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<PublisherName>jmedicalcasereports</PublisherName>
<JournalTitle>Frontiers in Medical Case Reports</JournalTitle>
<PISSN>I</PISSN>
<EISSN>S</EISSN>
<Volume-Issue>Volume 7; Issue 3</Volume-Issue>
<PartNumber/>
<IssueTopic>Multidisciplinary</IssueTopic>
<IssueLanguage>English</IssueLanguage>
<Season>(May-Jun, 2026)</Season>
<SpecialIssue>N</SpecialIssue>
<SupplementaryIssue>N</SupplementaryIssue>
<IssueOA>Y</IssueOA>
<PubDate>
<Year>-0001</Year>
<Month>11</Month>
<Day>30</Day>
</PubDate>
<ArticleType>Medical Case Reports</ArticleType>
<ArticleTitle>Machine Learning Models Based on Clinical__ampersandsignndash;Radiological and Radiomics Features for Preoperative Prediction of Locoregional Staging in Pediatric Wilms Tumor</ArticleTitle>
<SubTitle/>
<ArticleLanguage>English</ArticleLanguage>
<ArticleOA>Y</ArticleOA>
<FirstPage>1</FirstPage>
<LastPage>15</LastPage>
<AuthorList>
<Author>
<FirstName>Jian</FirstName>
<LastName>Tao</LastName>
<AuthorLanguage>English</AuthorLanguage>
<Affiliation/>
<CorrespondingAuthor>N</CorrespondingAuthor>
<ORCID/>
<FirstName>Zi</FirstName>
<LastName>Xu</LastName>
<AuthorLanguage>English</AuthorLanguage>
<Affiliation/>
<CorrespondingAuthor>Y</CorrespondingAuthor>
<ORCID/>
<FirstName>Haiyan</FirstName>
<LastName>Ma</LastName>
<AuthorLanguage>English</AuthorLanguage>
<Affiliation/>
<CorrespondingAuthor>Y</CorrespondingAuthor>
<ORCID/>
<FirstName>Fang</FirstName>
<LastName>Wang</LastName>
<AuthorLanguage>English</AuthorLanguage>
<Affiliation/>
<CorrespondingAuthor>Y</CorrespondingAuthor>
<ORCID/>
<FirstName>Xianchun</FirstName>
<LastName>Zeng</LastName>
<AuthorLanguage>English</AuthorLanguage>
<Affiliation/>
<CorrespondingAuthor>Y</CorrespondingAuthor>
<ORCID/>
<FirstName>Yunsong</FirstName>
<LastName>Peng</LastName>
<AuthorLanguage>English</AuthorLanguage>
<Affiliation/>
<CorrespondingAuthor>Y</CorrespondingAuthor>
<ORCID/>
</Author>
</AuthorList>
<DOI/>
<Abstract>Purpose: To explore the value of machine learning models based on clinical__ampersandsignndash;radiological and radiomics features to preoperatively predict locoregional staging in pediatric Wilms tumor (WT). Materials and Methods: We enrolled 95 cases of pediatric WT confirmed by postoperative pathology (training cohort: n = 66, test cohort: n = 29). Using the WT staging system of the Children__ampersandsignrsquo;s Oncology Group, patients were divided into two groups, stage I (n = 60) and stage II__ampersandsignndash;III (n = 35). We used univariate and multivariate regression to analyze clinical and radiological features and identify clinical independent predictors. Radiomics features were extracted from preoperative portal venous-phase images of abdominal computed tomography scans. We screened for the optimal radiomics features using dimensionality reduction and selection. Logistic regression (LR), random forest (RF), and support vector machine (SVM) models, were developed based on the selected optimal radiomics features and clinical independent predictors. The predictive performance and clinical benefit of each model were assessed using the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). The performance of each model was comprehensively evaluated using area under the curve (AUC), accuracy, and F1 score. Results: Tumor morphology was the only clinically independent predictor. In total, ten optimal radiomics features were selected. The predictive performance of the models was good, with RF having the best overall performance. In the test cohort, the AUCs for the RF, LR, and SVM models were 0.737, 0.727, and 0.712, respectively, the F1 scores were 0.615, 0.583, and 0.583, respectively, and consistent accuracy was 65.5% for all models. The calibration curves indicated good consistency between the actual and predicted results for each model. The DCA indicated that all three models provided clinical net benefits. Conclusion: Machine learning models based on radiomics features and tumor morphology can preoperatively predict locoregional staging of pediatric WT.</Abstract>
<AbstractLanguage>English</AbstractLanguage>
<Keywords>Wilms Tumor,Clinical Staging,Tomography,X-Ray Computed,Radiomics,Machine Learning</Keywords>
<URLs>
<Abstract>https://www.jmedicalcasereports.org/ubijournal-v1copy/journals/abstract.php?article_id=16231&title=Machine Learning Models Based on Clinical__ampersandsignndash;Radiological and Radiomics Features for Preoperative Prediction of Locoregional Staging in Pediatric Wilms Tumor</Abstract>
</URLs>
<References>
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<ReferenceslastPage>19</ReferenceslastPage>
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