Search

The prediction of liver D-mean with 3-dimensional radiation treatment planning (3DRTP) is time consuming in the selection of proton beam therapy (PBT), and deep learning prediction generally requires large and tumor-specific databases. We developed a simple dose prediction tool (SDP) using deep learning and a novel contour-based data augmentation (CDA) approach and assessed its usability. We trained the SDP to predict the liver D mean immediately. Five and two computed tomography (CT) data sets of actual patients with liver cancer were used for the training and validation. Data augmentation was performed by artificially embedding 199 contours of virtual clinical target volume (CTV) into CT images for each patient. The data sets of the CTVs and OARs are labeled with liver D-mean for six different treatment plans using two-dimensional calculations assuming all tissue densities as 1.0. The test of the validated model was performed using 10 unlabeled CT data sets of actual patients. Contouring only of the liver and CTV was required as input. The mean relative error (MRE), the mean percentage error (MPE) and regression coefficient between the planned and predicted D-mean was 0.1637, 6.6%, and 0.9455, respectively. The mean time required for the inference of liver D-mean of the six different treatment plans for a patient was 4.47 +/- 0.13 seconds. We conclude that the SDP is cost-effective and usable for gross estimation of liver Dmean in the clinic although the accuracy should be improved further if we need the accuracy of liver D-mean tobe compatible with 3DRTP.

The prediction of liver D-mean with 3-dimensional radiation treatment planning (3DRTP) is time consuming in the selection of proton beam therapy (PBT), and deep learning prediction generally requires large and tumor-specific databases. We developed a simple dose prediction tool (SDP) using deep learning and a novel contour-based data augmentation (CDA) approach and assessed its usability. We trained the SDP to predict the liver D mean immediately. Five and two computed tomography (CT) data sets of actual patients with liver cancer were used for the training and validation. Data augmentation was performed by artificially embedding 199 contours of virtual clinical target volume (CTV) into CT images for each patient. The data sets of the CTVs and OARs are labeled with liver D-mean for six different treatment plans using two-dimensional calculations assuming all tissue densities as 1.0. The test of the validated model was performed using 10 unlabeled CT data sets of actual patients. Contouring only of the liver and CTV was required as input. The mean relative error (MRE), the mean percentage error (MPE) and regression coefficient between the planned and predicted D-mean was 0.1637, 6.6%, and 0.9455, respectively. The mean time required for the inference of liver D-mean of the six different treatment plans for a patient was 4.47 +/- 0.13 seconds. We conclude that the SDP is cost-effective and usable for gross estimation of liver Dmean in the clinic although the accuracy should be improved further if we need the accuracy of liver D-mean tobe compatible with 3DRTP.