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      Repeatability of 18F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

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          Abstract

          Background

          18F‐fluoro‐2‐deoxy‐D‐Glucose positron emission tomography ( 18F‐ FDG PET) radiomics has the potential to guide the clinical decision making in cancer patients, but validation is required before radiomics can be implemented in the clinical setting. The aim of this study was to explore how feature space reduction and repeatability of 18F‐ FDG PET radiomic features are affected by various sources of variation such as underlying data (e.g., object size and uptake), image reconstruction methods and settings, noise, discretization method, and delineation method.

          Methods

          The NEMA image quality phantom was scanned with various sphere‐to‐background ratios ( SBR), simulating different activity uptakes, including spheres with low uptake, that is, SBR smaller than 1. Furthermore, images of a phantom containing 3D printed inserts reflecting realistic heterogeneity uptake patterns were acquired. Data were reconstructed using various matrix sizes, reconstruction algorithms, and scan durations (noise). For every specific reconstruction and noise level, ten statistically equal replicates were generated. The phantom inserts were delineated using CT and PET‐based segmentation methods. A total of 246 radiomic features was extracted from each image dataset. Images were discretized with a fixed number of 64 bins ( FBN) and a fixed bin width ( FBW) of 0.25 for the high and a FBW of 0.05 for the low uptake data. In terms of feature reduction, we determined the impact of these factors on the composition of feature clusters, which were defined on the basis of Spearman's correlation matrices. To assess feature repeatability, the intraclass correlation coefficient was calculated over the ten replicates.

          Results

          In general, larger spheres with high uptake resulted in better repeatability compared to smaller low uptake spheres. In terms of repeatability, features extracted from heterogeneous phantom inserts were comparable to features extracted from bigger high uptake spheres. For example, for an EARL‐compliant reconstruction, larger and smaller high uptake spheres yielded good repeatability for 32% and 30% of the features, while the heterogeneous inserts resulted in 34% repeatable features. For the low uptake spheres, this was the case for 22% and 20% of the features for bigger and smaller spheres, respectively. Images reconstructed with point‐spread‐function ( PSF) resulted in the highest repeatability when compared with OSEM or time‐of‐flight, for example, 53%, 30%, and 32% of repeatable features, respectively (for unsmoothed data, discretized with FBN, 300 s scan duration). Reducing image noise (increasing scan duration and smoothing) and using CT‐based segmentation for the low uptake spheres yielded improved repeatability. FBW discretization resulted in higher repeatability than FBN discretization, for example, 89% and 35% of the features, respectively (for the EARL‐compliant reconstruction and larger high uptake spheres).

          Conclusion

          Feature space reduction and repeatability of 18F‐ FDG PET radiomic features depended on all studied factors. The high sensitivity of PET radiomic features to image quality suggests that a high level of image acquisition and preprocessing standardization is required to be used as clinical imaging biomarker.

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          Most cited references28

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          Machine Learning methods for Quantitative Radiomic Biomarkers

          Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.
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            FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0

            The aim of this guideline is to provide a minimum standard for the acquisition and interpretation of PET and PET/CT scans with [18F]-fluorodeoxyglucose (FDG). This guideline will therefore address general information about [18F]-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) and is provided to help the physician and physicist to assist to carrying out, interpret, and document quantitative FDG PET/CT examinations, but will concentrate on the optimisation of diagnostic quality and quantitative information.
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              A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

              This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.
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                Author and article information

                Contributors
                e.a.g.pfaehler@umcg.nl
                Journal
                Med Phys
                Med Phys
                10.1002/(ISSN)2473-4209
                MP
                Medical Physics
                John Wiley and Sons Inc. (Hoboken )
                0094-2405
                2473-4209
                28 December 2018
                February 2019
                : 46
                : 2 ( doiID: 10.1002/mp.2019.46.issue-2 )
                : 665-678
                Affiliations
                [ 1 ] Department of Nuclear Medicine and Molecular Imaging Medical Imaging Center University of Groningen University Medical Center Groningen Groningen The Netherlands
                [ 2 ] Department of Biomedical Photonic Imaging University of Twente Enschede The Netherlands
                [ 3 ] MIRA Institute for Biomedical Technology and Technical Medicine University of Twente Enschede The Netherlands
                [ 4 ] Department of Radiology & Nuclear Medicine Amsterdam University Medical Centers Location VUMC Amsterdam The Netherlands
                Author notes
                [*] [* ]Author to whom correspondence should be addressed. Electronic mail: e.a.g.pfaehler@ 123456umcg.nl ; Telephone: (+31) 503613471; Fax: (+31) 5036.
                [†]

                These authors have equally contributed to this work and are in control over the presented data.

                Article
                MP13322
                10.1002/mp.13322
                7380016
                30506687
                a7da805f-78cc-4d6c-a9a8-73f3a391af33
                © 2018 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 June 2018
                : 14 November 2018
                : 21 November 2018
                Page count
                Figures: 8, Tables: 4, Pages: 14, Words: 8041
                Funding
                Funded by: Netherlands Organisation for Scientific Research (NWO) , open-funder-registry 10.13039/501100003246;
                Funded by: Netherlands Organisation for Health Research and Development , open-funder-registry 10.13039/501100001826;
                Award ID: 10‐10400‐98‐14002
                Funded by: Dutch Cancer Society , open-funder-registry 10.13039/501100004622;
                Award ID: 10034
                Categories
                Research Article
                QUANTITATIVE IMAGING AND IMAGE PROCESSING
                Research Articles
                Custom metadata
                2.0
                February 2019
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.5 mode:remove_FC converted:24.07.2020

                18f‐fdg pet/ct radiomic features,delineation,image reconstruction settings

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