Automated determination of the ion-recombination correction factor (k) in ultra-high dose rate electron radiation therapy.

Background: Plane-parallel ionization chambers are the recommended secondary standard systems for clinical reference dosimetry of electrons. Dosimetry in high dose rate and dose-per-pulse (DPP) is challenging as ionization chambers are subject to ion recombination, especially when dose rate and/or DPP is increased beyond the range of conventional radiotherapy. The lack of universally accepted models for correction of ion recombination in UDHR is still an issue as it is, especially in FLASH-RT research, which is crucial in order to be able to accurately measure the dose for a wide range of dose rates and DPPs.

Purpose: The objective of this study was to show the feasibility of developing an Artificial Intelligence model to predict the ion-recombination factor-k for a plane-parallel Advanced Markus ionization chamber for conventional and ultra-high dose rate electron beams based on machine parameters. In addition, the predicted k of the AI model was compared with the current applied analytical models for this correction factor.

Methods: A total number of 425 measurements was collected with a balanced variety in machine parameter settings. The specific k values were determined by dividing the output of the reference dosimeter (optically stimulated luminescence [OSL]) by the output of the AM chamber. Subsequently, a XGBoost regression model was trained, which used the different machine parameters as input features and the corresponding k value as output. The prediction accuracy of this regression model was characterized by R-coefficient of determination, mean absolute error and root mean squared error. In addition, the model was compared with the Two-Voltage (TVA) method and empirical Petersson model for 19 different dose-per-pulse values ranging from conventional to UDHR regimes. The Akiake Information criterion (AIC) was calculated for the three different models.

Results: The XGBoost regression model reached a R-score of 0.94 on the independent test set with a MAE of 0.067 and RMSE of 0.106. For the additional 19 random data points, the k values predicted by the XGBoost model showed to be in agreement, within the uncertainties, with the ones determined by the Petersson model and better than the TVA method for doses per pulse >3.5 Gy with a maximum deviation from the ground truth of 14.2%, 16.7%, and -36.0%, respectively, for DPP >4 Gy.

Conclusion: The proposed method of using AI for k determination displays efficiency. For the investigated DPPs, the k values obtained with the XGBoost model were in concurrence with the ones obtained with the current available analytical models within the boundaries of uncertainty, certainly for the DPP characterizing UDHR. But the overall performance of the AI model, taking the number of free parameters into account, lacked efficiency. Future research should optimize the determination of the experimental k, and investigate the determination the k for DPPs higher than the ones investigated in this study, while also evaluating the prediction of the proposed XGBoost model for UDHR machines of different centers.