Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy - Zhang, Shujun; Lv, Bo; Zheng, Xiangpeng; Li, Ya; Ge, Weiqiang; Zhang, Libo; Mo, Fan; Qiu, Jianjian (Frontiers in Public Health, 2022)
Purpose: The purpose of this study was to evaluate the accuracy of a lung stereotactic body radiotherapy (SBRT) treatment plan with the target of ITVpredict and the feasibility of its clinical application. ITVpredict was generated automatically by our in-house deep learning model according to the cone-beam CT (CBCT) image database. Method: A retrospective study of 45 patients who underwent SBRT was involved, and Mask R-CNN based algorithm model helped to predict the internal target volume (ITV) using the CBCT image database. The geometric accuracy of ITVpredict was verified by the Dice Similarity Coefficient (DSC), 3D Motion Range (R3D), Relative Volume Index (RVI), and Hausdorff Distance (HD).The PTVpredict was generated by ITVpredict, which was registered and then projected on free-breath CT (FBCT) images. The PTVFBCT was margined from GTVFBCT. Treatment plans with the target of PTVpredict and PTVFBCT were re-established respectively, and the dosimetric parameters included R100%, R50%, Gradient Index (GI) and D2cm, which were evaluated via Plan4DCT, Planpredict and PlanFBCT. Result: The geometric results showed that there existed a good correlation between ITVpredict and ITV4DCT (DSC= 0.83 ±0.18). However, the average volume of ITVpredict was 10% less than that of ITV4DCT (P = .333). No significant difference in dose coverage was found in V100% for the ITV with 99.98±0.04% in the ITV4DCT versus 97.56±4.71% in the ITVpredict (P = .162). Dosimetry parameters of PTV, including R100%, R50%, Gradient Index (GI) and D2cm showed no statistically significant difference between each plan(p>0.05). Conclusion: Dosimetric parameters of Planpredict are clinically comparable to those of the original Plan4DCT. This study confirmed that the treatment plan based on ITVpredict produced by our model automatically could meet clinical requirements. Thus, for lung SBRT patients, the model has great potential for using CBCT images for ITV contouring, which can be used in treatment planning.