title: An assessment of PTV margin based on actual accumulated dose for prostate cancer radiotherapy.
abstract: The purpose of this work is to present the results of a margin reduction study involving dosimetric and radiobiologic assessment of cumulative dose distributions, computed using an image guided adaptive radiotherapy based framework. Eight prostate cancer patients, treated with 7-9, 6 MV, intensity modulated radiation therapy (IMRT) fields, were included in this study. The workflow consists of cone beam CT (CBCT) based localization, deformable image registration of the CBCT to simulation CT image datasets (SIM-CT), dose reconstruction and dose accumulation on the SIM-CT, and plan evaluation using radiobiological models. For each patient, three IMRT plans were generated with different margins applied to the CTV. The PTV margin for the original plan was 10 mm and 6 mm at the prostate/anterior rectal wall interface (10/6 mm) and was reduced to: (a) 5/3 mm, and (b) 3 mm uniformly. The average percent reductions in predicted tumor control probability (TCP) in the accumulated (actual) plans in comparison to the original plans over eight patients were 0.4%, 0.7% and 11.0% with 10/6 mm, 5/3 mm and 3 mm uniform margin respectively. The mean increase in predicted normal tissue complication probability (NTCP) for grades 2/3 rectal bleeding for the actual plans in comparison to the static plans with margins of 10/6, 5/3 and 3 mm uniformly was 3.5%, 2.8% and 2.4% respectively. For the actual dose distributions, predicted NTCP for late rectal bleeding was reduced by 3.6% on average when the margin was reduced from 10/6 mm to 5/3 mm, and further reduced by 1.0% on average when the margin was reduced to 3 mm. The average reduction in complication free tumor control probability (P+) in the actual plans in comparison to the original plans with margins of 10/6, 5/3 and 3 mm was 3.7%, 2.4% and 13.6% correspondingly. The significant reduction of TCP and P+ in the actual plan with 3 mm margin came from one outlier, where individualizing patient treatment plans through margin adaptation based on biological models, might yield higher quality treatments.
authors.affiliation: Department of Radiation Oncology, Henry Ford Health System, Detroit, MI 48202, USA.
authors.forename: Ning
authors.initials: N
authors.lastname: Wen
authors.forename: Akila
authors.initials: A
authors.lastname: Kumarasiri
authors.forename: Teamour
authors.initials: T
authors.lastname: Nurushev
authors.forename: Jay
authors.initials: J
authors.lastname: Burmeister
authors.forename: Lei
authors.initials: L
authors.lastname: Xing
authors.forename: Dezhi
authors.initials: D
authors.lastname: Liu
authors.forename: Carri
authors.initials: C
authors.lastname: Glide-Hurst
authors.forename: Jinkoo
authors.initials: J
authors.lastname: Kim
authors.forename: Hualiang
authors.initials: H
authors.lastname: Zhong
authors.forename: Benjamin
authors.initials: B
authors.lastname: Movsas
authors.forename: Indrin J
authors.initials: IJ
authors.lastname: Chetty
mesh_terms: D054893:Cone-Beam Computed Tomography; D006801:Humans; D008297:Male; D011336:Probability; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D011829:Radiation Dosage; D011874:Radiometry; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012189:Retrospective Studies