Deep Learning-Based Internal Target Volume (ITV) Prediction Using Cone-Beam CT Images in Lung Stereotactic Body Radiotherapy

Abstract

Purpose:This study aims to develop a deep learning (DL)-based (Mask R-CNN) method to predict the internal target volume (ITV) in cone beam computed tomography (CBCT) images for lung stereotactic body radiotherapy (SBRT) patients and to evaluate the prediction accuracy of the model using 4DCT as ground truth. Methods and Materials: This study enrolled 78 phantom cases and 156 patient cases who received SBRT treatment. We used a novel DL model (Mask R-CNN) to identify and delineate lung tumor ITV in CBCT images. The results of the DL-based method were compared quantitatively with the ground truth (4DCT) using 4 metrics, including Dice Similarity Coefficient (DSC), Relative Volume Index (RVI), 3D Motion Range (R3D), and Hausdorff Surface Distance (HD). Paired t-tests were used to determine the differences between the DL-based method and manual contouring. Results: The DSC value for 4DCTMIP versus CBCT is 0.86 ± 0.16 and for 4DCTAVG versus CBCT is 0.83 ± 0.18, indicating a high similarity of tumor delineation in CBCT and 4DCT. The mean Accuracy Precision (mAP), R3D, RVI, and HD values for phantom evaluation are 0.94 ± 0.04, 1.37 ± 0.36, 0.79 ± 0.02, and 6.79 ± 0.68, respectively. For patient evaluation, the mAP, R3D, RVI, and HD achieved averaged values of 0.74 ± 0.23, 2.39 ± 1.59, 1.27 ± 0.47, and 17.00 ± 19.89, respectively. These results showed a good correlation between predicted ITV and manually contoured ITV. The phantom p-value for RVI, R3D, and HD are 0.75, 0.08, 0.86, and patient p-value are 0.53, 0.07, 0.28, respectively. These p-values for phantom and patient showed no significant difference between the predicted ITV and physician's manual contouring. Conclusion:The current improved method (Mask R-CNN) yielded a good similarity between predicted ITV in CBCT and the manual contouring in 4DCT, thus indicating great potential for using CBCT for patient ITV contouring.