FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2792589984> ?p ?o ?g. }

• W2792589984 endingPage "2664" @default.
• W2792589984 startingPage "2655" @default.
• W2792589984 abstract "We aimed to investigate if lesion-specific ischaemia by invasive fractional flow reserve (FFR) can be predicted by an integrated machine learning (ML) ischaemia risk score from quantitative plaque measures from coronary computed tomography angiography (CTA).In a multicentre trial of 254 patients, CTA and invasive coronary angiography were performed, with FFR in 484 vessels. CTA data sets were analysed by semi-automated software to quantify stenosis and non-calcified (NCP), low-density NCP (LD-NCP, < 30 HU), calcified and total plaque volumes, contrast density difference (CDD, maximum difference in luminal attenuation per unit area) and plaque length. ML integration included automated feature selection and model building from quantitative CTA with a boosted ensemble algorithm, and tenfold stratified cross-validation.Eighty patients had ischaemia by FFR (FFR ≤ 0.80) in 100 vessels. Information gain for predicting ischaemia was highest for CDD (0.172), followed by LD-NCP (0.125), NCP (0.097), and total plaque volumes (0.092). ML exhibited higher area-under-the-curve (0.84) than individual CTA measures, including stenosis (0.76), LD-NCP volume (0.77), total plaque volume (0.74) and pre-test likelihood of coronary artery disease (CAD) (0.63); p < 0.006.Integrated ML ischaemia risk score improved the prediction of lesion-specific ischaemia by invasive FFR, over stenosis, plaque measures and pre-test likelihood of CAD.• Integrated ischaemia risk score improved prediction of ischaemia over quantitative plaque measures • Integrated ischaemia risk score showed higher prediction of ischaemia than standard approach • Contrast density difference had the highest information gain to identify lesion-specific ischaemia." @default.
• W2792589984 created "2018-03-29" @default.
• W2792589984 creator A5001353746 @default.
• W2792589984 creator A5002757728 @default.
• W2792589984 creator A5009637811 @default.
• W2792589984 creator A5017836717 @default.
• W2792589984 creator A5019801601 @default.
• W2792589984 creator A5037731520 @default.
• W2792589984 creator A5042237487 @default.
• W2792589984 creator A5047468528 @default.
• W2792589984 creator A5052713249 @default.
• W2792589984 creator A5058214014 @default.
• W2792589984 creator A5062520509 @default.
• W2792589984 creator A5073157470 @default.
• W2792589984 creator A5089319978 @default.
• W2792589984 creator A5091259019 @default.
• W2792589984 date "2018-01-19" @default.
• W2792589984 modified "2023-10-17" @default.
• W2792589984 title "Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study" @default.
• W2792589984 cites W1260204519 @default.
• W2792589984 cites W1601195893 @default.
• W2792589984 cites W161598465 @default.
• W2792589984 cites W188581382 @default.
• W2792589984 cites W1936833235 @default.
• W2792589984 cites W1952254076 @default.
• W2792589984 cites W1998650897 @default.
• W2792589984 cites W2024046085 @default.
• W2792589984 cites W2024105966 @default.
• W2792589984 cites W2031576042 @default.
• W2792589984 cites W2031917779 @default.
• W2792589984 cites W2034249063 @default.
• W2792589984 cites W2047207985 @default.
• W2792589984 cites W2052186469 @default.
• W2792589984 cites W2060219325 @default.
• W2792589984 cites W2070955056 @default.
• W2792589984 cites W2072874640 @default.
• W2792589984 cites W2094316018 @default.
• W2792589984 cites W2095680098 @default.
• W2792589984 cites W2101616997 @default.
• W2792589984 cites W2103381850 @default.
• W2792589984 cites W2105981176 @default.
• W2792589984 cites W2109676405 @default.
• W2792589984 cites W2115416118 @default.
• W2792589984 cites W2118810625 @default.
• W2792589984 cites W2123705467 @default.
• W2792589984 cites W2127649313 @default.
• W2792589984 cites W2129073471 @default.
• W2792589984 cites W2132259559 @default.
• W2792589984 cites W2132434940 @default.
• W2792589984 cites W2133990480 @default.
• W2792589984 cites W2141408670 @default.
• W2792589984 cites W2157026796 @default.
• W2792589984 cites W2161812533 @default.
• W2792589984 cites W2177870565 @default.
• W2792589984 cites W2271658865 @default.
• W2792589984 cites W2325881765 @default.
• W2792589984 cites W2328176404 @default.
• W2792589984 cites W2335916523 @default.
• W2792589984 cites W2408866005 @default.
• W2792589984 cites W2525984666 @default.
• W2792589984 cites W2616018170 @default.
• W2792589984 cites W2743269518 @default.
• W2792589984 cites W43533071 @default.
• W2792589984 doi "https://doi.org/10.1007/s00330-017-5223-z" @default.
• W2792589984 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5940537" @default.
• W2792589984 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29352380" @default.
• W2792589984 hasPublicationYear "2018" @default.
• W2792589984 type Work @default.
• W2792589984 sameAs 2792589984 @default.
• W2792589984 citedByCount "123" @default.
• W2792589984 countsByYear W27925899842018 @default.
• W2792589984 countsByYear W27925899842019 @default.
• W2792589984 countsByYear W27925899842020 @default.
• W2792589984 countsByYear W27925899842021 @default.
• W2792589984 countsByYear W27925899842022 @default.
• W2792589984 countsByYear W27925899842023 @default.
• W2792589984 crossrefType "journal-article" @default.
• W2792589984 hasAuthorship W2792589984A5001353746 @default.
• W2792589984 hasAuthorship W2792589984A5002757728 @default.
• W2792589984 hasAuthorship W2792589984A5009637811 @default.
• W2792589984 hasAuthorship W2792589984A5017836717 @default.
• W2792589984 hasAuthorship W2792589984A5019801601 @default.
• W2792589984 hasAuthorship W2792589984A5037731520 @default.
• W2792589984 hasAuthorship W2792589984A5042237487 @default.
• W2792589984 hasAuthorship W2792589984A5047468528 @default.
• W2792589984 hasAuthorship W2792589984A5052713249 @default.
• W2792589984 hasAuthorship W2792589984A5058214014 @default.
• W2792589984 hasAuthorship W2792589984A5062520509 @default.
• W2792589984 hasAuthorship W2792589984A5073157470 @default.
• W2792589984 hasAuthorship W2792589984A5089319978 @default.
• W2792589984 hasAuthorship W2792589984A5091259019 @default.
• W2792589984 hasBestOaLocation W27925899842 @default.
• W2792589984 hasConcept C118552586 @default.
• W2792589984 hasConcept C126322002 @default.
• W2792589984 hasConcept C126838900 @default.
• W2792589984 hasConcept C164705383 @default.
• W2792589984 hasConcept C16568411 @default.
• W2792589984 hasConcept C2777987666 @default.

• next