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Use of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy
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10.1118/1.3582947
/content/aapm/journal/medphys/38/6/10.1118/1.3582947
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/6/10.1118/1.3582947

Figures

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FIG. 1.

ANNs performance during the best training epoch in large scale GA optimization.

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FIG. 2.

Scheme of the best net architecture after >26 cycles of GA optimization.

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FIG. 3.

Performance during training the epochs for the selected network. The 21th epoch resulted as the best one.

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FIG. 4.

ROC curve after blind simulation with Db2 using large scale algorithm.

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FIG. 5.

ROC curve after blind simulation with Db2 using SVM.

Tables

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TABLE I.

Clinical data.

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TABLE II.

Radiation induced acute toxicity RTOG/EORTC classification criteria (Ref. 24).

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TABLE III.

List of the parameters used as ANN inputs.

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TABLE IV.

Distribution of grade 0 and 1 in GI, GU and overall toxicity analysis (n = 321 patients).

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TABLE V.

Summary of candidate ANN architectures. [a,b,c] refers to ANNs with only one hidden layer. [d,e,f] refers to ANNs with two hidden layers.

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TABLE VI.

Threshold based classification for the ROC curve of the ANN selected by large scale optimization.

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TABLE VII.

Overall accuracy after blind simulation.

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/content/aapm/journal/medphys/38/6/10.1118/1.3582947
2011-05-20
2014-04-16
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Use of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/6/10.1118/1.3582947
10.1118/1.3582947
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