FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386263507> ?p ?o ?g. }

• W4386263507 endingPage "5640" @default.
• W4386263507 startingPage "5622" @default.
• W4386263507 abstract "The aim of this study was to develop a radiomics machine learning model based on computed tomography (CT) that can predict whether thymic epithelial tumors (TETs) can be separated from veins during surgery and to compare the accuracy of the radiomics model to that of radiologists.Patients who underwent thymectomy at our hospital from 2009 to 2017 were included in the screening process. After the selection of patients according to the inclusion and exclusion criteria, the cohort was randomly divided into training and testing groups, and CT images of these patients were collected. Subsequently, two-dimensional (2D) and three-dimensional (3D) regions of interest were labelled using ITK-SNAP 3.8.0 software, and Radiomics features were extracted using Python software (Python Software Foundation) and selected through the least absolute shrinkage and selection operator (LASSO) regression model. To construct the classifier, a support vector machine (SVM) was employed, and a nomogram was created using logistic regression to predict vascular inseparable TETs based on the radiomics score (radscore) and image features. To assess the accuracy of these models, area under receiver operating characteristic (ROC) curves of these models were calculated, and differences among the models were identified using the Delong test.In this retrospective study, 204 patients with TETs were included, among whom 21 were diagnosed with surgical vascularly inseparable TETs. The area under ROC curve (AUC) of the 2D model, 3D model, 2D + 3D model, and radiologist diagnoses were 0.94, 0.92, 0.95, and 0.87 in the training cohort and 0.95, 0.92, 0.98, and 0.78 in testing cohort, respectively. The Delong test revealed a significant improvement in the performance of the radiomics models compared to radiologists' diagnoses. The logistic regression selected 3 image features, namely maximum diameter of the tumor, degree of abutment of vessel circumference >50%, and absence of the mediastinal fat layer or space between the tumor and surrounding structures. These features, along with the radscore, were included to develop a nomogram. The AUCs of this nomogram were 0.99 in both the training set and testing set, and the Delong test did not find a significant difference between ROC plots of the nomogram and radiomics models.The proposed radiomics model could accurately predict surgical vascularly inseparable TETs preoperatively and was shown to have a higher predictive value than the radiologists." @default.
• W4386263507 created "2023-08-30" @default.
• W4386263507 creator A5037015805 @default.
• W4386263507 creator A5045359188 @default.
• W4386263507 creator A5047761025 @default.
• W4386263507 creator A5054564556 @default.
• W4386263507 creator A5055567970 @default.
• W4386263507 creator A5060742226 @default.
• W4386263507 creator A5063865607 @default.
• W4386263507 creator A5064369996 @default.
• W4386263507 creator A5064442417 @default.
• W4386263507 creator A5083611970 @default.
• W4386263507 date "2023-09-01" @default.
• W4386263507 modified "2023-10-14" @default.
• W4386263507 title "The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein" @default.
• W4386263507 cites W1875240237 @default.
• W4386263507 cites W2051854131 @default.
• W4386263507 cites W2187700984 @default.
• W4386263507 cites W2229233325 @default.
• W4386263507 cites W2754000510 @default.
• W4386263507 cites W2767128594 @default.
• W4386263507 cites W2794293522 @default.
• W4386263507 cites W2946868693 @default.
• W4386263507 cites W2949746657 @default.
• W4386263507 cites W2981000855 @default.
• W4386263507 cites W2993944961 @default.
• W4386263507 cites W3005956073 @default.
• W4386263507 cites W3010662565 @default.
• W4386263507 cites W3024654886 @default.
• W4386263507 cites W3030921761 @default.
• W4386263507 cites W3034050503 @default.
• W4386263507 cites W3064574647 @default.
• W4386263507 cites W3093417629 @default.
• W4386263507 cites W3098585559 @default.
• W4386263507 cites W3110863459 @default.
• W4386263507 cites W3134563761 @default.
• W4386263507 cites W3135796979 @default.
• W4386263507 cites W3138565626 @default.
• W4386263507 cites W3173751756 @default.
• W4386263507 cites W3177896729 @default.
• W4386263507 cites W3199559618 @default.
• W4386263507 cites W3201546270 @default.
• W4386263507 cites W4200243233 @default.
• W4386263507 cites W4200606490 @default.
• W4386263507 cites W4221131374 @default.
• W4386263507 cites W4282930728 @default.
• W4386263507 cites W4360852809 @default.
• W4386263507 doi "https://doi.org/10.21037/qims-22-1050" @default.
• W4386263507 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37711814" @default.
• W4386263507 hasPublicationYear "2023" @default.
• W4386263507 type Work @default.
• W4386263507 citedByCount "0" @default.
• W4386263507 crossrefType "journal-article" @default.
• W4386263507 hasAuthorship W4386263507A5037015805 @default.
• W4386263507 hasAuthorship W4386263507A5045359188 @default.
• W4386263507 hasAuthorship W4386263507A5047761025 @default.
• W4386263507 hasAuthorship W4386263507A5054564556 @default.
• W4386263507 hasAuthorship W4386263507A5055567970 @default.
• W4386263507 hasAuthorship W4386263507A5060742226 @default.
• W4386263507 hasAuthorship W4386263507A5063865607 @default.
• W4386263507 hasAuthorship W4386263507A5064369996 @default.
• W4386263507 hasAuthorship W4386263507A5064442417 @default.
• W4386263507 hasAuthorship W4386263507A5083611970 @default.
• W4386263507 hasBestOaLocation W43862635071 @default.
• W4386263507 hasConcept C126322002 @default.
• W4386263507 hasConcept C126838900 @default.
• W4386263507 hasConcept C136764020 @default.
• W4386263507 hasConcept C141071460 @default.
• W4386263507 hasConcept C151956035 @default.
• W4386263507 hasConcept C167135981 @default.
• W4386263507 hasConcept C2778559731 @default.
• W4386263507 hasConcept C34626388 @default.
• W4386263507 hasConcept C37616216 @default.
• W4386263507 hasConcept C41008148 @default.
• W4386263507 hasConcept C58471807 @default.
• W4386263507 hasConcept C71924100 @default.
• W4386263507 hasConceptScore W4386263507C126322002 @default.
• W4386263507 hasConceptScore W4386263507C126838900 @default.
• W4386263507 hasConceptScore W4386263507C136764020 @default.
• W4386263507 hasConceptScore W4386263507C141071460 @default.
• W4386263507 hasConceptScore W4386263507C151956035 @default.
• W4386263507 hasConceptScore W4386263507C167135981 @default.
• W4386263507 hasConceptScore W4386263507C2778559731 @default.
• W4386263507 hasConceptScore W4386263507C34626388 @default.
• W4386263507 hasConceptScore W4386263507C37616216 @default.
• W4386263507 hasConceptScore W4386263507C41008148 @default.
• W4386263507 hasConceptScore W4386263507C58471807 @default.
• W4386263507 hasConceptScore W4386263507C71924100 @default.
• W4386263507 hasIssue "9" @default.
• W4386263507 hasLocation W43862635071 @default.
• W4386263507 hasLocation W43862635072 @default.
• W4386263507 hasOpenAccess W4386263507 @default.
• W4386263507 hasPrimaryLocation W43862635071 @default.
• W4386263507 hasRelatedWork W3153073605 @default.
• W4386263507 hasRelatedWork W3164925000 @default.
• W4386263507 hasRelatedWork W3170297370 @default.
• W4386263507 hasRelatedWork W3182298349 @default.
• W4386263507 hasRelatedWork W3207094908 @default.

• next