F Yang

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

Abstract. Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes.

TU-AB-BRA-04: Quantitative Radiomics: Sensitivity of PET Textural Features to Image Acquisition and Reconstruction Parameters Implies the Need for Standards

Purpose: Despite increased use of heterogeneity metrics for PET imaging, standards for metrics such as textural features have yet to be developed. We evaluated the quantitative variability caused by image acquisition and reconstruction parameters on PET textural features. Methods: PET images of the NEMA IQ phantom were simulated with realistic image acquisition noise. 35 features based on intensity histograms (IH), co-occurrence matrices (COM), neighborhood-difference matrices (NDM), and zone-size matrices (ZSM) were evaluated within lesions (13, 17, 22, 28, 33 mm diameter). Variability in metrics across 50 independent images was evaluated as percent difference from mean for three phantom girths (850, 1030, 1200 mm) and two OSEM reconstructions (2 iterations, 28 subsets, 5 mm FWHM filtration vs 6 iterations, 28 subsets, 8.6 mm FWHM filtration). Also, patient sample size to detect a clinical effect of 30% with Bonferroni-corrected α=0.001 and 95% power was estimated. Results: As a class, NDM features demonstrated greatest sensitivity in means (5–50% difference for medium girth and reconstruction comparisons and 10–100% for large girth comparisons). Some IH features (standard deviation, energy, entropy) had variability below 10% for all sensitivity studies, while others (kurtosis, skewness) had variability above 30%. COM and ZSM features had complex sensitivities; correlation, energy, entropy (COM) and zone percentage, short-zone emphasis, zone-size non-uniformity (ZSM) had variability less than 5% while other metrics had differences up to 30%. Trends were similar for sample size estimation; for example, coarseness, contrast, and strength required 12, 38, and 52 patients to detect a 30% effect for the small girth case but 38, 88, and 128 patients in the large girth case. Conclusion: The sensitivity of PET textural features to image acquisition and reconstruction parameters is large and feature-dependent. Standards are needed to ensure that prospective trials which incorporate textural features are properly designed to detect clinical endpoints. Supported by NIH grants R01 CA169072, U01 CA148131, NCI Contract (SAIC-Frederick) 24XS036-004, and a research contract from GE Healthcare.

Dosimetric effects of bolus and lens shielding in treating ocular lymphomas with low-energy electrons

Radiation therapy is an effective treatment for primary orbital lymphomas. Lens shielding with electrons can reduce the risk of high-grade cataracts in patients undergoing treatment for superficial tumors. This work evaluates the dosimetric effects of a suspended eye shield, placement of bolus, and varying electron energies. Film (GafChromic EBT3) dosimetry and relative output factors were measured for 6, 8, and 10 MeV electron energies. A customized 5-cm diameter circle electron orbital cutout was constructed for a 6 × 6-cm applicator with a suspended lens shield (8-mm diameter Cerrobend cylinder, 2.2-cm length). Point doses were measured using a scanning electron diode in a solid water phantom at depths representative of the anterior and posterior lens. Depth dose profiles were compared for 0-mm, 3-mm, and 5-mm bolus thicknesses. At 5 mm (the approximate distance of the anterior lens from the surface of the cornea), the percent depth dose under the suspended lens shield was reduced to 15%, 15%, and 14% for electron energies 6, 8, and 10 MeV, respectively. Applying bolus reduced the benefit of lens shielding by increasing the estimated doses under the block to 27% for 3-mm and 44% for 5-mm bolus for a 6 MeV incident electron beam. This effect is minimized with 8 MeV electron beams where the corresponding values were 15.5% and 18% for 3-mm and 5-mm bolus. Introduction of a 7-mm hole in 5-mm bolus to stabilize eye motion during treatment altered lens doses by about 1%. Careful selection of electron energy and consideration of bolus effects are needed to account for electron scatter under a lens shield.

Electron beam energy QA — a note on measurement tolerances

Monthly QA is recommended to verify the constancy of high‐energy electron beams generated for clinical use by linear accelerators. The tolerances are defined as 2%/2 mm in beam penetration according to AAPM task group report 142. The practical implementation is typically achieved by measuring the ratio of readings at two different depths, preferably near the depth of maximum dose and at the depth corresponding to half the dose maximum. Based on beam commissioning data, we show that the relationship between the ranges of energy ratios for different electron energies is highly nonlinear. We provide a formalism that translates measurement deviations in the reference ratios into change in beam penetration for electron energies for six Elekta (6‐18 MeV) and eight Varian (6‐22 MeV) electron beams. Experimental checks were conducted for each Elekta energy to compare calculated values with measurements, and it was shown that they are in agreement. For example, for a 6 MeV beam a deviation in the measured ionization ratio of ±15% might still be acceptable (i.e., be within ±2 mm), whereas for an 18 MeV beam the corresponding tolerance might be ±6%. These values strongly depend on the initial ratio chosen. In summary, the relationship between differences of the ionization ratio and the corresponding beam energy are derived. The findings can be translated into acceptable tolerance values for monthly QA of electron beam energies. PACS number(s): 87.55, 87.56Monthly QA is recommended to verify the constancy of high-energy electron beams generated for clinical use by linear accelerators. The tolerances are defined as 2%/2 mm in beam penetration according to AAPM task group report 142. The practical implementation is typically achieved by measuring the ratio of readings at two different depths, preferably near the depth of maximum dose and at the depth corresponding to half the dose maximum. Based on beam commissioning data, we show that the relationship between the ranges of energy ratios for different electron energies is highly nonlinear. We provide a formalism that translates measurement deviations in the reference ratios into change in beam penetration for electron energies for six Elekta (6-18 MeV) and eight Varian (6-22 MeV) electron beams. Experimental checks were conducted for each Elekta energy to compare calculated values with measurements, and it was shown that they are in agreement. For example, for a 6 MeV beam a deviation in the measured ionization ratio of ±15% might still be acceptable (i.e., be within ±2 mm), whereas for an 18 MeV beam the corresponding tolerance might be ±6%. These values strongly depend on the initial ratio chosen. In summary, the relationship between differences of the ionization ratio and the corresponding beam energy are derived. The findings can be translated into acceptable tolerance values for monthly QA of electron beam energies. PACS number(s): 87.55, 87.56.

Rounded leaf end modeling in Pinnacle VMAT treatment planning for fixed jaw linacs

During volume-modulated arc therapies (VMAT), dosimetric errors are introduced by multiple open dynamic leaf gaps that are present in fixed diaphragm linear accelerators. The purpose of this work was to develop a methodology for adjusting the rounded leaf end modeling parameters to improve out-of-field dose agreement in SmartArc VMAT treatment plans delivered by fixed jaw linacs where leaf gap dose is not negligible. Leaf gap doses were measured for an Elekta beam modulator linac with 0.4 cm micro-multileaf collimators (MLC) using an A16 micro-ionization chamber, a MatriXX ion chamber detector array, and Kodak EDR2 film dosimetry in a solid water phantom. The MLC offset and rounded end tip radius were adjusted in the Pinnacle treatment planning system (TPS) to iteratively arrive at the optimal configuration for 6 MV and 10 MV photon energies. Improvements in gamma index with a 3%/3 mm acceptance criteria and an inclusion threshold of 5% of maximum dose were measured, analyzed, and validated using an ArcCHECK diode detector array for field sizes ranging from 1.6 to 14 cm square field arcs and Task Group (TG) 119 VMAT test cases. The best results were achieved for a rounded leaf tip radius of 13 cm with a 0.1 cm MLC offset. With the optimized MLC model, measured gamma indices ranged between 99.9% and 91.7% for square field arcs with sizes between 3.6 cm and 1.6 cm, with a maximum improvement of 42.7% for the 1.6 cm square field size. Gamma indices improved up to 2.8% in TG-119 VMAT treatment plans. Imaging and Radiation Oncology Core (IROC) credentialing of a VMAT plan with the head and neck phantom passed with a gamma index of 100%. Fine-tune adjustments to MLC rounded leaf ends may improve patient-specific QA pass rates and provide more accurate predictions of dose deposition to avoidance structures. PACS number(s): 87.55.D-, 87.55.kd, 87.55.kh.During volume‐modulated arc therapies (VMAT), dosimetric errors are introduced by multiple open dynamic leaf gaps that are present in fixed diaphragm linear accelerators. The purpose of this work was to develop a methodology for adjusting the rounded leaf end modeling parameters to improve out‐of‐field dose agreement in SmartArc VMAT treatment plans delivered by fixed jaw linacs where leaf gap dose is not negligible. Leaf gap doses were measured for an Elekta beam modulator linac with 0.4 cm micro‐multileaf collimators (MLC) using an A16 micro‐ionization chamber, a MatriXX ion chamber detector array, and Kodak EDR2 film dosimetry in a solid water phantom. The MLC offset and rounded end tip radius were adjusted in the Pinnacle treatment planning system (TPS) to iteratively arrive at the optimal configuration for 6 MV and 10 MV photon energies. Improvements in gamma index with a 3%/3 mm acceptance criteria and an inclusion threshold of 5% of maximum dose were measured, analyzed, and validated using an ArcCHECK diode detector array for field sizes ranging from 1.6 to 14 cm square field arcs and Task Group (TG) 119 VMAT test cases. The best results were achieved for a rounded leaf tip radius of 13 cm with a 0.1 cm MLC offset. With the optimized MLC model, measured gamma indices ranged between 99.9% and 91.7% for square field arcs with sizes between 3.6 cm and 1.6 cm, with a maximum improvement of 42.7% for the 1.6 cm square field size. Gamma indices improved up to 2.8% in TG‐119 VMAT treatment plans. Imaging and Radiation Oncology Core (IROC) credentialing of a VMAT plan with the head and neck phantom passed with a gamma index of 100%. Fine‐tune adjustments to MLC rounded leaf ends may improve patient‐specific QA pass rates and provide more accurate predictions of dose deposition to avoidance structures. PACS number(s): 87.55.D‐, 87.55.kd, 87.55.kh

TU-F-CAMPUS-J-04: Impact of Voxel Anisotropy On Statistic Texture Features of Oncologic PET: A Simulation Study

Purpose: Texture metrics extracted from oncologic PET have been investigated with respect to their usefulness as definitive indicants for prognosis in a variety of cancer. Metric calculation is often based on cubic voxels. Most commonly used PET scanners, however, produce rectangular voxels, which may change texture metrics. The objective of this study was to examine the variability of PET texture feature metrics resulting from voxel anisotropy. Methods: Sinograms of NEMA NU-2 phantom for 18F-FDG were simulated using the ASIM simulation tool. The obtained projection data was reconstructed (3D-OSEM) on grids of cubic and rectangular voxels, producing PET images of resolution of 2.73x2.73x3.27mm3 and 3.27x3.27x3.27mm3, respectively. An interpolated dataset obtained from resampling the rectangular voxel data for isotropic voxel dimension (3.27mm) was also considered. For each image dataset, 28 texture parameters based on grey-level co-occurrence matrices (GLCOM), intensity histograms (GLIH), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated within lesions of diameter of 33, 28, 22, and 17mm. Results: In reference to the isotopic image data, texture features appearing on the rectangular voxel data varied with a range of -34-10% for GLCOM based, -31-39% for GLIH based, -80 -161% for GLNDM based, and −6–45% for GLZSM based while varied with a range of -35-23% for GLCOM based, -27-35% for GLIH based, -65-86% for GLNDM based, and -22 -18% for GLZSM based for the interpolated image data. For the anisotropic data, GLNDM_cplx exhibited the largest extent of variation (161%) while GLZSM_zp showed the least (<1%). As to the interpolated data, GLNDM_busy varied the most (86%) while GLIH_engy varied the least (<1%). Conclusion: Variability of texture appearance on oncologic PET with respect to voxel representation is substantial and feature-dependent. It necessitates consideration of standardized voxel representation for inter-institution studies attempting to validate prognostic values of PET texture features in cancer treatment.

Validating FMEA output against incident learning data: A study in stereotactic body radiation therapy: Validating FMEA output against incident learning data

PURPOSE Though failure mode and effects analysis (FMEA) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge, its output has never been validated against data on errors that actually occur. The objective of this study was to perform FMEA of a stereotactic body radiation therapy (SBRT) treatment planning process and validate the results against data recorded within an incident learning system. METHODS FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, dosimetrists, and IT technologists. Potential failure modes were identified through a systematic review of the process map. Failure modes were rated for severity, occurrence, and detectability on a scale of one to ten and risk priority number (RPN) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that has been active for two and a half years. Differences between FMEA anticipated failure modes and existing incidents were identified. RESULTS FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near-miss events related to SBRT planning. Combining both methods yielded a total of 76 possible process failures, of which 13 (17%) were missed by FMEA while 43 (57%) identified by FMEA only. When scored for RPN, the 13 events missed by FMEA ranked within the lower half of all failure modes and exhibited significantly lower severity relative to those identified by FMEA (p = 0.02). CONCLUSIONS FMEA, though valuable, is subject to certain limitations. In this study, FMEA failed to identify 17% of actual failure modes, though these were of lower risk. Similarly, an incident learning system alone fails to identify a large number of potentially high-severity process errors. Using FMEA in combination with incident learning may render an improved overview of risks within a process.

Validating FMEA output against incident learning data

PURPOSE Though failure mode and effects analysis (FMEA) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge, its output has never been validated against data on errors that actually occur. The objective of this study was to perform FMEA of a stereotactic body radiation therapy (SBRT) treatment planning process and validate the results against data recorded within an incident learning system. METHODS FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, dosimetrists, and IT technologists. Potential failure modes were identified through a systematic review of the process map. Failure modes were rated for severity, occurrence, and detectability on a scale of one to ten and risk priority number (RPN) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that has been active for two and a half years. Differences between FMEA anticipated failure modes and existing incidents were identified. RESULTS FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near-miss events related to SBRT planning. Combining both methods yielded a total of 76 possible process failures, of which 13 (17%) were missed by FMEA while 43 (57%) identified by FMEA only. When scored for RPN, the 13 events missed by FMEA ranked within the lower half of all failure modes and exhibited significantly lower severity relative to those identified by FMEA (p = 0.02). CONCLUSIONS FMEA, though valuable, is subject to certain limitations. In this study, FMEA failed to identify 17% of actual failure modes, though these were of lower risk. Similarly, an incident learning system alone fails to identify a large number of potentially high-severity process errors. Using FMEA in combination with incident learning may render an improved overview of risks within a process.

SU-E-T-288: Root Cause Analysis for a Wrong Isocenter Error.

PURPOSE This study is to explore root cause analysis (RCA) in the context of a departmental incident reporting system through a case report of a wrong isocenter. RCA of this incident provides a means to undercover and address underlying causal factors to prevent such an event in the future. METHODS The wrong isocenter error was detected on port films, making it a near-miss, but the potential impact was severe. Following the modified London Protocol for RCA, we interviewed all the staff involved, including the simulation and treatment therapists, the dosimetrist, the physicist and the physician. A timeline of events leading up to the incident and its discovery was created. We then generated a list of care delivery problems and associated contributing factors. Finally, solutions were identified to address the contributing factors and prevent future recurrences of such an incident. RESULTS Though not obvious at the outset of this RCA, the main care delivery problem turned out to be a misidentification of the ball-bearing (BB) location on the treatment planning CT. A metal wire used to mark a drain site was identified as an isocenter mark rather than the BB on the AP surface as intended. Contributing factors include: 1)no policy implemented regarding the location of BBs and metal wire; 2) wires with similar diameter and feature as BBs in CT images; 3)lack of communication when confusion and uncertainties exist. A solution was chosen to help visualize the drain site. When drain site needs to be marked, a marker different from the regular wire will be used (e.g. plastic washer). CONCLUSION Through RCA of an incorrect isocenter placement case, we identified the causal factors and also proposed solutions. RCA is a crucial tool for incident learning. This case study serves as valuable guidance for this particular problem and for RCA more generally.

SU-E-T-247: Multi-Leaf Collimator Model Adjustments Improve Small Field Dosimetry in VMAT Plans.

PURPOSE The Elekta beam modulator linac employs a 4-mm micro multileaf collimator (MLC) backed by a fixed jaw. Out-of-field dose discrepancies between treatment planning system (TPS) calculations and output water phantom measurements are caused by the 1-mm leaf gap required for all moving MLCs in a VMAT arc. In this study, MLC parameters are optimized to improve TPS out-of-field dose approximations. METHODS Static 2.4 cm square fields were created with a 1-mm leaf gap for MLCs that would normally park behind the jaw. Doses in the open field and leaf gap were measured with an A16 micro ion chamber and EDR2 film for comparison with corresponding point doses in the Pinnacle TPS. The MLC offset table and tip radius were adjusted until TPS point doses agreed with photon measurements. Improvements to the beam models were tested using static arcs consisting of square fields ranging from 1.6 to 14.0 cm, with 45° collimator rotation, and 1-mm leaf gap to replicate VMAT conditions. Gamma values for the 3-mm distance, 3% dose difference criteria were evaluated using standard QA procedures with a cylindrical detector array. RESULTS The best agreement in point doses within the leaf gap and open field was achieved by offsetting the default rounded leaf end table by 0.1 cm and adjusting the leaf tip radius to 13 cm. Improvements in TPS models for 6 and 10 MV photon beams were more significant for smaller field sizes 3.6 cm or less where the initial gamma factors progressively increased as field size decreased, i.e. for a 1.6cm field size, the Gamma increased from 56.1% to 98.8%. CONCLUSION The MLC optimization techniques developed will achieve greater dosimetric accuracy in small field VMAT treatment plans for fixed jaw linear accelerators. Accurate predictions of dose to organs at risk may reduce adverse effects of radiotherapy.