Three dimensional planning target volumes: A model and a software tool

Austin-Seymour, Mary; Kalet, Ira J.; McDonald, John; Kromhout-Schiro, Sharon; Jacky, Jonathan; Hummel, Sharon; Unger, Jonathan

doi:10.1016/0360-3016(95)00217-0

Cited by 61 publications

(12 citation statements)

References 27 publications

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“…This ensures that adequate margins are included in the PTV, so that the CTV will receive the prescribed dose during each treatment session. Portable software tools have been developed which allow 3D PTVs to be rapidly, consistently, and automatically generated from CTVs [13]. This improved knowledge of target location and configuration, when incorporated into 3D treatment planning systems, can both reduce the probability of local failure due to inadequate target definition, and greatly enhance the ability of newer techniques, such as heavy charged particle radiation therapy, to safely escalate the dose delivered to the tumor, thus improving the probability of achieving uncomplicated local control of human tumors at various sites in the body.…”

Section: Target Definition and Conformal 3d Treatment Planningmentioning

confidence: 99%

Proton therapy for tumors of the skull base

1999

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Charged particle beams are ideal for treating skull base and cervical spine tumors: dose can be focused in the target, while achieving significant sparing of the brain, brain stem, cervical cord, and optic nerves and chiasm. For skull base tumors, 10-year local control rates with combined proton-photon therapy are highest for chondrosarcomas, intermediate for male chordomas, and lowest for female chordomas (94%, 65%, and 42%, respectively). For cervical spine tumors, 10-year local control rates are not significantly different for chordomas and chondrosarcomas (54% and 48%, respectively), nor is there any difference in local control between males and females. Observed treatment-related morbidity has been judged acceptable, in view of the major morbidity and mortality which accompany uncontrolled tumor growth.

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Section: Target Definition and Conformal 3d Treatment Planningmentioning

confidence: 99%

Proton therapy for tumors of the skull base

1999

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“…The PTV volume was measured using the algorithm of treatment planning systems, and its radius (r PTV ) was calculated by plugging the volume into Equation (1). The difference between the ideal margin and r PTV 2r GTV/CTV was regarded as the error of margin growing.…”

Section: The Volume Difference Methodsmentioning

confidence: 99%

“…In 1995, Austin-Seymour et al 1,2 proposed a cylindrical expansion method. This method firstly created the convex hull of the CTV, and then the PTV was computed by moving each point on the CTV convex hull contour away from its centre by a distance equal to the required margin distance in transaxial plane.…”

Section: Introductionmentioning

confidence: 99%

A simple method of evaluating margin-growing accuracy in image-guided radiation therapy

Wang¹,

Jin²,

Zhou³

et al. 2016

BJR

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“…For our autoactivation algorithm we have used a modified version of the triangulation method described by Fuchs et al 17 Some methods have been published for the calculation of 3D margins around a delineated gross tumor volume ͑GTV͒ or clinical target volume ͑CTV͒ in order to extract the PTV. [18][19][20][21] These methods as well as our own tests using volume growing methods based on the triangulated surface 22 have resulted in run times in the range seconds to minutes. The autoactivation algorithm provide a more rapid procedure, of the order of milliseconds, which can deal with several 3D structures simultaneously in an interactive manner.…”

Section: A Introductionmentioning

confidence: 99%

Autoactivation of source dwell positions for HDR brachytherapy treatment planning

et al. 2000

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The most accurate classical dose optimization algorithms in HDR brachytherapy strongly depend on an appropriate selection of source dwell positions which fulfill user-defined geometrical boundary conditions which are relative to patient anatomy. Most anatomical situations, such as for prostate and head and neck tumors, are complex and can require geometries with 5-15 catheters with 48 possible dwell positions per catheter depending on the tumor volume. The manual selection of dwell positions using visual checks by trial and error is very time consuming. This can only be improved by the use of a technique which automatically recognizes and selects the optimum dwell positions for each catheter. We have developed an algorithm, termed an autoactivation algorithm, which improves implant planning by providing a facility for the necessary automatic recognition of HDR source dwell positions.

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Three dimensional planning target volumes: A model and a software tool

Cited by 61 publications

References 27 publications

Proton therapy for tumors of the skull base

Proton therapy for tumors of the skull base

A simple method of evaluating margin-growing accuracy in image-guided radiation therapy

Autoactivation of source dwell positions for HDR brachytherapy treatment planning

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