Dosimetric Evaluation of Automatic Segmentation for Adaptive IMRT for Head-and-Neck Cancer

Tsuji, Stuart Y.; Hwang, Andrew; Weinberg, Vivian; Yom, Sue S.; Quivey, Jeanne M.; Xia, Peng

doi:10.1016/j.ijrobp.2009.06.012

Cited by 98 publications

(103 citation statements)

References 31 publications

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“…Approaches used to quantify the performance of distinct DIR in these studies include landmark identification (12), ROI-based comparison (3, 10, 13, 34, 35), or computational phantom deformation (8, 9, 36, 37); each of these methods have their specific caveats and limitations of application. In the examined setting of DxCT-SimCT co-registration, the application of an evenly and densely distributed matrix of anatomic landmark points is intuitively understandable, and, with sufficient point placement, exceptionally spatially accurate and statistically robust as a validation method (24).…”

Section: Discussionmentioning

confidence: 99%

Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

Mohamed¹,

Ruangskul²,

Awan³

et al. 2015

Radiology

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Purpose To develop a quality assurance (QA) workflow using a robust, curated, manually-segmented anatomic ROI library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy-simulation CT (SimCT) to diagnostic CT (DxCT) co-registration. Materials and Methods SimCTs and DxCTs of twenty patients with head and neck squamous cell carcinoma treated with curative-intent intensity modulated radiotherapy (IMRT) between August 2011 and May 2012 were retrospectively retrieved under an institutional review board approval. 68 reference anatomic regions of interest (ROIs) in addition to gross tumor and nodal targets were then manually contoured on each scan. DxCT was registered to SimCT rigidly, and through 4 different deformable image registration (DIR) algorithms; Atlas-based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs using similarity coefficient metrics (i.e. Dice similarity coefficient) and surface distance metrics (i.e. 95% maximum Hausdorff distance). Non-parametric Steel test with control was used to compare different DIR algorithms to rigid registration (RIR) with post hoc Wilcoxon rank test for stratified metric comparison. Results A total of 2720 anatomic and 50 tumor/nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for both anatomic and target ROIs conformance as shown for the majority of comparison metrics (Steel test, p-value <0.008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures/category, and SimCT image slice thickness. Conclusion Development of a formal ROI-based QA workflow for registration assessment revealed improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck DxCT-SimCT allineation, especially for target delineation.

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Section: Discussionmentioning

confidence: 99%

Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

Mohamed¹,

Ruangskul²,

Awan³

et al. 2015

Radiology

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“…A daily contouring of organ at risk (OAR) and tumor might allow distributing the dose to the observed deformations [6]. By using a hybrid deformable image registration (HDIR) algorithms during treatments, a quantitative evaluation of warping become possible for each single fraction, enabling proper modifications of treatment plan related to the observed variations [7e9].…”

Section: Introductionmentioning

confidence: 99%

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

et al. 2015

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“…Deformable image registration is not limited to the 6 degrees of freedom and permits different sub-sections of the image to move and scale independently, enabling better estimates of the spatial relationship between the volume elements of corresponding structures across image data sets. Deformable image registration is potentially useful for adaptive re-planning [5,6] and as a tool for computer assisted target and organ segmentation for a number of treatment sites [7][8][9]. One important application of deformable image registration is radiation dose summation [10].…”

mentioning

confidence: 99%

Assessment of cumulative external beam and intracavitary brachytherapy organ doses in gynecologic cancers using deformable dose summation

Teo¹,

Millar²,

Ding

et al. 2015

Radiotherapy and Oncology

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Dosimetric Evaluation of Automatic Segmentation for Adaptive IMRT for Head-and-Neck Cancer

Cited by 98 publications

References 31 publications

Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

Assessment of cumulative external beam and intracavitary brachytherapy organ doses in gynecologic cancers using deformable dose summation

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