Jessie Huang-Vredevoogd, PhD

PURPOSE: We aimed to determine the relationship between gross tumor volume (GTV) dose and tumor control in women with medically inoperable endometrial cancer, and to demonstrate the feasibility of targeting a GTV-focused volume using imaged-guided brachytherapy.

METHODS AND MATERIALS: An endometrial cancer database was used to identify patients. Treatment plans were reviewed to determine doses to GTV, clinical target volume (CTV), and OARs. Uterine recurrence-free survival was evaluated as a function of CTV and GTV doses. Brachytherapy was replanned with a goal of GTV D98 EQD2 ≥ 80 Gy, without regard for coverage of the uninvolved uterus and while respecting OAR dose constraints.

RESULTS: Fifty-four patients were identified. In the delivered plans, GTV D90 EQD2 ≥ 80 Gy was achieved in 36 (81.8%) patients. Uterine recurrence-free survival was 100% in patients with GTV D90 EQD2 ≥ 80 Gy and 66.7% in patients with EQD2 < 80 Gy (p = 0.001). On GTV-only replans, GTV D98 EQD2 ≥ 80 Gy was achieved in 39 (88.6%) patients. Mean D2cc was lower for bladder (47.1 Gy vs. 73.0 Gy, p < 0.001), and sigmoid (47.0 Gy vs. 58.0 Gy, p = 0.007) on GTV-only replans compared to delivered plans. Bladder D2cc was ≥ 80 Gy in 11 (25.0%) delivered plans and four (9.1%) GTV-only replans (p = 0.043). Sigmoid D2cc was ≥ 65 Gy in 20 (45.4%) delivered plans and 10 (22.7%) GTV-only replans (p = 0.021).

CONCLUSIONS: OAR dose constraints should be prioritized over CTV coverage if GTV coverage is sufficient. Prospective evaluation of image-guided brachytherapy to a reduced, GTV-focused volume is warranted.

PMID:36030167 | DOI:10.1016/j.brachy.2022.07.006

PURPOSE: Several dual-energy computed tomography (DECT) techniques require a deformable image registration to correct for motion between the acquisition of low and high energy data. However, current DECT software does not provide tools to assess registration accuracy or allow the user to export deformed images, presenting a unique challenge for image registration quality assurance (QA). This work presents a methodology to evaluate the accuracy of DECT deformable registration and to quantify the impact of registration errors on end-product images.

METHODS: The deformable algorithm implemented in Siemen Healthineers's Syngo was evaluated using a deformable abdomen phantom and a rigid phantom to mimic sliding motion in the thorax. Both phantoms were imaged using sequential 80 and 140 kVp scans with motion applied between the two scans. Since Syngo does not allow the export of the deformed images, this study focused on quantifying the accuracy of various end-product, dual-energy images resulting from processing of deformed images.

RESULTS: The Syngo algorithm performed well for the abdomen phantom with a mean registration error of 0.4 mm for landmark analysis, Dice similarity coefficients (DSCs) > 0.90 for five organs contoured, and mean iodine concentrations within 0.2 mg/mL of values measured on static images. For rigid sliding motion, the algorithm performed poorer and resulted in noticeable registration errors toward the superior and inferior scan extents and DSCs as low as 0.41 for iodine rods imaged in the phantom. Additionally, local iodine concentration errors in areas of misregistration exceeded 3 mg/mL.

CONCLUSIONS: This work represents the first methodology for DECT image registration QA using commercial software. Our data support the clinical use of the Syngo algorithm for abdominal sites with limited motion (i.e., pancreas and liver). However, dual-energy images generated with this algorithm should be used with caution for quantitative measurements in areas with sliding motion.

PMID:32710502 | PMC:PMC7497912 | DOI:10.1002/acm2.12987

The RayStation treatment planning system implements a Monte Carlo (MC) algorithm for electron dose calculations. For a TrueBeam accelerator, beam modeling was performed for four electron energies (6, 9, 12, and 15 MeV), and the dose calculation accuracy was tested for a range of geometries. The suite of validation tests included those tests recommended by AAPM's Medical Physics Practice Guideline 5.a, but extended beyond these tests in order to validate the MC algorithm in more challenging geometries. For MPPG 5.a testing, calculation accuracy was evaluated for square cutouts of various sizes, two custom cutout shapes, oblique incidence, and heterogenous media (cork). In general, agreement between ion chamber measurements and RayStation dose calculations was excellent and well within suggested tolerance limits. However, this testing did reveal calculation errors for the output of small cutouts. Of the 312 output factors evaluated for square cutouts, 20 (6.4%) were outside of 3% and 5 (1.6%) were outside of 5%, with these larger errors generally being for the smallest cutout sizes within a given applicator. Adjustment of beam modeling parameters did not fix these calculation errors, nor does the planning software allow the user to input correction factors as a function of field size. Additional validation tests included several complex phantom geometries (triangular nose phantom, lung phantom, curved breast phantom, and cortical bone phantom), designed to test the ability of the algorithm to handle high density heterogeneities and irregular surface contours. In comparison to measurements with radiochromic film, RayStation showed good agreement, with an average of 89.3% pixels passing for gamma analysis (3%/3mm) across four phantom geometries. The MC algorithm was able to accurately handle the presence of irregular surface contours (curved cylindrical phantom and a triangular nose phantom), as well as heterogeneities (cork and cortical bone).

PMID:31120615 | PMC:PMC6560228 | DOI:10.1002/acm2.12622

PURPOSE: To characterize image quality and feasibility of using ViewRay MRI (VR)-guided brachytherapy planning for cervical cancer.

METHODS AND MATERIALS: Cervical cancer patients receiving intracavitary brachytherapy with tandem and ovoids, planned using 0.35T VR MRI at our institution, were included in this series. The high-risk clinical target volume (HR-CTV), visible gross tumor volume, bladder, sigmoid, bowel, and rectum contours for each fraction of brachytherapy were evaluated for dosimetric parameters. Typically, five brachytherapy treatments were planned using the T2 sequence on diagnostic MRI for the first and third fractions, and a noncontrast true fast imaging with steady-state precession sequence on VR or CT scan for the remaining fractions. Most patients received 5.5 Gy × 5 fractions using high-dose-rate Ir-192 following 45 Gy of whole-pelvis radiotherapy. The plan was initiated at 5.5 Gy to point A and subsequently optimized and prescribed to the HR-CTV. The goal equivalent dose in 2 Gy fractions for the combined external beam and brachytherapy dose was 85 Gy. Soft-tissue visualization using contrast-to-noise ratios to distinguish normal tissues from tumor at their interface was compared between diagnostic MRI, CT, and VR.

RESULTS: One hundred and forty-two fractions of intracavitary brachytherapy were performed from April 2015 to January 2017 on 29 cervical cancer patients, ranging from stages IB1 to IVA. The median HR-CTV was 27.78 cc, with median D90 HR-CTV of 6.1 Gy. The median time from instrument placement to start of treatment using VR was 65 min (scan time 2 min), compared to 105 min using diagnostic MRI (scan time 11 min) (t-test, p < 0.01). The contrast-to-noise ratio of tumor to cervix in both diagnostic MRI and VR had significantly higher values compared to CT (ANOVA and t-tests, p < 0.01).

CONCLUSIONS: We report the first clinical use of VR-guided brachytherapy. Time to treatment using this approach was shorter compared to diagnostic MRI. VR also provided significant advantage in visualizing the tumor and cervix compared to CT. This presents a feasible and reliable manner to image and plan gynecologic brachytherapy.

PMID:29773331 | DOI:10.1016/j.brachy.2018.04.005

PURPOSE: Dose calculation errors near metal implants are caused by limitations of the dose calculation algorithm in modeling tissue/metal interface effects as well as density assignment errors caused by imaging artifacts. The purpose of this study was to investigate two strategies for reducing dose calculation errors near metal implants: implementation of metal-based energy deposition kernels in the convolution/superposition (C/S) dose calculation method and use of metal artifact reduction methods for computed tomography (CT) imaging.

METHODS: Both error reduction strategies were investigated using a simple geometric slab phantom with a rectangular metal insert (composed of titanium or Cerrobend), as well as two anthropomorphic phantoms (one with spinal hardware and one with dental fillings), designed to mimic relevant clinical scenarios. To assess the dosimetric impact of metal kernels, the authors implemented titanium and silver kernels in a commercial collapsed cone C/S algorithm. To assess the impact of CT metal artifact reduction methods, the authors performed dose calculations using baseline imaging techniques (uncorrected 120 kVp imaging) and three commercial metal artifact reduction methods: Philips Healthcare's o-mar, GE Healthcare's monochromatic gemstone spectral imaging (gsi) using dual-energy CT, and gsi with metal artifact reduction software (mars) applied. For the simple geometric phantom, radiochromic film was used to measure dose upstream and downstream of metal inserts. For the anthropomorphic phantoms, ion chambers and radiochromic film were used to quantify the benefit of the error reduction strategies.

RESULTS: Metal kernels did not universally improve accuracy but rather resulted in better accuracy upstream of metal implants and decreased accuracy directly downstream. For the clinical cases (spinal hardware and dental fillings), metal kernels had very little impact on the dose calculation accuracy (<1.0%). Of the commercial CT artifact reduction methods investigated, the authors found that o-mar was the most consistent method, resulting in either improved dose calculation accuracy (dental case) or little impact on calculation accuracy (spine case). gsi was unsuccessful at reducing the severe artifacts caused by dental fillings and had very little impact on calculation accuracy. gsi with mars on the other hand gave mixed results, sometimes introducing metal distortion and increasing calculation errors (titanium rectangular implant and titanium spinal hardware) but other times very successfully reducing artifacts (Cerrobend rectangular implant and dental fillings).

CONCLUSIONS: Though successful at improving dose calculation accuracy upstream of metal implants, metal kernels were not found to substantially improve accuracy for clinical cases. Of the commercial artifact reduction methods investigated, o-mar was found to be the most consistent candidate for all-purpose CT simulation imaging. The mars algorithm for gsi should be used with caution for titanium implants, larger implants, and implants located near heterogeneities as it can distort the size and shape of implants and increase calculation errors.

PMID:27587042 | PMC:PMC4991994 | DOI:10.1118/1.4960632

Three commercial metal artifact reduction methods were evaluated for use in computed tomography (CT) imaging in the presence of clinically realistic metal implants: Philips O-MAR, GE's monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI monochromatic imaging with metal artifact reduction software applied (MARs). Each method was evaluated according to CT number accuracy, metal size accuracy, and streak artifact severity reduction by using several phantoms, including three anthropomorphic phantoms containing metal implants (hip prosthesis, dental fillings and spinal fixation rods). All three methods showed varying degrees of success for the hip prosthesis and spinal fixation rod cases, while none were particularly beneficial for dental artifacts. Limitations of the methods were also observed. MARs underestimated the size of metal implants and introduced new artifacts in imaging planes beyond the metal implant when applied to dental artifacts, and both the O-MAR and MARs algorithms induced artifacts for spinal fixation rods in a thoracic phantom. Our findings suggest that all three artifact mitigation methods may benefit patients with metal implants, though they should be used with caution in certain scenarios.

PMID:25585685 | PMC:PMC4311882 | DOI:10.1088/0031-9155/60/3/1047

PURPOSE: To determine whether in-house patient-specific intensity modulated radiation therapy quality assurance (IMRT QA) results predict Imaging and Radiation Oncology Core (IROC)-Houston phantom results.

METHODS AND MATERIALS: IROC Houston's IMRT head and neck phantoms have been irradiated by numerous institutions as part of clinical trial credentialing. We retrospectively compared these phantom results with those of in-house IMRT QA (following the institution's clinical process) for 855 irradiations performed between 2003 and 2013. The sensitivity and specificity of IMRT QA to detect unacceptable or acceptable plans were determined relative to the IROC Houston phantom results. Additional analyses evaluated specific IMRT QA dosimeters and analysis methods.

RESULTS: IMRT QA universally showed poor sensitivity relative to the head and neck phantom, that is, poor ability to predict a failing IROC Houston phantom result. Depending on how the IMRT QA results were interpreted, overall sensitivity ranged from 2% to 18%. For different IMRT QA methods, sensitivity ranged from 3% to 54%. Although the observed sensitivity was particularly poor at clinical thresholds (eg 3% dose difference or 90% of pixels passing gamma), receiver operator characteristic analysis indicated that no threshold showed good sensitivity and specificity for the devices evaluated.

CONCLUSIONS: IMRT QA is not a reasonable replacement for a credentialing phantom. Moreover, the particularly poor agreement between IMRT QA and the IROC Houston phantoms highlights surprising inconsistency in the QA process.

PMID:25442044 | PMC:PMC4276500 | DOI:10.1016/j.ijrobp.2014.08.334

PURPOSE: As clinics begin to use 3D metrics for intensity-modulated radiation therapy (IMRT) quality assurance, it must be noted that these metrics will often produce results different from those produced by their 2D counterparts. 3D and 2D gamma analyses would be expected to produce different values, in part because of the different search space available. In the present investigation, the authors compared the results of 2D and 3D gamma analysis (where both datasets were generated in the same manner) for clinical treatment plans.

METHODS: Fifty IMRT plans were selected from the authors' clinical database, and recalculated using Monte Carlo. Treatment planning system-calculated ("evaluated dose distributions") and Monte Carlo-recalculated ("reference dose distributions") dose distributions were compared using 2D and 3D gamma analysis. This analysis was performed using a variety of dose-difference (5%, 3%, 2%, and 1%) and distance-to-agreement (5, 3, 2, and 1 mm) acceptance criteria, low-dose thresholds (5%, 10%, and 15% of the prescription dose), and data grid sizes (1.0, 1.5, and 3.0 mm). Each comparison was evaluated to determine the average 2D and 3D gamma, lower 95th percentile gamma value, and percentage of pixels passing gamma.

RESULTS: The average gamma, lower 95th percentile gamma value, and percentage of passing pixels for each acceptance criterion demonstrated better agreement for 3D than for 2D analysis for every plan comparison. The average difference in the percentage of passing pixels between the 2D and 3D analyses with no low-dose threshold ranged from 0.9% to 2.1%. Similarly, using a low-dose threshold resulted in a difference between the mean 2D and 3D results, ranging from 0.8% to 1.5%. The authors observed no appreciable differences in gamma with changes in the data density (constant difference: 0.8% for 2D vs 3D).

CONCLUSIONS: The authors found that 3D gamma analysis resulted in up to 2.9% more pixels passing than 2D analysis. It must be noted that clinical 2D versus 3D datasets may have additional differences--for example, if 2D measurements are made with a different dosimeter than 3D measurements. Factors such as inherent dosimeter differences may be an important additional consideration to the extra dimension of available data that was evaluated in this study.

PMID:24506601 | PMC:PMC3977814 | DOI:10.1118/1.4860195

PURPOSE: Several simplifications used in clinical implementations of the convolution∕superposition (C∕S) method, specifically, density scaling of water kernels for heterogeneous media and use of a single polyenergetic kernel, lead to dose calculation inaccuracies. Although these weaknesses of the C∕S method are known, it is not well known which of these simplifications has the largest effect on dose calculation accuracy in clinical situations. The purpose of this study was to generate and characterize high-resolution, polyenergetic, and material-specific energy deposition kernels (EDKs), as well as to investigate the dosimetric impact of implementing spatially variant polyenergetic and material-specific kernels in a collapsed cone C∕S algorithm.

METHODS: High-resolution, monoenergetic water EDKs and various material-specific EDKs were simulated using the EGSnrc Monte Carlo code. Polyenergetic kernels, reflecting the primary spectrum of a clinical 6 MV photon beam at different locations in a water phantom, were calculated for different depths, field sizes, and off-axis distances. To investigate the dosimetric impact of implementing spatially variant polyenergetic kernels, depth dose curves in water were calculated using two different implementations of the collapsed cone C∕S method. The first method uses a single polyenergetic kernel, while the second method fully takes into account spectral changes in the convolution calculation. To investigate the dosimetric impact of implementing material-specific kernels, depth dose curves were calculated for a simplified titanium implant geometry using both a traditional C∕S implementation that performs density scaling of water kernels and a novel implementation using material-specific kernels.

RESULTS: For our high-resolution kernels, we found good agreement with the Mackie et al. kernels, with some differences near the interaction site for low photon energies (<500 keV). For our spatially variant polyenergetic kernels, we found that depth was the most dominant factor affecting the pattern of energy deposition; however, the effects of field size and off-axis distance were not negligible. For the material-specific kernels, we found that as the density of the material increased, more energy was deposited laterally by charged particles, as opposed to in the forward direction. Thus, density scaling of water kernels becomes a worse approximation as the density and the effective atomic number of the material differ more from water. Implementation of spatially variant, polyenergetic kernels increased the percent depth dose value at 25 cm depth by 2.1%-5.8% depending on the field size, while implementation of titanium kernels gave 4.9% higher dose upstream of the metal cavity (i.e., higher backscatter dose) and 8.2% lower dose downstream of the cavity.

CONCLUSIONS: Of the various kernel refinements investigated, inclusion of depth-dependent and metal-specific kernels into the C∕S method has the greatest potential to improve dose calculation accuracy. Implementation of spatially variant polyenergetic kernels resulted in a harder depth dose curve and thus has the potential to affect beam modeling parameters obtained in the commissioning process. For metal implants, the C∕S algorithms generally underestimate the dose upstream and overestimate the dose downstream of the implant. Implementation of a metal-specific kernel mitigated both of these errors.

PMID:24320507 | PMC:PMC3856653 | DOI:10.1118/1.4831758