FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387206426> ?p ?o ?g. }

• W4387206426 endingPage "e86" @default.
• W4387206426 startingPage "e85" @default.
• W4387206426 abstract "Radiation necrosis can be difficult to non-invasively discern from tumor progression after stereotactic radiosurgery (SRS). In this work, we investigate the utility of radiomics (computerized features) and machine learning to capture per-voxel lesion heterogeneity on routine MRI scans, to differentiate radionecrosis from tumor recurrence in patients with brain metastases treated with SRS.A retrospective analysis was conducted of patients with brain metastases treated with SRS. Eighty-three lesions (n = 56 intact; n = 27 surgical cavity) from 69 patients were identified with median age 68.8 years (range 40.2 - 91.0), of whom 53.6% were male and 33.3% received prior whole-brain radiotherapy (WBRT). Lesion histology included lung (60.2%), renal cell (15.7%), melanoma (10.8%), breast (9.6%), and other (3.6%). Pathologic confirmation was available in 73.5% of lesions. Both intact and resection cavity lesions were included and individually segmented. Image preprocessing and radiomic feature extraction were done using ANTsPy and open-source software. A total of 210 features were extracted from post-contrast T1-weighted (T1w) and T2/FLAIR MRIs. Highly correlated features were removed. Univariate logistic regression was conducted on the remaining T1w and T2/FLAIR features as well as on clinical variables. Multivariate analysis was implemented with various classifiers (Random Forest, Ridge, Lasso, Support Vector Machine [SVM]) on the top-performing features found on univariate logistic regression. Models were assessed using cross-validation to select the best model by area under ROC curve (AUC). Specificity and sensitivity were calculated.On univariate analysis, the top 10 radiomics features consisted of 6 T1w features and 4 T2/FLAIR features (4 GLCM, 3 first order, 1 GLSZM, 1 GLRLM, and 1 shape feature). Age, gender, disease site, prior WBRT, prior fractionated SRS, planning tumor volume, brain-GTV V12 Gy, and immunotherapy before or after SRS were not predictive (AUC less than 62.0%) on univariate analysis compared to radiomic features. Multivariate analysis of top performing radiomic features on both intact and surgical cavities yielded an AUC of 72.0% (standard deviation [SD] ±8.8%). Multivariate analysis of top features on intact lesions alone improved the AUC to 80.5% (SD ±10.8%), with sensitivity of 77.8%, specificity of 72.4%, and positive likelihood ratio of 2.82 in differentiating radionecrosis from recurrence.Radiomics and machine learning tools may improve diagnostic ability of distinguishing radiation necrosis from tumor recurrence after SRS. Further work is needed to deploy this in a larger multi-institutional cohort and prospectively evaluate its efficacy as a decision-support tool to personalize care in patients with brain metastases." @default.
• W4387206426 created "2023-09-30" @default.
• W4387206426 creator A5023006263 @default.
• W4387206426 creator A5025879011 @default.
• W4387206426 creator A5026127617 @default.
• W4387206426 creator A5041234276 @default.
• W4387206426 creator A5042392208 @default.
• W4387206426 creator A5047000587 @default.
• W4387206426 creator A5073037017 @default.
• W4387206426 creator A5080493254 @default.
• W4387206426 date "2023-10-01" @default.
• W4387206426 modified "2023-10-14" @default.
• W4387206426 title "Leveraging Quantitative Imaging and Machine Learning to Differentiate Radionecrosis from Disease Recurrence in Patients with Brain Metastases" @default.
• W4387206426 doi "https://doi.org/10.1016/j.ijrobp.2023.06.838" @default.
• W4387206426 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37786199" @default.
• W4387206426 hasPublicationYear "2023" @default.
• W4387206426 type Work @default.
• W4387206426 citedByCount "0" @default.
• W4387206426 crossrefType "journal-article" @default.
• W4387206426 hasAuthorship W4387206426A5023006263 @default.
• W4387206426 hasAuthorship W4387206426A5025879011 @default.
• W4387206426 hasAuthorship W4387206426A5026127617 @default.
• W4387206426 hasAuthorship W4387206426A5041234276 @default.
• W4387206426 hasAuthorship W4387206426A5042392208 @default.
• W4387206426 hasAuthorship W4387206426A5047000587 @default.
• W4387206426 hasAuthorship W4387206426A5073037017 @default.
• W4387206426 hasAuthorship W4387206426A5080493254 @default.
• W4387206426 hasConcept C101070640 @default.
• W4387206426 hasConcept C126322002 @default.
• W4387206426 hasConcept C126838900 @default.
• W4387206426 hasConcept C143409427 @default.
• W4387206426 hasConcept C144301174 @default.
• W4387206426 hasConcept C151956035 @default.
• W4387206426 hasConcept C2780387249 @default.
• W4387206426 hasConcept C2989005 @default.
• W4387206426 hasConcept C38180746 @default.
• W4387206426 hasConcept C509974204 @default.
• W4387206426 hasConcept C58471807 @default.
• W4387206426 hasConcept C71924100 @default.
• W4387206426 hasConceptScore W4387206426C101070640 @default.
• W4387206426 hasConceptScore W4387206426C126322002 @default.
• W4387206426 hasConceptScore W4387206426C126838900 @default.
• W4387206426 hasConceptScore W4387206426C143409427 @default.
• W4387206426 hasConceptScore W4387206426C144301174 @default.
• W4387206426 hasConceptScore W4387206426C151956035 @default.
• W4387206426 hasConceptScore W4387206426C2780387249 @default.
• W4387206426 hasConceptScore W4387206426C2989005 @default.
• W4387206426 hasConceptScore W4387206426C38180746 @default.
• W4387206426 hasConceptScore W4387206426C509974204 @default.
• W4387206426 hasConceptScore W4387206426C58471807 @default.
• W4387206426 hasConceptScore W4387206426C71924100 @default.
• W4387206426 hasIssue "2" @default.
• W4387206426 hasLocation W43872064261 @default.
• W4387206426 hasLocation W43872064262 @default.
• W4387206426 hasOpenAccess W4387206426 @default.
• W4387206426 hasPrimaryLocation W43872064261 @default.
• W4387206426 hasRelatedWork W2013727445 @default.
• W4387206426 hasRelatedWork W2023427764 @default.
• W4387206426 hasRelatedWork W2163132578 @default.
• W4387206426 hasRelatedWork W2374376758 @default.
• W4387206426 hasRelatedWork W2387391090 @default.
• W4387206426 hasRelatedWork W2425878018 @default.
• W4387206426 hasRelatedWork W274490372 @default.
• W4387206426 hasRelatedWork W2784206856 @default.
• W4387206426 hasRelatedWork W3032662949 @default.
• W4387206426 hasRelatedWork W4366602155 @default.
• W4387206426 hasVolume "117" @default.
• W4387206426 isParatext "false" @default.
• W4387206426 isRetracted "false" @default.
• W4387206426 workType "article" @default.