A personalized DVH prediction model for HDR brachytherapy in cervical cancer treatment

PurposeAlthough the knowledge-based dose-volume histogram (DVH) prediction has been largely researched and applied in External Beam Radiation Therapy, it is still less investigated in the domain of brachytherapy. The purpose of this study is to develop a reliable DVH prediction method for high-dose-rate brachytherapy plans. MethodA DVH prediction workflow combining kernel density estimation (KDE), k-nearest neighbor (kNN), and principal component analysis (PCA) was proposed. PCA and kNN were first employed together to select similar patients based on principal component directions. 79 cervical cancer patients with different applicators inserted was included in this study. The KDE model was built based on the relationship between distance-to-target (DTH) and the dose in selected cases, which can be subsequently used to estimate the dose probability distribution in the validation set. Model performance of bladder and rectum was quantified by |Delta D-2cc|, |Delta D-1cc|, |Delta D-0.1cc|, |Delta D-max|, and |Delta D-mean| in the form of mean and standard deviation. The model performance between KDE only and the combination of kNN, PCA, and KDE was compared. Result20, 30 patients were selected for rectum and bladder based on KNN and PCA, respectively. The absolute residual between the actual plans and the predicted plans were 0.38 +/- 0.29, 0.4 +/- 0.32, 0.43 +/- 0.36, 0.97 +/- 0.66, and 0.13 +/- 0.99 for |Delta D-2cc|, |Delta D-1cc|, |Delta D-0.1cc|, |Delta D-max|, and |Delta D-mean| in the bladder, respectively. For rectum, the corresponding results were 0.34 +/- 0.27, 0.38 +/- 0.33, 0.63 +/- 0.57, 1.41 +/- 0.99 and 0.23 +/- 0.17, respectively. The combination of kNN, PCA, and KDE showed a significantly better prediction performance than KDE only, with an improvement of 30.3% for the bladder and 33.3% for the rectum. ConclusionIn this study, a knowledge-based machine learning model was proposed and verified to accurately predict the DVH for new patients. This model is proved to be effective in our testing group in the workflow of HDR brachytherapy.

Automated summary

This study aimed to develop a reliable DVH prediction method for high-dose-rate brachytherapy plans using a workflow combining kernel density estimation (KDE), k-nearest neighbor (kNN), and principal component analysis (PCA). The combination of kNN, PCA, and KDE showed significantly better prediction performance than using KDE alone, improving by 30.3% for bladder and 33.3% for rectum in terms of model performance.