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Radiation Oncology
Published in: Radiation Oncology 1/2018

Open Access 01-12-2018 | Research

Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model

Authors: Gang Yu, Yang Li, Ziwei Feng, Cheng Tao, Zuyi Yu, Baosheng Li, Dengwang Li

Published in: Radiation Oncology | Issue 1/2018

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Abstract

Background

The purpose of this work is to benchmark RapidPlan against clinical plans for liver Intensity-modulated radiotherapy (IMRT) treatment of patients with special anatomical characteristics, and to investigate the prediction capability of the general model (Model-G) versus our specific model (Model-S).

Methods

A library consisting of 60 liver cancer patients with IMRT planning was used to set up two models (Model-S, Model-G), using the RapidPlan knowledge-based planning system. Model-S consisted of 30 patients with special anatomical characteristics where the distance from planning target volume (PTV) to the right kidney was less than three centimeters and Model-G was configurated using all 60 patients in this library. Knowledge-based IMRT plans were created for the evaluation group formed of 13 patients similar to those included in Model-S by Model-G, Model-S and manually (M), named RPG-plans, RPS-plans and M-plans, respectively. The differences in the dose-volume histograms (DVHs) were compared, not only between RP-plans and their respective M-plans, but also between RPG-plans and RPS-plans.

Results

For all 13 patients, RapidPlan could automatically produce clinically acceptable plans. Comparing RP-plans to M-plans, RP-plans improved V95% of PTV and had greater dose sparing in the right kidney. For the normal liver, RPG-plans delivered similar doses, while RPS-plans delivered a higher dose than M-plans. With respect to RapidPlan models, RPS-plans had better conformity index (CI) values and delivered lower doses to the right kidney V20Gy and maximizing point doses to spinal cord, while delivering higher doses to the normal liver.

Conclusion

The study shows that RapidPlan can create high-quality plans, and our specific model can improve the CI of PTV, resulting in more sparing of OAR in IMRT for individual liver cancer patients.
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Metadata
Title
Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model
Authors
Gang Yu
Yang Li
Ziwei Feng
Cheng Tao
Zuyi Yu
Baosheng Li
Dengwang Li
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Radiation Oncology / Issue 1/2018
Electronic ISSN: 1748-717X
DOI
https://doi.org/10.1186/s13014-018-0996-z

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