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Evaluating the Utility of "3D Slicer" As a Fast and Independent Tool to assess Intrafractional Organ Dose Variations in Gynecological brachytherapy

Overview
Journal Brachytherapy
Specialty Radiology
Date 2016 May 16
PMID 27180127
Citations 6
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Abstract

Purpose: To demonstrate the utility of "3D Slicer" for easy treatment verification by comparing dose-volume histograms (DVHs) calculated on pretreatment and posttreatment images.

Methods And Materials: Thirty cervical cancer patients were CT scanned twice: first for treatment planning and a second time after the dose delivery. The initial plan was manually duplicated on the posttreatment image set in Flexiplan treatment planning system, and DVH parameters were calculated. Pretreatment and posttreatment images, organ structures, and plan data were exported from the treatment planning system to 3D Slicer to validate DVH parameter calculation with 3D Slicer. The gamma analysis was used to compare Flexiplan and 3D Slicer DVHs. Posttreatment images were rigidly fused on the initial CT to automatically transfer the data of the pretreatment plan onto the posttreatment images. DVH parameters were calculated in 3D Slicer for both image sets, and their relative variations were compared.

Results: In calculating DVH parameter variations, no significant differences were observed between Flexiplan and 3D Slicer. Where the registration accuracy was better than 0.03, they returned similar results for D2 cm(3) of bladder, rectum, and sigmoid. Mean and standard deviation of DVH parameters were calculated on pretreatment and posttreatment images for several organs; both the manually duplicated plan and the automatically registered plan in SlicerRT returned comparable relative variations of these parameters. For 88% of the organs, more than 95% of the DVH dose bins passed the gamma analysis.

Conclusions: We tested an automated DVH assessment method with an imaging freeware, 3D Slicer, for use in image-guided adaptive brachytherapy. SlicerRT is a viable verification tool to report and detect DVH variations between different contoured images series.

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