title: Analysis of the advantage of individual PTVs defined on axial 3D CT and 4D CT images for liver cancer.
abstract: The purpose of this study was to compare positional and volumetric differences of planning target volumes (PTVs) defined on axial three dimensional CT (3D CT) and four dimensional CT (4D CT) for liver cancer. Fourteen patients with liver cancer underwent 3D CT and 4D CT simulation scans during free breathing. The tumor motion was measured by 4D CT. Three internal target volumes (ITVs) were produced based on the clinical target volume from 3DCT (CTV3D): i) A conventional ITV (ITVconv) was produced by adding 10 mm in CC direction and 5 mm in LR and and AP directions to CTV3D; ii) A specific ITV (ITVspec) was created using a specific margin in transaxial direction; iii) ITVvector was produced by adding an isotropic margin derived from the individual tumor motion vector. ITV4D was defined on the fusion of CTVs on all phases of 4D CT. PTVs were generated by adding a 5 mm setup margin to ITVs. The average centroid shifts between PTVs derived from 3DCT and PTV4D in left-right (LR), anterior-posterior (AP), and cranial-caudal (CC) directions were close to zero. Comparing PTV4D to PTVconv, PTVspec, and PTVvector resulted in a decrease in volume size by 33.18% ± 12.39%, 24.95% ± 13.01%, 48.08% ± 15.32%, respectively. The mean degree of inclusions (DI) of PTV4D in PTVconv, and PTV4D in PTVspec, and PTV4D in PTVvector was 0.98, 0.97, and 0.99, which showed no significant correlation to tumor motion vector (r = -0.470, 0.259, and 0.244; p = 0.090, 0.371, and 0.401). The mean DIs of PTVconv in PTV4D, PTVspec in PTV4D, and PTVvector in PTV4D was 0.66, 0.73, and 0.52. The size of individual PTV from 4D CT is significantly less than that of PTVs from 3DCT. The position of targets derived from axial 3DCT images scatters around the center of 4D targets randomly. Compared to conventional PTV, the use of 3D CT-based PTVs with individual margins cannot significantly reduce normal tissues being unnecessarily irradiated, but may contribute to reducing the risk of missing targets for tumors with large motion.
authors.affiliation: Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Jinan, China.
authors.forename: Fengxiang
authors.initials: F
authors.lastname: Li
authors.forename: Jianbin
authors.initials: J
authors.lastname: Li
authors.forename: Jun
authors.initials: J
authors.lastname: Xing
authors.forename: Yingjie
authors.initials: Y
authors.lastname: Zhang
authors.forename: Tingyong
authors.initials: T
authors.lastname: Fan
authors.forename: Min
authors.initials: M
authors.lastname: Xu
authors.forename: Dongping
authors.initials: D
authors.lastname: Shang
authors.forename: Tonghai
authors.initials: T
authors.lastname: Liu
authors.forename: Jinlong
authors.initials: J
authors.lastname: Song
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D056973:Four-Dimensional Computed Tomography; D006801:Humans; D021621:Imaging, Three-Dimensional; D008113:Liver Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy; D008297:Male; D008875:Middle Aged; D009038:Motion; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D014057:Tomography, X-Ray Computed