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      Intraindividual reproducibility of myocardial radiomic features between energy-integrating detector and photon-counting detector CT angiography

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          Abstract

          Background

          Radiomics is not yet used in clinical practice due to concerns regarding its susceptibility to technical factors. We aimed to assess the stability and interscan and interreader reproducibility of myocardial radiomic features between energy-integrating detector computed tomography (EID-CT) and photon-counting detector CT (PCD-CT) in patients undergoing coronary CT angiography (CCTA) on both systems.

          Methods

          Consecutive patients undergoing clinically indicated CCTA on an EID-CT were prospectively enrolled for a PCD-CT CCTA within 30 days. Virtual monoenergetic images (VMI) at various keV levels and polychromatic images (T3D) were generated for PCD-CT, with image reconstruction parameters standardized between scans. Two readers performed myocardial segmentation and 110 radiomic features were compared intraindividually between EID-CT and PDC-CT series. The agreement of parameters was assessed using the intraclass correlation coefficient and paired t-test for the stability of the parameters.

          Results

          Eighteen patients (15 males) aged 67.6 ± 9.7 years (mean ± standard deviation) were included. Besides polychromatic PCD-CT reconstructions, 60- and 70-keV VMIs showed the highest feature stability compared to EID-CT (96%, 90%, and 92%, respectively). The interscan reproducibility of features was moderate even in the most favorable comparisons (median ICC 0.50 [interquartile range 0.20–0.60] for T3D; 0.56 [0.33–0.74] for 60 keV; 0.50 [0.36–0.62] for 70 keV). Interreader reproducibility was excellent for the PCD-CT series and good for EID-CT segmentations.

          Conclusion

          Most myocardial radiomic features remain stable between EID-CT and PCD-CT. While features demonstrated moderate reproducibility between scanners, technological advances associated with PCD-CT may lead to greater reproducibility, potentially expediting future standardization efforts.

          Relevance statement

          While the use of PCD-CT may facilitate reduced interreader variability in radiomics analysis, the observed interscanner variations in comparison to EID-CT should be taken into account in future research, with efforts being made to minimize their impact in future radiomics studies.

          Key Points

          • Most myocardial radiomic features resulted in being stable between EID-CT and PCD-CT on certain VMIs.

          • The reproducibility of parameters between detector technologies was limited.

          • PCD-CT improved interreader reproducibility of myocardial radiomic features.

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          Most cited references31

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          Radiomics: Images Are More than Pictures, They Are Data

          This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
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            Computational Radiomics System to Decode the Radiographic Phenotype

            Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io . With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.
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              Radiomics: the bridge between medical imaging and personalized medicine

              Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
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                Author and article information

                Contributors
                tilman.emrich@unimedizin-mainz.de
                Journal
                Eur Radiol Exp
                Eur Radiol Exp
                European Radiology Experimental
                Springer Vienna (Vienna )
                2509-9280
                28 August 2024
                28 August 2024
                December 2024
                : 8
                : 101
                Affiliations
                [1 ]Department of Radiology and Radiological Science, Medical University of South Carolina, ( https://ror.org/012jban78) Charleston, SC USA
                [2 ]Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, ( https://ror.org/02be6w209) Rome, Italy
                [3 ]Medical Imaging Centre, Semmelweis University, ( https://ror.org/01g9ty582) Budapest, Hungary
                [4 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Department of Radiology, University Hospital, , LMU Munich, ; Munich, Germany
                [5 ]Siemens Medical Solutions, ( https://ror.org/054962n91) Malvern, PA USA
                [6 ]GRID grid.410607.4, Department of Diagnostic and Interventional Radiology, , University Medical Center of the Johannes Gutenberg-University Mainz, ; Mainz, Germany
                [7 ]Heart and Vascular Center, Semmelweis University, ( https://ror.org/01g9ty582) Budapest, Hungary
                Author information
                http://orcid.org/0000-0003-4156-7727
                Article
                493
                10.1186/s41747-024-00493-7
                11358367
                39196286
                035fe501-af3b-44a7-871e-3fbb32893d1e
                © The Author(s) 2024

                Open Access This 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
                : 8 April 2024
                : 3 July 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100011699, Siemens Healthineers;
                Categories
                Original Article
                Custom metadata
                © European Society of Radiology (ESR) 2024

                computed tomography angiography,myocardium (radiomics),reproducibility of results,tomography (x-ray computed)

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