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Multivariate statistical modelling to improve particle treatment verification: Implications for prompt gamma-ray timing

Schellhammer, S.; Wiedkamp, J.; Löck, S.; Kögler, T.


We present an improved method for in-vivo proton range verification by prompt gamma-ray timing based on multivariate statistical modelling.

To this end, prompt gamma-ray timing distributions acquired during pencil beam irradiation of an acrylic glass phantom with air cavities of different thicknesses were analysed. Relevant histogram features were chosen using forward variable selection and the Least Absolute Shrinkage and Selection Operator (LASSO) from a feature assortment based on recommendations of the Image Biomarker Standardisation Initiative. Candidate models were defined by multivariate linear regression and evaluated based on their coefficient of determination \(R^2\) and root mean square error \(RMSE\).

The newly developed models showed a clearly improved predictive power (\(R^2 > 0.7\)) compared to the previously used models (\(R^2 < 0.5\)) and allowed for the identification of introduced air cavities in a scanned treatment field. %The parameter selection showed better predictive power of the energy-specific models (RM SE < 1,8 mm) compared to the energy-independent models (RM SE > 3 mm).
%for counting statistics equivalent to a single spot measured with eight detector units.

These results demonstrate that elaborate statistical models can enhance prompt gamma ray based treatment verification and increase its potential for routine clinical application.

Keywords: proton therapy; treatment verification; prompt gamma-ray timing; machine learning; multivariate modelling


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