Literature DB >> 23298079

An overlap-volume-histogram based method for rectal dose prediction and automated treatment planning in the external beam prostate radiotherapy following hydrogel injection.

Yidong Yang1, Eric C Ford, Binbin Wu, Michael Pinkawa, Baukelien van Triest, Patrick Campbell, Danny Y Song, Todd R McNutt.   

Abstract

PURPOSE: Hydrogel injected between the rectum and prostate prior to radiotherapy provides a possible means of increased dose sparing to the rectum. Here the authors evaluate the overlap volume histogram (OVH) metric as a means to predict the rectal dose following hydrogel injection. Whether OVH predicted dose can serve as the dose objective or constraint for automated treatment planning was also investigated.
METHODS: Treatment planning was performed on 21 prostate cancer patients both pre- and posthydrogel injection, with five-field IMRT delivering 78 Gy to the planning target volume (PTV). The authors quantify the geometrical relationship between the rectum and the prostate PTV using an OVH metric which determines the fractional volume of the rectum that is within a specified distance of the PTV. For an OVH distance the authors selected, L(20), the PTV expansion distance at which 20% of the rectum overlaps. The authors calculated the rectal dose, D(20), received by 20% of the rectum volume on the dose volume histogram. Linear regression was used to examine the correlation between the L(20) and D(20), and between ΔL(20) and ΔD(20) (i.e., the change of L(20) and D(20) posthydrogel injection). Additionally, rectal dose D(15), D(25), D(35), D(50), and bladder dose D(15) were predicted from the OVH (L(15), L(25), L(35), L(50), for rectum and L(15) for bladder) by the L(x)-D(x) linear regression. The predicted doses were applied to the objectives for automated treatment planning of ten plans from five patients. Automatically generated plans were compared with plans manually generated on trial-and-error basis.
RESULTS: The rectal L(20) was increased and dose D(20) decreased due to the enlarged separation of rectum caused by the hydrogel injection. Linear regression showed an inverse linear correlation between L(20) and D(20), and between ΔL(20) and ΔD(20) (r(2) = 0.77, 0.60, respectively; p < 0.0001). The increase in rectal sparing (ΔD(20)) is only weakly correlated with the volume of injected hydrogel (r(2) = 0.17; p = 0.07), indicating OVH is a more predictive indicator of rectal sparing than the volume of hydrogel itself. Application of the predicted rectum and bladder doses to automated planning produced acceptable treatment plans, with rectal dose reduced for eight of ten plans.
CONCLUSIONS: The OVH metric can predict the rectal dose in the external beam prostate radiotherapy for patients with hydrogel injection. The predicted doses can be applied to the objectives of optimization in automated treatment planning to produce acceptable treatment plans.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 23298079     DOI: 10.1118/1.4769424

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  27 in total

Review 1.  Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations.

Authors:  Mohammad Hussein; Ben J M Heijmen; Dirk Verellen; Andrew Nisbet
Journal:  Br J Radiol       Date:  2018-09-04       Impact factor: 3.039

2.  Treatment planning after hydrogel injection during radiotherapy of prostate cancer.

Authors:  M Pinkawa; C Bornemann; N Escobar-Corral; M D Piroth; R Holy; M J Eble
Journal:  Strahlenther Onkol       Date:  2013-07-10       Impact factor: 3.621

3.  A multi-institutional clinical trial of rectal dose reduction via injected polyethylene-glycol hydrogel during intensity modulated radiation therapy for prostate cancer: analysis of dosimetric outcomes.

Authors:  Danny Y Song; Klaus K Herfarth; Matthias Uhl; Michael J Eble; Michael Pinkawa; Baukelien van Triest; Robin Kalisvaart; Damien C Weber; Raymond Miralbell; Theodore L Deweese; Eric C Ford
Journal:  Int J Radiat Oncol Biol Phys       Date:  2013-02-13       Impact factor: 7.038

4.  Predicting the dose absorbed by organs at risk during intensity modulated radiation therapy for nasopharyngeal carcinoma.

Authors:  Haowen Pang; Xiaoyang Sun; Bo Yang; Jingbo Wu
Journal:  Br J Radiol       Date:  2018-08-10       Impact factor: 3.039

5.  Dose Prediction Model for Duodenum Sparing With a Biodegradable Hydrogel Spacer for Pancreatic Cancer Radiation Therapy.

Authors:  Ziwei Feng; Avani D Rao; Zhi Cheng; Eun Ji Shin; Joseph Moore; Lin Su; Seong-Hun Kim; John Wong; Amol Narang; Joseph M Herman; Todd McNutt; Dengwang Li; Kai Ding
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-07-19       Impact factor: 7.038

6.  A predictive model to guide management of the overlap region between target volume and organs at risk in prostate cancer volumetric modulated arc therapy.

Authors:  Malcolm D Mattes; Jennifer C Lee; Sara Elnaiem; Adel Guirguis; N C Ikoro; Hani Ashamalla
Journal:  Radiat Oncol J       Date:  2014-03-27

7.  A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks.

Authors:  Dan Nguyen; Azar Sadeghnejad Barkousaraie; Gyanendra Bohara; Anjali Balagopal; Rafe McBeth; Mu-Han Lin; Steve Jiang
Journal:  Phys Med Biol       Date:  2021-02-24       Impact factor: 3.609

8.  Applying a RapidPlan model trained on a technique and orientation to another: a feasibility and dosimetric evaluation.

Authors:  Hao Wu; Fan Jiang; Haizhen Yue; Hui Zhang; Kun Wang; Yibao Zhang
Journal:  Radiat Oncol       Date:  2016-08-18       Impact factor: 3.481

9.  A dosimetric evaluation of knowledge-based VMAT planning with simultaneous integrated boosting for rectal cancer patients.

Authors:  Hao Wu; Fan Jiang; Haizhen Yue; Sha Li; Yibao Zhang
Journal:  J Appl Clin Med Phys       Date:  2016-11-08       Impact factor: 2.102

10.  Support Vector Machine Model Predicts Dose for Organs at Risk in High-Dose Rate Brachytherapy of Cervical Cancer.

Authors:  Ping Zhou; Xiaojie Li; Hao Zhou; Xiao Fu; Bo Liu; Yu Zhang; Sheng Lin; Haowen Pang
Journal:  Front Oncol       Date:  2021-07-15       Impact factor: 6.244

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.