Comparison of 2 methods for prediction of liver dosimetric indices in hepatocellular cancer IMRT planning

Abstract

To evaluate the performance of 2 methods predicting liver dosimetric indices for hepatocellular cancer patients treated with intensity modulated radiation therapy (IMRT). Two predicting methods were implemented to build correlation between geometric and dosimetric information of hepatocellular cancer IMRT plans. One method used Principle Component Analysis method to simplify information and Support Vector Machine (SVM) regression method to build correlation. The other method used a simple linear function to map volumes of certain regions to dosimetic indices. Thirty eight hepatocellular cancer IMRT plans were randomly selected to train the 2 methods. The effectiveness of the 2 methods was validated using another 8 plans. Liver dosimetric indices V10, V20, V30, and mean dose of the 8 plans in validation cohort were calculated using the predicting methods. The predicted indices were compared with the indices derived from the clinically accepted treatment plans. The absolute differences of V10 calculated with the SVM method and the actual values of treatment plans had a mean value of 7.87%. And the values for V20, V30, and mean dose were 4.37%, 4.41%, and 4.20%, respectively. The absolute differences of V10 calculated with the linear formulation method and the actual values of treatment plans had a mean value of 6.09%. And the values for V20, V30, and mean dose were 5.28%, 5.05%, and 5.92%, respectively. These 2 methods have similar accuracy in predicting dosimetric indices V10, V20, V30 of livers. The predicting of V30 and V20 is more accurate than the predicting of V10. The SVM method is more accurate in predicting mean liver dose.