Matches in SemOpenAlex for { <https://semopenalex.org/work/W4362664457> ?p ?o ?g. }
Showing items 1 to 78 of
78
with 100 items per page.
- W4362664457 abstract "Coronary plaque risk classification in images acquired with photon-counting-detector (PCD) CT was performed using a radiomics-based machine learning (ML) model. With IRB approval, 19 coronary CTA patients were scanned on a PCD-CT (NAEOTOM Alpha, Siemens Healthineers) with median CTDIvol of 8.02 mGy. Five types of images: virtual monoenergetic images (VMIs) at 50-keV, 70-keV, and 100-keV, iodine maps, and virtual non-contrast (VNC) images were reconstructed using an iterative reconstruction algorithm (QIR), a quantitative kernel (Qr40) and 0.6-mm/0.3-mm slice thickness/increment. Atherosclerotic plaques were segmented using semi-automatic software (Research Frontier, Siemens). Segmentation confirmation and risk stratification (low- vs high-risk) were performed by a board-certified cardiac radiologist. A total of 93 radiomic features were extracted from each image using PyRadiomics (v2.2.0b1). For each feature, a t-test was performed between low- and high-risk plaques (p<0.05 considered significant). Two significant and non-redundant features were input into a support vector machine (SVM). A leave-one-out cross-validation strategy was adopted and the classification accuracy was computed. Fifteen low-risk and ten high-risk plaques were identified by the radiologist. A total of 18, 32, 43, 16, and 55 out of 93 features in 50-keV, 70-keV, 100-keV, iodine map, and VNC images were statistically significant. A total of 17, 19, 22, 20, and 22 out of 25 plaques were classified correctly in 50-keV, 70-keV, 100-keV, iodine map, and VNC images, respectively. A ML model using 100-keV VMIs and VNC images derived from coronary PCD-CTA best automatically differentiated low- and high-risk coronary plaques." @default.
- W4362664457 created "2023-04-07" @default.
- W4362664457 creator A5008758029 @default.
- W4362664457 creator A5032222962 @default.
- W4362664457 creator A5033648312 @default.
- W4362664457 creator A5038240758 @default.
- W4362664457 creator A5042479940 @default.
- W4362664457 creator A5045794290 @default.
- W4362664457 creator A5047904907 @default.
- W4362664457 creator A5051084260 @default.
- W4362664457 creator A5051694808 @default.
- W4362664457 creator A5067668310 @default.
- W4362664457 date "2023-04-07" @default.
- W4362664457 modified "2023-09-27" @default.
- W4362664457 title "Classification of high-risk coronary plaques using radiomic analysis of multi-energy photon-counting-detector computed tomography (PCD-CT) images" @default.
- W4362664457 cites W2524707231 @default.
- W4362664457 cites W2767128594 @default.
- W4362664457 cites W2774994588 @default.
- W4362664457 cites W2889741596 @default.
- W4362664457 cites W2943602527 @default.
- W4362664457 cites W3105079555 @default.
- W4362664457 cites W3110147920 @default.
- W4362664457 cites W3129459249 @default.
- W4362664457 cites W4200039099 @default.
- W4362664457 cites W4281396868 @default.
- W4362664457 doi "https://doi.org/10.1117/12.2654412" @default.
- W4362664457 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37064414" @default.
- W4362664457 hasPublicationYear "2023" @default.
- W4362664457 type Work @default.
- W4362664457 citedByCount "0" @default.
- W4362664457 crossrefType "proceedings-article" @default.
- W4362664457 hasAuthorship W4362664457A5008758029 @default.
- W4362664457 hasAuthorship W4362664457A5032222962 @default.
- W4362664457 hasAuthorship W4362664457A5033648312 @default.
- W4362664457 hasAuthorship W4362664457A5038240758 @default.
- W4362664457 hasAuthorship W4362664457A5042479940 @default.
- W4362664457 hasAuthorship W4362664457A5045794290 @default.
- W4362664457 hasAuthorship W4362664457A5047904907 @default.
- W4362664457 hasAuthorship W4362664457A5051084260 @default.
- W4362664457 hasAuthorship W4362664457A5051694808 @default.
- W4362664457 hasAuthorship W4362664457A5067668310 @default.
- W4362664457 hasBestOaLocation W43626644572 @default.
- W4362664457 hasConcept C120665830 @default.
- W4362664457 hasConcept C121332964 @default.
- W4362664457 hasConcept C12267149 @default.
- W4362664457 hasConcept C154945302 @default.
- W4362664457 hasConcept C2989005 @default.
- W4362664457 hasConcept C41008148 @default.
- W4362664457 hasConcept C71924100 @default.
- W4362664457 hasConcept C89600930 @default.
- W4362664457 hasConcept C94915269 @default.
- W4362664457 hasConceptScore W4362664457C120665830 @default.
- W4362664457 hasConceptScore W4362664457C121332964 @default.
- W4362664457 hasConceptScore W4362664457C12267149 @default.
- W4362664457 hasConceptScore W4362664457C154945302 @default.
- W4362664457 hasConceptScore W4362664457C2989005 @default.
- W4362664457 hasConceptScore W4362664457C41008148 @default.
- W4362664457 hasConceptScore W4362664457C71924100 @default.
- W4362664457 hasConceptScore W4362664457C89600930 @default.
- W4362664457 hasConceptScore W4362664457C94915269 @default.
- W4362664457 hasLocation W43626644571 @default.
- W4362664457 hasLocation W43626644572 @default.
- W4362664457 hasLocation W43626644573 @default.
- W4362664457 hasOpenAccess W4362664457 @default.
- W4362664457 hasPrimaryLocation W43626644571 @default.
- W4362664457 hasRelatedWork W1999101396 @default.
- W4362664457 hasRelatedWork W2101819884 @default.
- W4362664457 hasRelatedWork W2109197306 @default.
- W4362664457 hasRelatedWork W2153189372 @default.
- W4362664457 hasRelatedWork W2355927362 @default.
- W4362664457 hasRelatedWork W2358941527 @default.
- W4362664457 hasRelatedWork W2748952813 @default.
- W4362664457 hasRelatedWork W2772879096 @default.
- W4362664457 hasRelatedWork W2899084033 @default.
- W4362664457 hasRelatedWork W2345184372 @default.
- W4362664457 isParatext "false" @default.
- W4362664457 isRetracted "false" @default.
- W4362664457 workType "article" @default.