FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3187702971> ?p ?o ?g. }

• W3187702971 endingPage "101" @default.
• W3187702971 startingPage "96" @default.
• W3187702971 abstract "Reducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased.Auto-segmentation was based on purpose-built DL, and automated planning used our in-house system, ECHO. Treatment plans for ten HNC patients, treated with 2 Gy × 35 fractions, were optimized (ECHO0). Six manually segmented OARs were replaced with DL auto-segmentations and the plans re-optimized (ECHO1). In a third set of plans, mean doses for auto-segmented ipsilateral masseter and medial pterygoid (MIMean, MPIMean), derived from a trismus risk model, were implemented as dose-volume objectives (ECHO2). Clinical dose-volume criteria were compared between the two scenarios (ECHO0vs. ECHO1; ECHO1vs. ECHO2; Wilcoxon signed-rank test; significance: p < 0.01).Small systematic differences were observed between the doses to the six auto-segmented OARs and their manual counterparts (median: ECHO1 = 6.2 (range: 0.4, 21) Gy vs. ECHO0 = 6.6 (range: 0.3, 22) Gy; p = 0.007), and the ECHO1 plans provided improved normal tissue sparing across a larger dose-volume range. Only in the ECHO2 plans, all patients fulfilled both MIMean and MPIMean criteria. The population median MIMean and MPIMean were considerably lower than those suggested by the trismus model (ECHO0: MIMean = 13 Gy vs. ≤42 Gy; MPIMean = 29 Gy vs. ≤68 Gy).Automated treatment planning can efficiently incorporate new structures from DL auto-segmentation, which results in trismus risk sparing without deteriorating treatment plan quality. Auto-planning and deep learning auto-segmentation together provide a powerful platform to further improve treatment planning." @default.
• W3187702971 created "2021-08-16" @default.
• W3187702971 creator A5014597008 @default.
• W3187702971 creator A5020546494 @default.
• W3187702971 creator A5026159944 @default.
• W3187702971 creator A5026507245 @default.
• W3187702971 creator A5035407370 @default.
• W3187702971 creator A5042683392 @default.
• W3187702971 creator A5043619532 @default.
• W3187702971 creator A5043964591 @default.
• W3187702971 creator A5058225724 @default.
• W3187702971 creator A5061457064 @default.
• W3187702971 creator A5075003661 @default.
• W3187702971 creator A5075085778 @default.
• W3187702971 creator A5078382635 @default.
• W3187702971 creator A5085580649 @default.
• W3187702971 creator A5088189613 @default.
• W3187702971 creator A5090188819 @default.
• W3187702971 date "2021-07-01" @default.
• W3187702971 modified "2023-10-01" @default.
• W3187702971 title "Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy" @default.
• W3187702971 cites W2016427306 @default.
• W3187702971 cites W2061629820 @default.
• W3187702971 cites W2082217364 @default.
• W3187702971 cites W2130808635 @default.
• W3187702971 cites W2136716437 @default.
• W3187702971 cites W2142800221 @default.
• W3187702971 cites W2613409207 @default.
• W3187702971 cites W2807278326 @default.
• W3187702971 cites W2910347321 @default.
• W3187702971 cites W2920291762 @default.
• W3187702971 cites W2922812404 @default.
• W3187702971 cites W2943982309 @default.
• W3187702971 cites W2982648196 @default.
• W3187702971 cites W2992550731 @default.
• W3187702971 cites W2997271239 @default.
• W3187702971 cites W3022763969 @default.
• W3187702971 cites W3092818728 @default.
• W3187702971 cites W3098165599 @default.
• W3187702971 cites W3110335203 @default.
• W3187702971 doi "https://doi.org/10.1016/j.phro.2021.07.009" @default.
• W3187702971 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8552336" @default.
• W3187702971 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34746452" @default.
• W3187702971 hasPublicationYear "2021" @default.
• W3187702971 type Work @default.
• W3187702971 sameAs 3187702971 @default.
• W3187702971 citedByCount "9" @default.
• W3187702971 countsByYear W31877029712022 @default.
• W3187702971 countsByYear W31877029712023 @default.
• W3187702971 crossrefType "journal-article" @default.
• W3187702971 hasAuthorship W3187702971A5014597008 @default.
• W3187702971 hasAuthorship W3187702971A5020546494 @default.
• W3187702971 hasAuthorship W3187702971A5026159944 @default.
• W3187702971 hasAuthorship W3187702971A5026507245 @default.
• W3187702971 hasAuthorship W3187702971A5035407370 @default.
• W3187702971 hasAuthorship W3187702971A5042683392 @default.
• W3187702971 hasAuthorship W3187702971A5043619532 @default.
• W3187702971 hasAuthorship W3187702971A5043964591 @default.
• W3187702971 hasAuthorship W3187702971A5058225724 @default.
• W3187702971 hasAuthorship W3187702971A5061457064 @default.
• W3187702971 hasAuthorship W3187702971A5075003661 @default.
• W3187702971 hasAuthorship W3187702971A5075085778 @default.
• W3187702971 hasAuthorship W3187702971A5078382635 @default.
• W3187702971 hasAuthorship W3187702971A5085580649 @default.
• W3187702971 hasAuthorship W3187702971A5088189613 @default.
• W3187702971 hasAuthorship W3187702971A5090188819 @default.
• W3187702971 hasBestOaLocation W31877029711 @default.
• W3187702971 hasConcept C126322002 @default.
• W3187702971 hasConcept C126838900 @default.
• W3187702971 hasConcept C12868164 @default.
• W3187702971 hasConcept C154945302 @default.
• W3187702971 hasConcept C193206974 @default.
• W3187702971 hasConcept C199343813 @default.
• W3187702971 hasConcept C201645570 @default.
• W3187702971 hasConcept C206041023 @default.
• W3187702971 hasConcept C2776530083 @default.
• W3187702971 hasConcept C2778838824 @default.
• W3187702971 hasConcept C2908647359 @default.
• W3187702971 hasConcept C2989005 @default.
• W3187702971 hasConcept C41008148 @default.
• W3187702971 hasConcept C509974204 @default.
• W3187702971 hasConcept C71924100 @default.
• W3187702971 hasConcept C89600930 @default.
• W3187702971 hasConcept C99454951 @default.
• W3187702971 hasConceptScore W3187702971C126322002 @default.
• W3187702971 hasConceptScore W3187702971C126838900 @default.
• W3187702971 hasConceptScore W3187702971C12868164 @default.
• W3187702971 hasConceptScore W3187702971C154945302 @default.
• W3187702971 hasConceptScore W3187702971C193206974 @default.
• W3187702971 hasConceptScore W3187702971C199343813 @default.
• W3187702971 hasConceptScore W3187702971C201645570 @default.
• W3187702971 hasConceptScore W3187702971C206041023 @default.
• W3187702971 hasConceptScore W3187702971C2776530083 @default.
• W3187702971 hasConceptScore W3187702971C2778838824 @default.
• W3187702971 hasConceptScore W3187702971C2908647359 @default.
• W3187702971 hasConceptScore W3187702971C2989005 @default.
• W3187702971 hasConceptScore W3187702971C41008148 @default.
• W3187702971 hasConceptScore W3187702971C509974204 @default.

• next