FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2922384037> ?p ?o ?g. }

• W2922384037 endingPage "114" @default.
• W2922384037 startingPage "105" @default.
• W2922384037 abstract "To develop and evaluate the feasibility of deep learning approaches for MR-based treatment planning (deepMTP) in brain tumor radiation therapy.A treatment planning pipeline was constructed using a deep learning approach to generate continuously valued pseudo CT images from MR images. A deep convolutional neural network was designed to identify tissue features in volumetric head MR images training with co-registered kVCT images. A set of 40 retrospective 3D T1-weighted head images was utilized to train the model, and evaluated in 10 clinical cases with brain metastases by comparing treatment plans using deep learning generated pseudo CT and using an acquired planning kVCT. Paired-sample Wilcoxon signed rank sum tests were used for statistical analysis to compare dosimetric parameters of plans made with pseudo CT images generated from deepMTP to those made with kVCT-based clinical treatment plan (CTTP).deepMTP provides an accurate pseudo CT with Dice coefficients for air: 0.95 ± 0.01, soft tissue: 0.94 ± 0.02, and bone: 0.85 ± 0.02 and a mean absolute error of 75 ± 23 HU compared with acquired kVCTs. The absolute percentage differences of dosimetric parameters between deepMTP and CTTP was 0.24% ± 0.46% for planning target volume (PTV) volume, 1.39% ± 1.31% for maximum dose and 0.27% ± 0.79% for the PTV receiving 95% of the prescribed dose (V95). Furthermore, no significant difference was found for PTV volume (P = 0.50), the maximum dose (P = 0.83) and V95 (P = 0.19) between deepMTP and CTTP.We have developed an automated approach (deepMTP) that allows generation of a continuously valued pseudo CT from a single high-resolution 3D MR image and evaluated it in partial brain tumor treatment planning. The deepMTP provided dose distribution with no significant difference relative to a kVCT-based standard volumetric modulated arc therapy plans." @default.
• W2922384037 created "2019-03-22" @default.
• W2922384037 creator A5041234276 @default.
• W2922384037 creator A5054461115 @default.
• W2922384037 creator A5074710777 @default.
• W2922384037 creator A5080493254 @default.
• W2922384037 date "2019-03-01" @default.
• W2922384037 modified "2023-10-06" @default.
• W2922384037 title "<scp>MR</scp>‐based treatment planning in radiation therapy using a deep learning approach" @default.
• W2922384037 cites W1497983146 @default.
• W2922384037 cites W1594118958 @default.
• W2922384037 cites W1677182931 @default.
• W2922384037 cites W1859655790 @default.
• W2922384037 cites W1901129140 @default.
• W2922384037 cites W1965726483 @default.
• W2922384037 cites W1969750491 @default.
• W2922384037 cites W1974481022 @default.
• W2922384037 cites W1977661786 @default.
• W2922384037 cites W1979905751 @default.
• W2922384037 cites W1985180563 @default.
• W2922384037 cites W1985964670 @default.
• W2922384037 cites W2002129123 @default.
• W2922384037 cites W2003933915 @default.
• W2922384037 cites W2005508273 @default.
• W2922384037 cites W2014510250 @default.
• W2922384037 cites W2021177063 @default.
• W2922384037 cites W2021573820 @default.
• W2922384037 cites W2040357326 @default.
• W2922384037 cites W2069872397 @default.
• W2922384037 cites W2074170895 @default.
• W2922384037 cites W2080858163 @default.
• W2922384037 cites W2083848232 @default.
• W2922384037 cites W2086284908 @default.
• W2922384037 cites W2087726204 @default.
• W2922384037 cites W2089221057 @default.
• W2922384037 cites W2094768870 @default.
• W2922384037 cites W2096305085 @default.
• W2922384037 cites W2127961656 @default.
• W2922384037 cites W2131103601 @default.
• W2922384037 cites W2131611242 @default.
• W2922384037 cites W2133287637 @default.
• W2922384037 cites W2155235045 @default.
• W2922384037 cites W2157025461 @default.
• W2922384037 cites W2167157872 @default.
• W2922384037 cites W2172015533 @default.
• W2922384037 cites W2194775991 @default.
• W2922384037 cites W2199097904 @default.
• W2922384037 cites W2246298096 @default.
• W2922384037 cites W2253429366 @default.
• W2922384037 cites W2267700533 @default.
• W2922384037 cites W2298007812 @default.
• W2922384037 cites W2346062110 @default.
• W2922384037 cites W2413430220 @default.
• W2922384037 cites W2578835425 @default.
• W2922384037 cites W2580163862 @default.
• W2922384037 cites W2592929672 @default.
• W2922384037 cites W2737373222 @default.
• W2922384037 cites W2739527645 @default.
• W2922384037 cites W2754132686 @default.
• W2922384037 cites W2765246041 @default.
• W2922384037 cites W2765429622 @default.
• W2922384037 cites W2766261245 @default.
• W2922384037 cites W2794613614 @default.
• W2922384037 cites W2803328900 @default.
• W2922384037 cites W2804411736 @default.
• W2922384037 cites W2902404424 @default.
• W2922384037 cites W2963881378 @default.
• W2922384037 cites W3024826984 @default.
• W2922384037 cites W3101123465 @default.
• W2922384037 cites W4242745054 @default.
• W2922384037 doi "https://doi.org/10.1002/acm2.12554" @default.
• W2922384037 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6414148" @default.
• W2922384037 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30861275" @default.
• W2922384037 hasPublicationYear "2019" @default.
• W2922384037 type Work @default.
• W2922384037 sameAs 2922384037 @default.
• W2922384037 citedByCount "48" @default.
• W2922384037 countsByYear W29223840372019 @default.
• W2922384037 countsByYear W29223840372020 @default.
• W2922384037 countsByYear W29223840372021 @default.
• W2922384037 countsByYear W29223840372022 @default.
• W2922384037 countsByYear W29223840372023 @default.
• W2922384037 crossrefType "journal-article" @default.
• W2922384037 hasAuthorship W2922384037A5041234276 @default.
• W2922384037 hasAuthorship W2922384037A5054461115 @default.
• W2922384037 hasAuthorship W2922384037A5074710777 @default.
• W2922384037 hasAuthorship W2922384037A5080493254 @default.
• W2922384037 hasBestOaLocation W29223840371 @default.
• W2922384037 hasConcept C126322002 @default.
• W2922384037 hasConcept C126838900 @default.
• W2922384037 hasConcept C12868164 @default.
• W2922384037 hasConcept C154945302 @default.
• W2922384037 hasConcept C201645570 @default.
• W2922384037 hasConcept C206041023 @default.
• W2922384037 hasConcept C2989005 @default.
• W2922384037 hasConcept C41008148 @default.
• W2922384037 hasConcept C509974204 @default.
• W2922384037 hasConcept C71924100 @default.

• next