Prediction of dose-volume histograms in nasopharyngeal cancer IMRT using geometric and dosimetric information

Abstract

A method using both patient geometric and dosimetric information was proposed to predict dosevolume histograms (DVHs) of organs at risk (OARs) for a nasopharyngeal cancer (NPC) intensitymodulated radiation therapy (IMRT) plan.A total of 106 nine-field IMRT NPC plans were used in this study. Twenty-six plans were randomly selected as testing cases, and the remaining plans were used as the training data. A method employing geometric and dosimetric information was developed for OAR DVH prediction. The dosimetric information was derived from an initial dose calculation using a simple unoptimized conformal plan. The DVHs were also predicted using only the geometric information. The DVH prediction model was a generalized regression neural network (GRNN). Mean absolute error (MAE) and R 2 values were introduced to evaluate DVH prediction accuracy.Significant differences in the DVH prediction accuracy were found between the method employing the geometric and dosimetric information and the method utilizing the geometric information for the brainstem (R 2 , 0.98 versus 0.95, p = 0.007; MAE, 3.52% versus 7.19%, p = 0.002), spinal cord (R 2 , 0.98 versus 0.96, p < 0.001; MAE, 2.80% versus 4.36%, p < 0.001), left optic nerve (R 2 , 0.90 versus 0.77, p = 0.014; MAE, 3.07% versus 11.29%, p = 0.025) and other organs. On average, the R 2 value increased by ~6.7% and the MAE value decreased by ~46.7% after adding the dosimetric information to the DVH prediction.We developed a method for predicting DVHs of OARs in NPC IMRT plans by using geometric and dosimetric information. Adding dosimetric information can help predict the DVHs of OARs in NPC IMRT plans. NOTE RECEIVED

Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning

“…As shown in Fig. 1 , the dose fall-off rate information of each individual field could be clearly presented, which differs from previous reports on the small dose fall-off rate of the PTV boundary regions after interfield dose superposition 26 .

Figure 1 The dose distributions of the 9 individual fields with uniform-intensity irradiation at the same CT slice of a nasopharyngeal carcinoma patient (left) and the IDVHs of a highlighted structure (right).

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“…As shown in Fig. 1, the dose fall-off rate information of each individual field could be clearly presented, which differs from previous reports on the small dose fall-off rate of the PTV boundary regions after interfield dose superposition 26 .…”

“…The dosimetric information from individual fields with uniform-intensity radiation, termed the individual dose-volume histograms (IDVHs), was used to predict the OAR DVH and PTV DVH. Another method applied the DTH and the conformal plan dose-volume histogram (CPDVH) to predict the OAR DVH, which is referred to as the DTH + CPDVH method 26 . Table 2 shows the input features of the OAR and PTV used in this work.…”

Probabilistic feature extraction, dose statistic prediction and dose mimicking for automated radiation therapy treatment planning

“…Regarding spatial dose prediction, two-or three-dimensional convolutional neural networks in various architectures, such as U-nets, have been widely used [9][10][11][12][13] and extended to generative models such as generative adversarial networks [14,15], although other methods such as random forests have also been studied [16]. For prediction of DVHs or other dose statistics, while overlap volume histograms evaluated on the input image have been traditionally used for this purpose [17][18][19][20][21][22][23][24], more recent literature also includes the use of neural networkbased methods to simultaneously predict spatial dose and DVHs directly from input images [25,26]. Common for all of the aforementioned approaches, however, is that predictions are made deterministically with no associated predictive probability distribution.…”

Support Vector Machine Model Predicts Dose for Organs at Risk in High-Dose Rate Brachytherapy of Cervical Cancer

“…Although there is a correlation between the dose to the OARs and their volumes, information to predict the dose to the OARs is limited. In recent years, methods for predicting the dose to the OARs have been widely introduced in external irradiation intensity-modulated radiotherapy (6)(7)(8)(9)(10). These approaches typically use libraries of existing patient plans to create models that predict the extent of OAR sparing that can be achieved in a new patient based on, for example, the planning target volume (PTV)-OAR distance and overlap (11).…”