VMAT Dose Prediction in Radiotherapy by Using Progressive Refinement UNet

2021 ◽  
Author(s):  
Jianyong Wang ◽  
Junjie Hu ◽  
Ying Song ◽  
Qiang Wang ◽  
Xiaozhi Zhang ◽  
...  
Keyword(s):  
Dose Prediction ◽  
Progressive Refinement

The Negative Impact of Biological Variation in the Effect and Clearance of Warfarin on Methods for Prediction of Dose Requirements

1986 ◽  
Vol 56 (03) ◽  
pp. 371-375 ◽  
Author(s):  
Peretz Weiss ◽  
Hillel Halkin ◽  
Shlomo Almog
Keyword(s):  
Individual Variation ◽  
Negative Impact ◽  
Warfarin Dose ◽  
Empirical Therapy ◽  
Maintenance Phase ◽  
Individual Variability ◽  
Dose Prediction ◽  
Group I ◽  
Over Time ◽  
Variance Explained

SummaryWithin-individual variation over time in the clearance (Cl) and effect (PT%) of warfarin, was measured in 25 inpatients (group I) studied after standard single or individualized split loading doses and 1-3 times (n = 16) 8-16 weeks later during maintenance. Mean Cl (2.5 α 0.9 ml/min) was similar in both phases but significant changes occurred in 6/16 patients, exceeding those expected from within-individual variation alone (defined by its 95% tolerance limits -24% to +62%). Initial PT% (21 α 5) was unaffected by dosing schedule, total or free plasma warfarin, varying between patients by only 18-24%. Mean initial and maintenance dose-PT% ratios (8.2 mg/d: 21% and 4.1 mg/d: 40%) were similar but significant changes in sensitivity to warfarin occurred in 4/16 patients. In group I and 64 other outpatients on maintenance therapy, between-individual variability was 36-52% for Cl and 49-56% for effect. PT% correlated best (r = 0.56) with free and total plasma warfarin but poorly with dose (r = 0.29), with only 30% of PT% variance explained at best, due to high between patient variability.Warfarin dose prediction whether based on extrapolation from initial effects to the maintenance phase, or on iterative methods not allowing for between- or within-patient variation in warfarin clearance or effect which may occur independently over time, have not improved on empirical therapy. This, due to the elements of biological variability as well as the intricacy of the warfarin - prothrombin complex interaction not captured by any kinetic-dynamic model used for prediction to date.

Pharmacogenetic algorithm for predicting daily dose of warfarin in Caucasian patients of Czech origin

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Aleš Tomek ◽  
Tereza Růžičková ◽  
Vojtěch Kaplan ◽  
Zuzana Lacinová ◽  
Simona Kumstýřová ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Warfarin Dose ◽  
Therapeutic Index ◽  
Coefficient Of Determination ◽  
Dose Prediction ◽  
Pharmacogenetic Algorithm ◽  
Daily Dose ◽  
Caucasian Patients ◽  
Variant Alleles ◽  
Initiation Of Therapy

Abstract Objectives Warfarin use is limited by a low therapeutic index and significant interindividual variability of the daily dose. The most important factor predicting daily warfarin dose is individual genotype, polymorphisms of genes CYP2C9 (warfarin metabolism) and VKORC1 (sensitivity for warfarin). Algorithms using clinical and genetic variables could predict the daily dose before the initiation of therapy. The aim of this study was to develop and validate an algorithm for the prediction of warfarin daily dose in Czech patients. Methods Detailed clinical data of patients with known and stable warfarin daily dose were collected. All patients were genotyped for polymorphisms in genes CYP2C9 and VKORC1. Results Included patients were divided into derivation (n=175) and validation (n=223) cohorts. The final algorithm includes the following variables: Age, height, weight, treatment with amiodarone and presence of variant alleles of genes CYP2C9 and VKORC1. The adjusted coefficient of determination is 72.4% in the derivation and 62.3% in the validation cohort (p<0.001). Conclusions Our validated algorithm for warfarin daily dose prediction in our Czech cohort had higher precision than other currently published algorithms. Pharmacogenetics of warfarin has the potential in the clinical practice in specialized centers.

scholarly journals Erratum to: Warfarin dose prediction in children using pharmacometric bridging – comparison with published pharmacogenetic dosing algorithms

2013 ◽  
Vol 69 (9) ◽  
pp. 1737-1737
Author(s):  
Anna-Karin Hamberg ◽  
Lena E. Friberg ◽  
Katarina Hanséus ◽  
Britt-Marie Ekman-Joelsson ◽  
Jan Sunnegårdh ◽  
...  
Keyword(s):  
Warfarin Dose ◽  
Dose Prediction

scholarly journals EP-1510: Cumulated dose prediction

2015 ◽  
Vol 115 ◽  
pp. S822-S823
Author(s):  
M. Gizynska ◽  
D. Blatkiewicz ◽  
B. Czyzew ◽  
M. Galecki ◽  
M. Gil-Ulkowska ◽  
...  
Keyword(s):  
Dose Prediction

PH-0287: Transfer learning and Deep Neural Network for lung and heart dose prediction in breast treatments

2020 ◽  
Vol 152 ◽  
pp. S146-S147
Author(s):  
J. Perez-Alija ◽  
P. Gallego ◽  
M. Lizondo ◽  
J. Nuria ◽  
A. Latorre-Musoll ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Deep Neural Network ◽  
Heart Dose ◽  
Dose Prediction

Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110381
Author(s):  
Xue Bai ◽  
Ze Liu ◽  
Jie Zhang ◽  
Shengye Wang ◽  
Qing Hou ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Region Of Interest ◽  
Dice Similarity Coefficient ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Dose Distributions ◽  
Convolutional Networks ◽  
Dose Prediction ◽  
Fully Convolutional Networks ◽  
Percentage Difference

Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk ( p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model ( p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs.

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