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      Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy.

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          Abstract

          Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.

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          Author and article information

          Journal
          Phys Med Biol
          Physics in medicine and biology
          1361-6560
          0031-9155
          Jul 7 2013
          : 58
          : 13
          Affiliations
          [1 ] Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA, F-69622 Lyon, France.
          Article
          10.1088/0031-9155/58/13/4563
          23771015
          856039ab-2345-4149-8bc4-4e2e25546fd9
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