FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3208664874> ?p ?o ?g. }

• W3208664874 endingPage "e147" @default.
• W3208664874 startingPage "e146" @default.
• W3208664874 abstract "Purpose/Objective(s)As current radiotherapy (RT) treatment schedules are not personalized for individual patients, with the prescribed dose being uniform for particular subtypes and stages of cancer, despite highly variable responses between patients, we performed an in silico trial to determine optimal personalized RT dose for head and neck cancer patients in order to minimize excess dose with the objective of minimizing toxicity and improving QOL without sacrificing tumor control.Materials/MethodsWeekly tumor volume data were collected from two cancer centers for n = 39 head and neck cancer patients that received 66-70 Gy in 2-2.12 Gy daily fractions or with accelerated fractionation. Clinical outcome data, i.e., locoregional control (LRC) and disease-free survival (DFS), were also collected. Tumor volume reduction was connected to LRC by means of a volume reduction threshold associated with LRC. Due to limited patient numbers, the in silico trial was performed in an iterative leave-one-out fashion as follows: (1) a simple 2 parameter model of tumor growth and response to RT was calibrated to tumor volume data from 38 patients, (2) the calibrated model parameters were combined with the left-out patient's tumor volume data from weeks 1-4 of RT to simulate tumor volumes forward sampling from a patient-specific distribution of radiosensitivity parameters, (3) minimum cumulative radiation dose required for local tumor control was estimated by calculating the dose at which > 95% the simulated tumor volume trajectories were below the volume reduction threshold associated with LRC.ResultsThe optimal minimum radiation dose required for LRC was calculated for each patient. We found that 87% of the patients (34/39) received a higher total dose than estimated as necessary by our model with an average overdose of 37 Gy/patient, while the remaining patients were estimated to have received too little dose, with an average underdose of 47 Gy/patient. Notably, our results showed that the current one-size-fits-all approach results in no patient receiving their optimal RT dose.ConclusionHere we show a method to determine a personalized minimum RT dose to achieve LRC. Such simulations and estimates may allow radiation oncologists to identify candidates for dose de-escalation, dose escalation, or additional concurrent treatments, such as chemotherapy. As current radiotherapy (RT) treatment schedules are not personalized for individual patients, with the prescribed dose being uniform for particular subtypes and stages of cancer, despite highly variable responses between patients, we performed an in silico trial to determine optimal personalized RT dose for head and neck cancer patients in order to minimize excess dose with the objective of minimizing toxicity and improving QOL without sacrificing tumor control. Weekly tumor volume data were collected from two cancer centers for n = 39 head and neck cancer patients that received 66-70 Gy in 2-2.12 Gy daily fractions or with accelerated fractionation. Clinical outcome data, i.e., locoregional control (LRC) and disease-free survival (DFS), were also collected. Tumor volume reduction was connected to LRC by means of a volume reduction threshold associated with LRC. Due to limited patient numbers, the in silico trial was performed in an iterative leave-one-out fashion as follows: (1) a simple 2 parameter model of tumor growth and response to RT was calibrated to tumor volume data from 38 patients, (2) the calibrated model parameters were combined with the left-out patient's tumor volume data from weeks 1-4 of RT to simulate tumor volumes forward sampling from a patient-specific distribution of radiosensitivity parameters, (3) minimum cumulative radiation dose required for local tumor control was estimated by calculating the dose at which > 95% the simulated tumor volume trajectories were below the volume reduction threshold associated with LRC. The optimal minimum radiation dose required for LRC was calculated for each patient. We found that 87% of the patients (34/39) received a higher total dose than estimated as necessary by our model with an average overdose of 37 Gy/patient, while the remaining patients were estimated to have received too little dose, with an average underdose of 47 Gy/patient. Notably, our results showed that the current one-size-fits-all approach results in no patient receiving their optimal RT dose. Here we show a method to determine a personalized minimum RT dose to achieve LRC. Such simulations and estimates may allow radiation oncologists to identify candidates for dose de-escalation, dose escalation, or additional concurrent treatments, such as chemotherapy." @default.
• W3208664874 created "2021-11-08" @default.
• W3208664874 creator A5014796235 @default.
• W3208664874 creator A5027113415 @default.
• W3208664874 creator A5046046756 @default.
• W3208664874 creator A5065959120 @default.
• W3208664874 creator A5072668637 @default.
• W3208664874 creator A5089482166 @default.
• W3208664874 creator A5090769285 @default.
• W3208664874 date "2021-11-01" @default.
• W3208664874 modified "2023-10-16" @default.
• W3208664874 title "In Silico Trial to Estimate Personalized RT Dose in Head and Neck Cancer" @default.
• W3208664874 doi "https://doi.org/10.1016/j.ijrobp.2021.07.599" @default.
• W3208664874 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34700781" @default.
• W3208664874 hasPublicationYear "2021" @default.
• W3208664874 type Work @default.
• W3208664874 sameAs 3208664874 @default.
• W3208664874 citedByCount "0" @default.
• W3208664874 crossrefType "journal-article" @default.
• W3208664874 hasAuthorship W3208664874A5014796235 @default.
• W3208664874 hasAuthorship W3208664874A5027113415 @default.
• W3208664874 hasAuthorship W3208664874A5046046756 @default.
• W3208664874 hasAuthorship W3208664874A5065959120 @default.
• W3208664874 hasAuthorship W3208664874A5072668637 @default.
• W3208664874 hasAuthorship W3208664874A5089482166 @default.
• W3208664874 hasAuthorship W3208664874A5090769285 @default.
• W3208664874 hasBestOaLocation W32086648741 @default.
• W3208664874 hasConcept C121608353 @default.
• W3208664874 hasConcept C126322002 @default.
• W3208664874 hasConcept C143998085 @default.
• W3208664874 hasConcept C201645570 @default.
• W3208664874 hasConcept C2776530083 @default.
• W3208664874 hasConcept C2989005 @default.
• W3208664874 hasConcept C509974204 @default.
• W3208664874 hasConcept C71924100 @default.
• W3208664874 hasConceptScore W3208664874C121608353 @default.
• W3208664874 hasConceptScore W3208664874C126322002 @default.
• W3208664874 hasConceptScore W3208664874C143998085 @default.
• W3208664874 hasConceptScore W3208664874C201645570 @default.
• W3208664874 hasConceptScore W3208664874C2776530083 @default.
• W3208664874 hasConceptScore W3208664874C2989005 @default.
• W3208664874 hasConceptScore W3208664874C509974204 @default.
• W3208664874 hasConceptScore W3208664874C71924100 @default.
• W3208664874 hasIssue "3" @default.
• W3208664874 hasLocation W32086648741 @default.
• W3208664874 hasLocation W32086648742 @default.
• W3208664874 hasOpenAccess W3208664874 @default.
• W3208664874 hasPrimaryLocation W32086648741 @default.
• W3208664874 hasRelatedWork W1969094794 @default.
• W3208664874 hasRelatedWork W1974239774 @default.
• W3208664874 hasRelatedWork W1995788949 @default.
• W3208664874 hasRelatedWork W2086793060 @default.
• W3208664874 hasRelatedWork W2153654819 @default.
• W3208664874 hasRelatedWork W2477137756 @default.
• W3208664874 hasRelatedWork W2577212349 @default.
• W3208664874 hasRelatedWork W2918829915 @default.
• W3208664874 hasRelatedWork W3214269845 @default.
• W3208664874 hasRelatedWork W935391112 @default.
• W3208664874 hasVolume "111" @default.
• W3208664874 isParatext "false" @default.
• W3208664874 isRetracted "false" @default.
• W3208664874 magId "3208664874" @default.
• W3208664874 workType "article" @default.