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      FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients

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          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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          Most cited references22

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          Repeatability and Reproducibility of Radiomic Features: A Systematic Review

          Purpose: An ever-growing number of predictive models used to inform clinical decision making have included quantitative, computer-extracted imaging biomarkers, or “radiomic features.” Broadly generalizable validity of radiomics-assisted models may be impeded by concerns about reproducibility. We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features, derived from a systematic review of published peer-reviewed literature. Methods and Materials: The PubMed electronic database was searched using combinations of the broad Haynes and Ingui filters along with a set of text words specific to cancer, radiomics (including texture analyses), reproducibility, and repeatability. This review has been reported in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From each full-text article, information was extracted regarding cancer type, class of radiomic feature examined, reporting quality of key processing steps, and statistical metric used to segregate stable features. Results: Among 624 unique records, 41 full-text articles were subjected to review. The studies primarily addressed non-small cell lung cancer and oropharyngeal cancer. Only 7 studies addressed in detail every methodologic aspect related to image acquisition, preprocessing, and feature extraction. The repeatability and reproducibility of radiomic features are sensitive at various degrees to processing details such as image acquisition settings, image reconstruction algorithm, digital image preprocessing, and software used to extract radiomic features. First-order features were overall more reproducible than shape metrics and textural features. Entropy was consistently reported as one of the most stable first-order features. There was no emergent consensus regarding either shape metrics or textural features; however, coarseness and contrast appeared among the least reproducible. Conclusions: Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. Reporting quality could be improved regarding details of feature extraction software, digital image manipulation (preprocessing), and the cutoff value used to distinguish stable features.
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            The number of subjects per variable required in linear regression analyses.

            To determine the number of independent variables that can be included in a linear regression model.
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              Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters

              Purpose To identify the reproducible and nonredundant radiomics features (RFs) for computed tomography (CT). Materials and Methods Two phantoms were used to test RF reproducibility by using test-retest analysis, by changing the CT acquisition parameters (hereafter, intra-CT analysis), and by comparing five different scanners with the same CT parameters (hereafter, inter-CT analysis). Reproducible RFs were selected by using the concordance correlation coefficient (as a measure of the agreement between variables) and the coefficient of variation (defined as the ratio of the standard deviation to the mean). Redundant features were grouped by using hierarchical cluster analysis. Results A total of 177 RFs including intensity, shape, and texture features were evaluated. The test-retest analysis showed that 91% (161 of 177) of the RFs were reproducible according to concordance correlation coefficient. Reproducibility of intra-CT RFs, based on coefficient of variation, ranged from 89.3% (151 of 177) to 43.1% (76 of 177) where the pitch factor and the reconstruction kernel were modified, respectively. Reproducibility of inter-CT RFs, based on coefficient of variation, also showed large material differences, from 85.3% (151 of 177; wood) to only 15.8% (28 of 177; polyurethane). Ten clusters were identified after the hierarchical cluster analysis and one RF per cluster was chosen as representative. Conclusion Many RFs were redundant and nonreproducible. If all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs (each representing a cluster) because of redundancy.
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                Author and article information

                Contributors
                matea.pavic@usz.ch
                Journal
                EJNMMI Res
                EJNMMI Res
                EJNMMI Research
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2191-219X
                13 July 2020
                13 July 2020
                2020
                : 10
                : 81
                Affiliations
                [1 ]GRID grid.412004.3, ISNI 0000 0004 0478 9977, Department of Radiation Oncology, , University Hospital Zurich and University Zurich, ; Rämistrasse 100, 8091 Zurich, Switzerland
                [2 ]GRID grid.412004.3, ISNI 0000 0004 0478 9977, Department of Thoracic Surgery, , University Hospital Zurich and University Zurich, ; Rämistrasse 100, 8091 Zurich, Switzerland
                [3 ]GRID grid.412004.3, ISNI 0000 0004 0478 9977, Institute of Diagnostic and Interventional Radiology, , University Hospital Zurich and University Zurich, ; Rämistrasse 100, 8091 Zurich, Switzerland
                [4 ]GRID grid.412004.3, ISNI 0000 0004 0478 9977, Department of Nuclear Medicine, , University Hospital Zurich and University Zurich, ; Rämistrasse 100, 8091 Zurich, Switzerland
                Author information
                http://orcid.org/0000-0002-3899-6152
                Article
                669
                10.1186/s13550-020-00669-3
                7359199
                32661672
                52365d3d-bb36-4145-b00b-33946f204842
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 April 2020
                : 2 July 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100006447, Universität Zürich;
                Award ID: Clinical research priority programme CRPP "Artifical Intelligence in oncological imaging"
                Categories
                Original Research
                Custom metadata
                © The Author(s) 2020

                Radiology & Imaging
                radiomics,machine learning,artificial intelligence,malignant pleural mesothelioma,prognostic model,clinical decision support system

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