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

被引:2
|
作者
Zhang, Shujun [1 ]
Lv, Bo [1 ]
Zheng, Xiangpeng [1 ]
Li, Ya [1 ]
Ge, Weiqiang [1 ]
Zhang, Libo [1 ]
Mo, Fan [1 ]
Qiu, Jianjian [1 ]
机构
[1] Fudan Univ, Huadong Hosp, Dept Radiat Oncol, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
4DCT; CBCT (cone beam computed tomography); SBRT (stereotactic body radiation therapy); deep learning; Mask R-CNN; COMPUTED-TOMOGRAPHY; RADIATION-THERAPY; TREATMENT PLANS; CANCER; MOTION; TOOL; SEGMENTATION; DELINEATION; IMAGES; 4DCT;
D O I
10.3389/fpubh.2022.860135
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
PurposeThe 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 (ITVpredict) and the feasibility of its clinical application. ITVpredict was automatically generated by our in-house deep learning model according to the cone-beam CT (CBCT) image database. MethodA 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 (R-3D), 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 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 (PTVpredict) and planning target volume on free-breath CT (PTVFBCT) 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 PTVpredict (Plan(predict)), and plan which based on PTVFBCT (Plan(FBCT)). ResultThe geometric results showed that there existed a good correlation between ITVpredict and ITV on the 4-dimensional CT [ITV4DCT; DSC= 0.83 +/- 0.18]. However, the average volume of ITVpredict was 10% less than that of ITV4DCT (p = 0.333). No significant difference in dose coverage was found in V-100% for the ITV with 99.98 +/- 0.04% in the ITV4DCT vs. 97.56 +/- 4.71% in the ITVpredict (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). ConclusionDosimetric parameters of Plan(predict) are clinically comparable to those of the original Plan(4DCT.) This study confirmed that the treatment plan based on ITVpredict 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.
引用
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页数:9
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