitative parameters: Part 2-a critical review of DCE-MRI and 18F-FDG PET/CT findings. Eur Radiol 2019; 30:247-260. [PMID: 31392480 PMCID: PMC6890619 DOI: 10.1007/s00330-019-06370-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 05/31/2019] [Accepted: 07/12/2019] [Indexed: 12/13/2022]
Abstract
Abstract There is yet no consensus on the application of functional imaging and qualitative image interpretation in the management of gastric cancer. In this second part, we will discuss the role of image-derived quantitative parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in gastric cancer, as both techniques have been shown to be promising and useful tools in the clinical decision making of this disease. We will focus on different aspects including aggressiveness assessment, staging and Lauren type discrimination, prognosis prediction and response evaluation. Although both the number of articles and the patients enrolled in the studies were rather small, there is evidence that quantitative parameters from DCE-MRI such as Ktrans, Ve, Kep and AUC could be promising image-derived surrogate parameters for the management of gastric cancer. Data from 18F-FDG PET/CT studies showed that standardised uptake value (SUV) is significantly associated with the aggressiveness, treatment response and prognosis of this disease. Along with the results from diffusion-weighted MRI and contrast-enhanced multidetector computed tomography presented in Part 1 of this critical review, there are additional image-derived quantitative parameters from DCE-MRI and 18F-FDG PET/CT that hold promise as effective tools in the diagnostic pathway of gastric cancer. Key Points • Quantitative analysis from DCE-MRI and18F-FDG PET/CT allows the extrapolation of multiple image-derived parameters. • Data from DCE-MRI (Ktrans, Ve, Kep and AUC) and 18F-FDG PET/CT (SUV) are non-invasive, quantitative image-derived parameters that hold promise in the evaluation of the aggressiveness, treatment response and prognosis of gastric cancer.
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Affiliation(s)
- Lei Tang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital, Beijing, China
| | - Xue-Juan Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Nuclear Medicine, Peking University Cancer Hospital, Beijing, China
| | - Hideo Baba
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK. .,Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, 3rd Floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
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Zwanenburg A. Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 2019; 46:2638-2655. [PMID: 31240330 DOI: 10.1007/s00259-019-04391-8] [Citation(s) in RCA: 169] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 06/04/2019] [Indexed: 12/16/2022]
Abstract
Radiomics in nuclear medicine is rapidly expanding. Reproducibility of radiomics studies in multicentre settings is an important criterion for clinical translation. We therefore performed a meta-analysis to investigate reproducibility of radiomics biomarkers in PET imaging and to obtain quantitative information regarding their sensitivity to variations in various imaging and radiomics-related factors as well as their inherent sensitivity. Additionally, we identify and describe data analysis pitfalls that affect the reproducibility and generalizability of radiomics studies. After a systematic literature search, 42 studies were included in the qualitative synthesis, and data from 21 were used for the quantitative meta-analysis. Data concerning measurement agreement and reliability were collected for 21 of 38 different factors associated with image acquisition, reconstruction, segmentation and radiomics-specific processing steps. Variations in voxel size, segmentation and several reconstruction parameters strongly affected reproducibility, but the level of evidence remained weak. Based on the meta-analysis, we also assessed inherent sensitivity to variations of 110 PET image biomarkers. SUVmean and SUVmax were found to be reliable, whereas image biomarkers based on the neighbourhood grey tone difference matrix and most biomarkers based on the size zone matrix were found to be highly sensitive to variations, and should be used with care in multicentre settings. Lastly, we identify 11 data analysis pitfalls. These pitfalls concern model validation and information leakage during model development, but also relate to reporting and the software used for data analysis. Avoiding such pitfalls is essential for minimizing bias in the results and to enable reproduction and validation of radiomics studies.
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Affiliation(s)
- Alex Zwanenburg
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Helmholtz-Zentrum Dresden - Rossendorf, Technische Universität Dresden, Dresden, Germany.
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.
- German Cancer Research Center (DKFZ), Heidelberg, Germany.
- German Cancer Consortium (DKTK), Partner Site Dresden, Dresden, Germany.
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Hanssen O, Lovinfosse P, Weekers L, Hustinx R, Jouret F. [ 18F-FDG positron emission tomography in non-oncological renal pathology: Current indications and perspectives]. Nephrol Ther 2019; 15:430-438. [PMID: 30982747 DOI: 10.1016/j.nephro.2018.11.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 11/28/2018] [Indexed: 12/26/2022]
Abstract
Positron emission tomography combined with computed tomography (PET/CT) is a nuclear imaging technique which provides anatomical and functional information. PET/CT is increasingly used in non-oncological nephrology since conventional radiological approaches after injection of contrast agents are relatively contra-indicated in patients with chronic kidney disease (CKD). PET/CT after i.v. injection of 18F-fluoro-deoxy-glucose (FDG) is not toxic and is characterized by a high sensitivity. The level of irradiation (∼5mSv) is acceptable. CKD does not significantly influence tissue uptake of 18F-FDG. The purpose of the present review aims at detailing the non-oncological indications of 18F-FDG PET/CT in general nephrology and after kidney transplantation. Particularly, 18F-FDG PET/CT appears useful in the diagnosis of cyst infection in patients with autosomal dominant polycystic kidney disease, as well as in the characterization of retroperitoneal fibrosis. In kidney transplant recipients, 18F-FDG PET/CT may help in the diagnostic work-up of suspected acute rejection, thereby eventually avoiding unnecessary kidney transplant biopsy. Perspectives in 18F-FDG PET/CT imaging are discussed, including innovative approaches of image analysis.
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Affiliation(s)
- Oriane Hanssen
- Service de néphrologie, centre hospitalier universitaire de Liège, avenue Hippocrate 13, 4000 Liège, Belgique
| | - Pierre Lovinfosse
- Service de médecine nucléaire et imagerie oncologique, centre hospitalier universitaire de Liège, avenue Hippocrate 13, 4000 Liège, Belgique
| | - Laurent Weekers
- Service de médecine nucléaire et imagerie oncologique, centre hospitalier universitaire de Liège, avenue Hippocrate 13, 4000 Liège, Belgique
| | - Roland Hustinx
- Service de médecine nucléaire et imagerie oncologique, centre hospitalier universitaire de Liège, avenue Hippocrate 13, 4000 Liège, Belgique
| | - François Jouret
- Service de néphrologie, centre hospitalier universitaire de Liège, avenue Hippocrate 13, 4000 Liège, Belgique; Groupe interdisciplinaire de géno-protéomique appliquée (GIGA), sciences cardiovasculaires, université de Liège, Liège, Belgique.
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Radiomics: is it time to compose the puzzle? Clin Transl Imaging 2018; 6:411-413. [PMID: 30416989 PMCID: PMC6208777 DOI: 10.1007/s40336-018-0302-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Accepted: 10/03/2018] [Indexed: 12/17/2022]
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Heterogeneity analysis of 18F-FDG PET imaging in oncology: clinical indications and perspectives. Clin Transl Imaging 2018. [DOI: 10.1007/s40336-018-0299-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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