The Feasibility and Efficiency of Volumetric Modulated Arc Therapy-Based Breath Control Stereotactic Body Radiotherapy for Liver Tumors

Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy

Purpose

The purpose of this study was to evaluate the accuracy of a lung stereotactic body radiotherapy (SBRT) treatment plan with the target of a newly predicted internal target volume (ITV_predict) and the feasibility of its clinical application. ITV_predict was automatically generated 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 ITV_predict was verified by the Dice Similarity Coefficient (DSC), 3D Motion Range (R_3D), Relative Volume Index (RVI), and Hausdorff Distance (HD). The PTV_predict was generated by ITV_predict, which was registered and then projected on free-breath CT (FBCT) images. The PTV_FBCT was margined from the GTV on FBCT images gross tumor volume on free-breath CT (GTV_FBCT). Treatment plans with the target of Predict planning target volume on CBCT images (PTV_predict) and planning target volume on free-breath CT (PTV_FBCT) were respectively re-established, and the dosimetric parameters included the ratio of the volume of patients receiving at least the prescribed dose to the volume of PTV (R_100%), the ratio of the volume of patients receiving at least 50% of the prescribed dose to the volume of PTV in the Radiation Therapy Oncology Group (RTOG) 0813 Trial (R_50%), Gradient Index (GI), and the maximum dose 2 cm from the PTV (D_2cm), which were evaluated via Plan_4DCT, plan which based on PTV_predict (Plan_predict), and plan which based on PTV_FBCT (Plan_FBCT).

Result

The geometric results showed that there existed a good correlation between ITV_predict and ITV on the 4-dimensional CT [ITV_4DCT; DSC= 0.83 ±0.18]. However, the average volume of ITV_predict was 10% less than that of ITV_4DCT (p = 0.333). No significant difference in dose coverage was found in V_100% for the ITV with 99.98 ± 0.04% in the ITV_4DCT vs. 97.56 ± 4.71% in the ITV_predict (p = 0.162). Dosimetry parameters of PTV, including R_100%, R_50%, GI and D_2cm showed no statistically significant difference between each plan (p > 0.05).

Conclusion

Dosimetric parameters of Plan_predict are clinically comparable to those of the original Plan_4DCT. This study confirmed that the treatment plan based on ITV_predict produced by our model could automatically meet clinical requirements. Thus, for patients undergoing lung SBRT, the model has great potential for using CBCT images for ITV contouring which can be used in treatment planning.