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      CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children

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          Abstract

          Background

          To investigate the role of CT radiomics in distinguishing Wilms tumor (WT) from clear cell sarcoma of the kidney (CCSK) in pediatric patients.

          Methods

          We retrospectively enrolled 83 cases of WT and 33 cases of CCSK. These cases were randomly stratified into a training set ( n = 81) and a test set ( n = 35). Several imaging features from the nephrographic phase were analyzed, including the maximum tumor diameter, the ratio of the maximum CT value of the tumor solid portion to the mean CT value of the contralateral renal vein (CTmax/CT renal vein), and the presence of dilated peritumoral cysts. Radiomics features from corticomedullary phase were extracted, selected, and subsequently integrated into a logistic regression model. We evaluated the model's performance using the area under the curve (AUC), 95% confidence interval (CI), and accuracy.

          Results

          In the training set, there were statistically significant differences in the maximum tumor diameter ( P = 0.021) and the presence of dilated peritumoral cysts ( P = 0.005) between WT and CCSK, whereas in the test set, no statistically significant differences were observed ( P > 0.05). The radiomics model, constructed using four radiomics features, demonstrated strong performance in the training set with an AUC of 0.889 (95% CI: 0.811–0.967) and an accuracy of 0.864. Upon evaluation using fivefold cross-validation in the training set, the AUC remained high at 0.863 (95% CI: 0.774–0.952), with an accuracy of 0.852. In the test set, the radiomics model achieved an AUC of 0.792 (95% CI: 0.616–0.968) and an accuracy of 0.857.

          Conclusion

          CT radiomics proves to be diagnostically valuable for distinguishing between WT and CCSK in pediatric cases.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12880-023-01184-2.

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          Most cited references29

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          Introduction to Radiomics

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            FeAture Explorer (FAE): A tool for developing and comparing radiomics models

            In radiomics studies, researchers usually need to develop a supervised machine learning model to map image features onto the clinical conclusion. A classical machine learning pipeline consists of several steps, including normalization, feature selection, and classification. It is often tedious to find an optimal pipeline with appropriate combinations. We designed an open-source software package named FeAture Explorer (FAE). It was programmed with Python and used NumPy, pandas, and scikit-learning modules. FAE can be used to extract image features, preprocess the feature matrix, develop different models automatically, and evaluate them with common clinical statistics. FAE features a user-friendly graphical user interface that can be used by radiologists and researchers to build many different pipelines, and to compare their results visually. To prove the effectiveness of FAE, we developed a candidate model to classify the clinical-significant prostate cancer (CS PCa) and non-CS PCa using the PROSTATEx dataset. We used FAE to try out different combinations of feature selectors and classifiers, compare the area under the receiver operating characteristic curve of different models on the validation dataset, and evaluate the model using independent test data. The final model with the analysis of variance as the feature selector and linear discriminate analysis as the classifier was selected and evaluated conveniently by FAE. The area under the receiver operating characteristic curve on the training, validation, and test dataset achieved results of 0.838, 0.814, and 0.824, respectively. FAE allows researchers to build radiomics models and evaluate them using an independent testing dataset. It also provides easy model comparison and result visualization. We believe FAE can be a convenient tool for radiomics studies and other medical studies involving supervised machine learning.
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              A review of original articles published in the emerging field of radiomics

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                Author and article information

                Contributors
                doctorheling@yeah.net
                Journal
                BMC Med Imaging
                BMC Med Imaging
                BMC Medical Imaging
                BioMed Central (London )
                1471-2342
                5 January 2024
                5 January 2024
                2024
                : 24
                : 13
                Affiliations
                Department of Radiology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, ( https://ror.org/05pz4ws32) Chongqing, 400014 China
                Author information
                http://orcid.org/0000-0003-2497-0872
                Article
                1184
                10.1186/s12880-023-01184-2
                10768092
                38182986
                f8d3b0bd-29c1-46df-b810-e89aabe727b1
                © 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 29 May 2023
                : 15 December 2023
                Funding
                Funded by: National Natural Science Foundation of Chongqing
                Award ID: (CSTB)2023NSCQ-BHX0127
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Radiology & Imaging
                children,clear cell sarcoma of the kidney,computed tomography,radiomics,wilms tumor

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