ABSTRACT
Objective:To predict the dose of lumbosacral spine (LS) and pelvic bone marrow (PBM) based on kernel density estimation (KDE) in patients with gynecological tumors.Methods:Fifteen patients with gynecological tumors receiving radiotherapy plans with dose limitation for LS and PBM in our hospital were selected as training data for machine learning. Another 10 cases were selected as the data for model validation. The minimum directional distance between the dose point in the organs and the edge of the planned target volume for the LS and PBM was calculated. Model training was performed by KDE. The accuracy of the model prediction was evaluated by the root mean square error. The model was utilized to predict the actual planned doses of the LS and PBM, and a linear fitting was performed on the predicted dose volume histogram (DVH) and actual results. The prediction effect was assessed by the goodness of fit R 2. Results:In terms of the DVH parameters required by the planner, the prediction doses from the model were similar to those of the verification plans: the difference of PBM V 40Gy was 2.0%, the difference of the mean dose was 1.6 Gy, and the difference of LS V 10Gy was -0.4%. In the unrequired DVH parameters, except for the PBM V 10Gy, the predicted values of the model were significantly high. The difference between the DVH predicted by the model and the actual plan was small, and the R 2 of the LS and PBM were 0.988 and 0.995, respectively. Conclusions:The model based on KDE method can accurately predict the doses of the LS and PBM. This model can also be used as a method to ensure the quality of the plan, and improve the consistency and quality of the plan.
ABSTRACT
ObjectiveTo compare and analyze conventional plan and inverse optimized plan in dosemetric of cervical cancer. MethodTwenty cases of cervical cancer treated with combination radical radiotherapy of EBT were selected,every case had two plans: one was conventional plan based A point prescription dose, the other was inverse optimized plan (IPSA, inverse planning with simulated annealing)based volume object dose.ResultsIPSA plans provided better values compared with the conventional plans in 90％ prescription dose volume V90[ (94 ± 15 )％ vs. (60 ± 17 )％], 100％ prescription dose volume V100[(90 ± 18)％ vs. (56 ± 14)％]and 100％ treatment volume dose D100[(54 ± 10)％ vs. (29 ±9)％](P ＜0.05),respectively. Meanwhile the organ at risk received lower dose volume. ConclusionsPlans generated using IPSA provide higher dose to the target volume but with lower dose to normal structure and less time. This study can help to guide the clinical application.