期刊
PHYSICS IN MEDICINE AND BIOLOGY
卷 58, 期 13, 页码 4563-4577出版社
IOP PUBLISHING LTD
DOI: 10.1088/0031-9155/58/13/4563
关键词
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资金
- European collaboration Envision [241851]
- PRRH (Pogramme Regional de Recherche en Hadrontherapie) from the 'region Rhone-Alpes'
- Labex PRIMES (French ANR)
- France Hadron research program
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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