FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3107674782> ?p ?o ?g. }

• W3107674782 endingPage "e200125" @default.
• W3107674782 startingPage "e200125" @default.
• W3107674782 abstract "To train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commonly observed benign masses.In this retrospective study, US images obtained between May 2010 and June 2019 from 419 patients (mean age, 52 years ± 18 [standard deviation]; 250 women) with histologic diagnosis confirmed at biopsy or surgical excision (n = 227) or masses that demonstrated imaging characteristics of lipoma, benign peripheral nerve sheath tumor, and vascular malformation (n = 192) were included. Images in patients with a histologic diagnosis (n = 227) were used to train and evaluate a CNN model to distinguish malignant and benign lesions. Twenty percent of cases were withheld as a test dataset, and the remaining cases were used to train the model with a 75%-25% training-validation split and fourfold cross-validation. Performance of the model was compared with retrospective interpretation of the same dataset by two experienced musculoskeletal radiologists, blinded to clinical history. A second group of US images from 275 of the 419 patients containing the three common benign masses was used to train and evaluate a separate model to differentiate between the masses. The models were trained on the Keras machine learning platform (version 2.3.1), with a modified pretrained VGG16 network. Performance metrics of the model and of the radiologists were compared by using the McNemar test, and 95% CIs for performance metrics were estimated by using the Clopper-Pearson method (accuracy, recall, specificity, and precision) and the DeLong method (area under the receiver operating characteristic curve).The model trained to classify malignant and benign masses demonstrated an accuracy of 79% (95% CI: 68, 88) on the test data, with an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.84, 0.98), matching the performance of two expert readers. Performance of the model distinguishing three benign masses was lower, with an accuracy of 71% (95% CI: 61, 80) on the test data.The trained CNN was capable of differentiating between benign and malignant soft-tissue masses depicted on US images, with performance matching that of two experienced musculoskeletal radiologists.© RSNA, 2020." @default.
• W3107674782 created "2020-12-07" @default.
• W3107674782 creator A5018251444 @default.
• W3107674782 creator A5066674428 @default.
• W3107674782 creator A5072694297 @default.
• W3107674782 creator A5088360975 @default.
• W3107674782 date "2021-01-01" @default.
• W3107674782 modified "2023-10-02" @default.
• W3107674782 title "Artificial Intelligence for Classification of Soft-Tissue Masses at US" @default.
• W3107674782 cites W1995073559 @default.
• W3107674782 cites W2042571564 @default.
• W3107674782 cites W2044483862 @default.
• W3107674782 cites W2084413241 @default.
• W3107674782 cites W2112098815 @default.
• W3107674782 cites W2137591261 @default.
• W3107674782 cites W2139989438 @default.
• W3107674782 cites W2152465492 @default.
• W3107674782 cites W2167829172 @default.
• W3107674782 cites W2328176404 @default.
• W3107674782 cites W2581082771 @default.
• W3107674782 cites W2608231518 @default.
• W3107674782 cites W2888878689 @default.
• W3107674782 cites W2910443141 @default.
• W3107674782 cites W2979819170 @default.
• W3107674782 cites W2982092517 @default.
• W3107674782 cites W2984043001 @default.
• W3107674782 cites W2984489845 @default.
• W3107674782 cites W2996779950 @default.
• W3107674782 cites W3001289497 @default.
• W3107674782 cites W3003903700 @default.
• W3107674782 cites W3004102604 @default.
• W3107674782 cites W3012097287 @default.
• W3107674782 cites W3012548239 @default.
• W3107674782 cites W3013244916 @default.
• W3107674782 cites W3098977020 @default.
• W3107674782 doi "https://doi.org/10.1148/ryai.2020200125" @default.
• W3107674782 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8082295" @default.
• W3107674782 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33937855" @default.
• W3107674782 hasPublicationYear "2021" @default.
• W3107674782 type Work @default.
• W3107674782 sameAs 3107674782 @default.
• W3107674782 citedByCount "13" @default.
• W3107674782 countsByYear W31076747822020 @default.
• W3107674782 countsByYear W31076747822021 @default.
• W3107674782 countsByYear W31076747822022 @default.
• W3107674782 countsByYear W31076747822023 @default.
• W3107674782 crossrefType "journal-article" @default.
• W3107674782 hasAuthorship W3107674782A5018251444 @default.
• W3107674782 hasAuthorship W3107674782A5066674428 @default.
• W3107674782 hasAuthorship W3107674782A5072694297 @default.
• W3107674782 hasAuthorship W3107674782A5088360975 @default.
• W3107674782 hasBestOaLocation W31076747821 @default.
• W3107674782 hasConcept C105795698 @default.
• W3107674782 hasConcept C126322002 @default.
• W3107674782 hasConcept C126838900 @default.
• W3107674782 hasConcept C136948725 @default.
• W3107674782 hasConcept C141071460 @default.
• W3107674782 hasConcept C143409427 @default.
• W3107674782 hasConcept C154945302 @default.
• W3107674782 hasConcept C167135981 @default.
• W3107674782 hasConcept C186282968 @default.
• W3107674782 hasConcept C2775934546 @default.
• W3107674782 hasConcept C33923547 @default.
• W3107674782 hasConcept C41008148 @default.
• W3107674782 hasConcept C58471807 @default.
• W3107674782 hasConcept C71924100 @default.
• W3107674782 hasConcept C81363708 @default.
• W3107674782 hasConceptScore W3107674782C105795698 @default.
• W3107674782 hasConceptScore W3107674782C126322002 @default.
• W3107674782 hasConceptScore W3107674782C126838900 @default.
• W3107674782 hasConceptScore W3107674782C136948725 @default.
• W3107674782 hasConceptScore W3107674782C141071460 @default.
• W3107674782 hasConceptScore W3107674782C143409427 @default.
• W3107674782 hasConceptScore W3107674782C154945302 @default.
• W3107674782 hasConceptScore W3107674782C167135981 @default.
• W3107674782 hasConceptScore W3107674782C186282968 @default.
• W3107674782 hasConceptScore W3107674782C2775934546 @default.
• W3107674782 hasConceptScore W3107674782C33923547 @default.
• W3107674782 hasConceptScore W3107674782C41008148 @default.
• W3107674782 hasConceptScore W3107674782C58471807 @default.
• W3107674782 hasConceptScore W3107674782C71924100 @default.
• W3107674782 hasConceptScore W3107674782C81363708 @default.
• W3107674782 hasIssue "1" @default.
• W3107674782 hasLocation W31076747821 @default.
• W3107674782 hasLocation W31076747822 @default.
• W3107674782 hasLocation W31076747823 @default.
• W3107674782 hasOpenAccess W3107674782 @default.
• W3107674782 hasPrimaryLocation W31076747821 @default.
• W3107674782 hasRelatedWork W2077331106 @default.
• W3107674782 hasRelatedWork W2086301242 @default.
• W3107674782 hasRelatedWork W2108943869 @default.
• W3107674782 hasRelatedWork W2156609790 @default.
• W3107674782 hasRelatedWork W2510224130 @default.
• W3107674782 hasRelatedWork W2980636754 @default.
• W3107674782 hasRelatedWork W3211448982 @default.
• W3107674782 hasRelatedWork W4229439559 @default.
• W3107674782 hasRelatedWork W4246321376 @default.
• W3107674782 hasRelatedWork W2039371645 @default.

• next