FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312106028> ?p ?o ?g. }

• W4312106028 endingPage "1647" @default.
• W4312106028 startingPage "1638" @default.
• W4312106028 abstract "Rationale and objectives To investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics at baseline and after two cycles of neoadjuvant therapy (NAT) and associated longitudinal changes for early prediction of the NAT response in patients with breast cancer. Materials and Methods One hundred seventeen patients with breast cancer who underwent DCE-MRI before NAT and after two cycles of NAT from April 2019 to November 2021 were enrolled retrospectively. Patients were randomly divided into a training set (n = 81) and a test set (n = 36) at a ratio of 7:3. Clinical-pathological data and the relative tumor maximum diameter regression value (diameter%) were also collected. A total of 851 radiomic features were extracted from the phase with the most pronounced tumor enhancement on DCE-MRI T1 imaging acquired both pre- and post-treatment. Delta and delta% radiomics features were also calculated. The Least Absolute Shrinkage and Selection Operator (LASSO) method was applied to select features, and a logistic regression model was used to calculate pre-NAT, early-NAT, delta, and delta% radscores and then select among four radscores to build a Fusion radiomics model. The final clinical-radiomics model was constructed by combining fusion radscores and clinical-pathological variables. The discrimination and clinical utility of the models were further evaluated and compared. Results The area under the curve (AUC) values of the fusion radiomics model based on pre-NAT, Delta, and Delta% radscores were 0.868 of 0.825. The clinical-radiomics model integrating Fusion radscores and clinical-pathological variables achieved AUC values of 0.920 of 0.884, which were higher than those of the clinical model constructed by AUC values (0.858/0.831), although no significant improvement was observed in the test set (Delong test, p = 0.196). Decision curve analysis (DCA) showed that the clinical-radiomics model demonstrated more clinical utility than the clinical model. Conclusion DCE-MRI-based radiomics features may have potential for pathological complete response (pCR) prediction in the early phase of NAT. By combining radiomics features and clinical-pathological characteristics, higher diagnostic performance can be achieved. To investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics at baseline and after two cycles of neoadjuvant therapy (NAT) and associated longitudinal changes for early prediction of the NAT response in patients with breast cancer. One hundred seventeen patients with breast cancer who underwent DCE-MRI before NAT and after two cycles of NAT from April 2019 to November 2021 were enrolled retrospectively. Patients were randomly divided into a training set (n = 81) and a test set (n = 36) at a ratio of 7:3. Clinical-pathological data and the relative tumor maximum diameter regression value (diameter%) were also collected. A total of 851 radiomic features were extracted from the phase with the most pronounced tumor enhancement on DCE-MRI T1 imaging acquired both pre- and post-treatment. Delta and delta% radiomics features were also calculated. The Least Absolute Shrinkage and Selection Operator (LASSO) method was applied to select features, and a logistic regression model was used to calculate pre-NAT, early-NAT, delta, and delta% radscores and then select among four radscores to build a Fusion radiomics model. The final clinical-radiomics model was constructed by combining fusion radscores and clinical-pathological variables. The discrimination and clinical utility of the models were further evaluated and compared. The area under the curve (AUC) values of the fusion radiomics model based on pre-NAT, Delta, and Delta% radscores were 0.868 of 0.825. The clinical-radiomics model integrating Fusion radscores and clinical-pathological variables achieved AUC values of 0.920 of 0.884, which were higher than those of the clinical model constructed by AUC values (0.858/0.831), although no significant improvement was observed in the test set (Delong test, p = 0.196). Decision curve analysis (DCA) showed that the clinical-radiomics model demonstrated more clinical utility than the clinical model. DCE-MRI-based radiomics features may have potential for pathological complete response (pCR) prediction in the early phase of NAT. By combining radiomics features and clinical-pathological characteristics, higher diagnostic performance can be achieved." @default.
• W4312106028 created "2023-01-04" @default.
• W4312106028 creator A5004975090 @default.
• W4312106028 creator A5031527634 @default.
• W4312106028 creator A5045078329 @default.
• W4312106028 creator A5052702846 @default.
• W4312106028 creator A5064203621 @default.
• W4312106028 creator A5068204486 @default.
• W4312106028 creator A5091306991 @default.
• W4312106028 date "2023-08-01" @default.
• W4312106028 modified "2023-10-14" @default.
• W4312106028 title "Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy" @default.
• W4312106028 cites W1830592026 @default.
• W4312106028 cites W1841467563 @default.
• W4312106028 cites W1966804628 @default.
• W4312106028 cites W1987326983 @default.
• W4312106028 cites W2026616100 @default.
• W4312106028 cites W2038833688 @default.
• W4312106028 cites W2050761481 @default.
• W4312106028 cites W2115791320 @default.
• W4312106028 cites W2128739912 @default.
• W4312106028 cites W2150539933 @default.
• W4312106028 cites W2165932245 @default.
• W4312106028 cites W2379981061 @default.
• W4312106028 cites W2521067254 @default.
• W4312106028 cites W2616461360 @default.
• W4312106028 cites W2896886167 @default.
• W4312106028 cites W2898574163 @default.
• W4312106028 cites W2903828121 @default.
• W4312106028 cites W2921520311 @default.
• W4312106028 cites W3000072063 @default.
• W4312106028 cites W3011899248 @default.
• W4312106028 cites W3014792565 @default.
• W4312106028 cites W3029973562 @default.
• W4312106028 cites W3042515931 @default.
• W4312106028 cites W3122861570 @default.
• W4312106028 cites W3128646645 @default.
• W4312106028 cites W3132834592 @default.
• W4312106028 cites W3137043506 @default.
• W4312106028 cites W3142995189 @default.
• W4312106028 cites W3177578585 @default.
• W4312106028 cites W3177896729 @default.
• W4312106028 cites W3195861792 @default.
• W4312106028 cites W4205415510 @default.
• W4312106028 cites W4210600686 @default.
• W4312106028 doi "https://doi.org/10.1016/j.acra.2022.11.006" @default.
• W4312106028 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36564256" @default.
• W4312106028 hasPublicationYear "2023" @default.
• W4312106028 type Work @default.
• W4312106028 citedByCount "2" @default.
• W4312106028 countsByYear W43121060282023 @default.
• W4312106028 crossrefType "journal-article" @default.
• W4312106028 hasAuthorship W4312106028A5004975090 @default.
• W4312106028 hasAuthorship W4312106028A5031527634 @default.
• W4312106028 hasAuthorship W4312106028A5045078329 @default.
• W4312106028 hasAuthorship W4312106028A5052702846 @default.
• W4312106028 hasAuthorship W4312106028A5064203621 @default.
• W4312106028 hasAuthorship W4312106028A5068204486 @default.
• W4312106028 hasAuthorship W4312106028A5091306991 @default.
• W4312106028 hasConcept C121608353 @default.
• W4312106028 hasConcept C126322002 @default.
• W4312106028 hasConcept C126838900 @default.
• W4312106028 hasConcept C136764020 @default.
• W4312106028 hasConcept C143409427 @default.
• W4312106028 hasConcept C151956035 @default.
• W4312106028 hasConcept C182516595 @default.
• W4312106028 hasConcept C207886595 @default.
• W4312106028 hasConcept C2778292576 @default.
• W4312106028 hasConcept C2778559731 @default.
• W4312106028 hasConcept C2989005 @default.
• W4312106028 hasConcept C31258907 @default.
• W4312106028 hasConcept C37616216 @default.
• W4312106028 hasConcept C41008148 @default.
• W4312106028 hasConcept C530470458 @default.
• W4312106028 hasConcept C58471807 @default.
• W4312106028 hasConcept C71924100 @default.
• W4312106028 hasConceptScore W4312106028C121608353 @default.
• W4312106028 hasConceptScore W4312106028C126322002 @default.
• W4312106028 hasConceptScore W4312106028C126838900 @default.
• W4312106028 hasConceptScore W4312106028C136764020 @default.
• W4312106028 hasConceptScore W4312106028C143409427 @default.
• W4312106028 hasConceptScore W4312106028C151956035 @default.
• W4312106028 hasConceptScore W4312106028C182516595 @default.
• W4312106028 hasConceptScore W4312106028C207886595 @default.
• W4312106028 hasConceptScore W4312106028C2778292576 @default.
• W4312106028 hasConceptScore W4312106028C2778559731 @default.
• W4312106028 hasConceptScore W4312106028C2989005 @default.
• W4312106028 hasConceptScore W4312106028C31258907 @default.
• W4312106028 hasConceptScore W4312106028C37616216 @default.
• W4312106028 hasConceptScore W4312106028C41008148 @default.
• W4312106028 hasConceptScore W4312106028C530470458 @default.
• W4312106028 hasConceptScore W4312106028C58471807 @default.
• W4312106028 hasConceptScore W4312106028C71924100 @default.
• W4312106028 hasFunder F4320326689 @default.
• W4312106028 hasFunder F4320330771 @default.
• W4312106028 hasFunder F4320332531 @default.
• W4312106028 hasIssue "8" @default.
• W4312106028 hasLocation W43121060281 @default.

• next