On the use of machine learning methods for mPSD calibration in HDR brachytherapy

We sought to evaluate the feasibility of using machine learning (ML) algorithms for multipoint plastic scintillator detector (mPSD) calibration in high-dose-rate (HDR) brachytherapy. Dose measurements were conducted under HDR brachytherapy conditions. The dosimetry system consisted of an optimized 1-mm-core mPSD and a compact assembly of photomultiplier tubes coupled with dichroic mirrors and filters. An ¹⁹²Ir source was remotely controlled and sent to various positions in a homemade PMMA holder, ensuring 0.1-mm positional accuracy. Dose measurements covering a range of 0.5 to 12 cm of source displacement were carried out according to TG-43 U1 recommendations. Individual scintillator doses were decoupled using a linear regression model, a random forest estimator, and artificial neural network algorithms. The dose predicted by the TG-43U1 formalism was used as the reference for system calibration and ML algorithm training. The performance of the different algorithms was evaluated using different sample sizes and distances to the source for the mPSD system calibration. We found that the calibration conditions influenced the accuracy in predicting the measured dose. The decoupling methods' deviations from the expected TG-43 U1 dose generally remained below 20%. However, the dose prediction with the three algorithms was accurate to within 7% relative to the dose predicted by the TG-43 U1 formalism when measurements were performed in the same range of distances used for calibration. In such cases, the predictions with random forest exhibited minimal deviations (<2%). However, the performance random forest was compromised when the predictions were done beyond the range of distances used for calibration. Because the linear regression algorithm can extrapolate the data, the dose prediction by the linear regression was less influenced by the calibration conditions than random forest. The linear regression algorithm's behavior along the distances to the source was smoother than those for the random forest and neural network algorithms, but the observed deviations were more significant than those for the neural network and random forest algorithms. The number of available measurements for training purposes influenced the random forest and neural network models the most. Their accuracy tended to converge toward deviation values close to 1% from a number of dwell positions greater than 100. In performing HDR brachytherapy dose measurements with an optimized mPSD system, ML algorithms are good alternatives for precise dose reporting and treatment assessment during this kind of cancer treatment.