3-DIMENSIONAL PLANNING TARGET VOLUMES - A MODEL AND A SOFTWARE TOOL

Citation
M. Austinseymour et al., 3-DIMENSIONAL PLANNING TARGET VOLUMES - A MODEL AND A SOFTWARE TOOL, International journal of radiation oncology, biology, physics, 33(5), 1995, pp. 1073-1080
Citations number
31
Categorie Soggetti
Oncology,"Radiology,Nuclear Medicine & Medical Imaging
ISSN journal
03603016
Volume
33
Issue
5
Year of publication
1995
Pages
1073 - 1080
Database
ISI
SICI code
0360-3016(1995)33:5<1073:3PTV-A>2.0.ZU;2-Q
Abstract
Purpose: Three dimensional (3D) target volumes are an essential compon ent of conformal therapy because the goal is to shape the treament vol ume to the target volume. The planning target volume (PTV) is defined by ICRU 50 as the clinical target volume (CTV) plus a margin to ensure that the CTV receives the prescribed dose. The margin must include al l interfractional and intrafractional treatment variations. This paper describes a software tool that automatically generates 3D PTVs from C TVs for lung cancers and immobile head and neck cancers.Methods and Ma terials: Values for the interfractional and intrafractional treatment variations were determined by a literature review and by targeted inte rviews with physicians. The software tool is written in Common LISP an d conforms to the specifications for shareable software of the Radioth erapy Treatment Planning Tools Collaborative Working Group. Results: T he tool is a rule-based expert system in which the inputs are the CTV contours, critical structure contours, and qualitative information abo ut the specific patient. The output is PTV contours, which are a cylin drical expansion of the CTV. A model for creating PTVs from CTVs is em bedded in the tool. The interfractional variation of setup uncertainty and the intrafractional variations of movement of the CTV (e.g., resp iration) and patient motion are included in the model. Measured data f or the component variations is consistent with modeling the components as independent samples from 3D Gaussian distributions. The components are combined using multivariate normal statistics to yield the cylind rical expansion factors. Rules are used to represent the values of the components for certain patient conditions (e.g., setup uncertainty fo r a head and neck patient immobilized in a mask). The tool uses a rule interpreter to combine qualitative information about a specific patie nt with rules representing the value of the components and to enter th e appropriate component values for that patient into the cylindrical e xpansion formula. Conclusion: The portable software tool allows the ra pid, consistent, and automatic generation of 3D PTVs and automatic fro m CTVs.