FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386116610> ?p ?o ?g. }

• W4386116610 abstract "Existing brain extraction models should be further optimized to provide more information for oncological analysis. We aimed to develop an nnU-Net-based deep learning model for automated brain extraction on contrast-enhanced T1-weighted (T1CE) images in presence of brain tumors.This is a multi-center, retrospective study involving 920 patients. A total of 720 cases with four types of intracranial tumors from private institutions were collected and set as the training group and the internal test group. Mann-Whitney U test (U test) was used to investigate if the model performance was associated with pathological types and tumor characteristics. Then, the generalization of model was independently tested on public datasets consisting of 100 glioma and 100 vestibular schwannoma cases.In the internal test, the model achieved promising performance with median Dice similarity coefficient (DSC) of 0.989 (interquartile range (IQR), 0.988-0.991), and Hausdorff distance (HD) of 6.403 mm (IQR, 5.099-8.426 mm). U test suggested a slightly descending performance in meningioma and vestibular schwannoma group. The results of U test also suggested that there was a significant difference in peritumoral edema group, with median DSC of 0.990 (IQR, 0.989-0.991, p = 0.002), and median HD of 5.916 mm (IQR, 5.000-8.000 mm, p = 0.049). In the external test, our model also showed to be robust performance, with median DSC of 0.991 (IQR, 0.983-0.998) and HD of 8.972 mm (IQR, 6.164-13.710 mm).For automated processing of MRI neuroimaging data presence of brain tumors, the proposed model can perform brain extraction including important superficial structures for oncological analysis.The proposed model serves as a radiological tool for image preprocessing in tumor cases, focusing on superficial brain structures, which could streamline the workflow and enhance the efficiency of subsequent radiological assessments.• The nnU-Net-based model is capable of segmenting significant superficial structures in brain extraction. • The proposed model showed feasible performance, regardless of pathological types or tumor characteristics. • The model showed generalization in the public datasets." @default.
• W4386116610 created "2023-08-25" @default.
• W4386116610 creator A5011130572 @default.
• W4386116610 creator A5011883978 @default.
• W4386116610 creator A5040721382 @default.
• W4386116610 creator A5046915157 @default.
• W4386116610 creator A5063528590 @default.
• W4386116610 creator A5089130207 @default.
• W4386116610 date "2023-08-24" @default.
• W4386116610 modified "2023-09-27" @default.
• W4386116610 title "Automated, fast, robust brain extraction on contrast-enhanced T1-weighted MRI in presence of brain tumors: an optimized model based on multi-center datasets" @default.
• W4386116610 cites W1999216386 @default.
• W4386116610 cites W2026616100 @default.
• W4386116610 cites W2045699027 @default.
• W4386116610 cites W2103248916 @default.
• W4386116610 cites W2131104747 @default.
• W4386116610 cites W2145661921 @default.
• W4386116610 cites W2284198383 @default.
• W4386116610 cites W2524444173 @default.
• W4386116610 cites W2534251519 @default.
• W4386116610 cites W2742774307 @default.
• W4386116610 cites W2768489043 @default.
• W4386116610 cites W2772075723 @default.
• W4386116610 cites W2808185485 @default.
• W4386116610 cites W2810024032 @default.
• W4386116610 cites W2913223168 @default.
• W4386116610 cites W2968446587 @default.
• W4386116610 cites W3017440719 @default.
• W4386116610 cites W3025885500 @default.
• W4386116610 cites W3037636883 @default.
• W4386116610 cites W3104087655 @default.
• W4386116610 cites W3112701542 @default.
• W4386116610 cites W3133026616 @default.
• W4386116610 cites W3184981399 @default.
• W4386116610 cites W3190678206 @default.
• W4386116610 cites W3197636907 @default.
• W4386116610 cites W3205449701 @default.
• W4386116610 cites W3208578242 @default.
• W4386116610 cites W4200288742 @default.
• W4386116610 cites W4283588740 @default.
• W4386116610 doi "https://doi.org/10.1007/s00330-023-10078-4" @default.
• W4386116610 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37615767" @default.
• W4386116610 hasPublicationYear "2023" @default.
• W4386116610 type Work @default.
• W4386116610 citedByCount "0" @default.
• W4386116610 crossrefType "journal-article" @default.
• W4386116610 hasAuthorship W4386116610A5011130572 @default.
• W4386116610 hasAuthorship W4386116610A5011883978 @default.
• W4386116610 hasAuthorship W4386116610A5040721382 @default.
• W4386116610 hasAuthorship W4386116610A5046915157 @default.
• W4386116610 hasAuthorship W4386116610A5063528590 @default.
• W4386116610 hasAuthorship W4386116610A5089130207 @default.
• W4386116610 hasBestOaLocation W43861166101 @default.
• W4386116610 hasConcept C118552586 @default.
• W4386116610 hasConcept C119060515 @default.
• W4386116610 hasConcept C126322002 @default.
• W4386116610 hasConcept C126838900 @default.
• W4386116610 hasConcept C12868164 @default.
• W4386116610 hasConcept C16568411 @default.
• W4386116610 hasConcept C191093355 @default.
• W4386116610 hasConcept C2779889316 @default.
• W4386116610 hasConcept C2781447767 @default.
• W4386116610 hasConcept C2989005 @default.
• W4386116610 hasConcept C513090587 @default.
• W4386116610 hasConcept C71924100 @default.
• W4386116610 hasConceptScore W4386116610C118552586 @default.
• W4386116610 hasConceptScore W4386116610C119060515 @default.
• W4386116610 hasConceptScore W4386116610C126322002 @default.
• W4386116610 hasConceptScore W4386116610C126838900 @default.
• W4386116610 hasConceptScore W4386116610C12868164 @default.
• W4386116610 hasConceptScore W4386116610C16568411 @default.
• W4386116610 hasConceptScore W4386116610C191093355 @default.
• W4386116610 hasConceptScore W4386116610C2779889316 @default.
• W4386116610 hasConceptScore W4386116610C2781447767 @default.
• W4386116610 hasConceptScore W4386116610C2989005 @default.
• W4386116610 hasConceptScore W4386116610C513090587 @default.
• W4386116610 hasConceptScore W4386116610C71924100 @default.
• W4386116610 hasFunder F4320334798 @default.
• W4386116610 hasLocation W43861166101 @default.
• W4386116610 hasLocation W43861166102 @default.
• W4386116610 hasOpenAccess W4386116610 @default.
• W4386116610 hasPrimaryLocation W43861166101 @default.
• W4386116610 hasRelatedWork W1490396055 @default.
• W4386116610 hasRelatedWork W1998837992 @default.
• W4386116610 hasRelatedWork W2008209885 @default.
• W4386116610 hasRelatedWork W2011959699 @default.
• W4386116610 hasRelatedWork W2040250064 @default.
• W4386116610 hasRelatedWork W2335628796 @default.
• W4386116610 hasRelatedWork W2602112854 @default.
• W4386116610 hasRelatedWork W2912446563 @default.
• W4386116610 hasRelatedWork W3023529681 @default.
• W4386116610 hasRelatedWork W4243229496 @default.
• W4386116610 isParatext "false" @default.
• W4386116610 isRetracted "false" @default.
• W4386116610 workType "article" @default.