Jeffrey V. Siebers

A clinically observed discrepancy between image-based and log-based MLC positions

PURPOSE To present a clinical case in which real-time intratreatment imaging identified an multileaf collimator (MLC) leaf to be consistently deviating from its programmed and logged position by >1 mm. METHODS An EPID-based exit-fluence dosimetry system designed to prevent gross delivery errors was used to capture cine during treatment images. The author serendipitously visually identified a suspected MLC leaf displacement that was not otherwise detected. The leaf position as recorded on the EPID images was measured and log-files were analyzed for the treatment in question, the prior days treatment, and for daily MLC test patterns acquired on those treatment days. Additional standard test patterns were used to quantify the leaf position. RESULTS Whereas the log-file reported no difference between planned and recorded positions, image-based measurements showed the leaf to be 1.3 ± 0.1 mm medial from the planned position. This offset was confirmed with the test pattern irradiations. CONCLUSIONS It has been clinically observed that log-file derived leaf positions can differ from their actual position by >1 mm, and therefore cannot be considered to be the actual leaf positions. This cautions the use of log-based methods for MLC or patient quality assurance without independent confirmation of log integrity. Frequent verification of MLC positions through independent means is a necessary precondition to trust log-file records. Intratreatment EPID imaging provides a method to capture departures from MLC planned positions.

Coverage‐based treatment planning to accommodate deformable organ variations in prostate cancer treatment

PURPOSE To compare two coverage-based planning (CP) techniques with standard fixed margin-based planning (FM), considering the dosimetric impact of interfraction deformable organ motion exclusively for high-risk prostate treatments. METHODS Nineteen prostate cancer patients with 8-13 prostate CT images of each patient were used to model patient-specific interfraction deformable organ changes. The model was based on the principal component analysis (PCA) method and was used to predict the patient geometries for virtual treatment course simulation. For each patient, an IMRT plan using zero margin on target structures, prostate (CTVprostate) and seminal vesicles (CTVSV), were created, then evaluated by simulating 1000 30-fraction virtual treatment courses. Each fraction was prostate centroid aligned. Patients whose D98 failed to achieve 95% coverage probability objective D98,95 ≥ 78 Gy (CTVprostate) or D98,95 ≥ 66 Gy (CTVSV) were replanned using planning techniques: (1) FM (PTVprostate = CTVprostate + 5 mm, PTVSV = CTVSV + 8 mm), (2) CPOM which optimized uniform PTV margins for CTVprostate and CTVSV to meet the coverage probability objective, and (3) CPCOP which directly optimized coverage probability objectives for all structures of interest. These plans were intercompared by computing probabilistic metrics, including 5% and 95% percentile DVHs (pDVH) and TCP/NTCP distributions. RESULTS All patients were replanned using FM and two CP techniques. The selected margins used in FM failed to ensure target coverage for 8/19 patients. Twelve CPOM plans and seven CPCOP plans were favored over the other plans by achieving desirable D98,95 while sparing more normal tissues. CONCLUSIONS Coverage-based treatment planning techniques can produce better plans than FM, while relative advantages of CPOM and CPCOP are patient-specific.

Coverage-based treatment planning to accommodate delineation uncertainties in prostate cancer treatment.

PURPOSE To compare two coverage-based planning (CP) techniques with fixed margin-based (FM) planning for high-risk prostate cancer treatments, with the exclusive consideration of the dosimetric impact of delineation uncertainties of target structures and normal tissues. METHODS In this work, 19-patient data sets were involved. To estimate structure dose for each delineated contour under the influence of interobserver contour variability and CT image quality limitations, 1000 alternative structures were simulated by an average-surface-of-standard-deviation model, which utilized the patient-specific information of delineated structure and CT image contrast. An IMRT plan with zero planning-target-volume (PTV) margin on the delineated prostate and seminal vesicles [clinical-target-volume (CTV prostate) and CTVSV] was created and dose degradation due to contour variability was quantified by the dosimetric consequences of 1000 alternative structures. When D98 failed to achieve a 95% coverage probability objective D98,95 ≥ 78 Gy (CTV prostate) or D98,95 ≥ 66 Gy (CTVSV), replanning was performed using three planning techniques: (1) FM (PTV prostate margin = 4,5,6 mm and PTVSV margin = 4,5,7 mm for RL, PA, and SI directions, respectively), (2) CPOM which optimized uniform PTV margins for CTV prostate and CTVSV to meet the D98,95 objectives, and (3) CPCOP which directly optimized coverage-based objectives for all the structures. These plans were intercompared by computing percentile dose-volume histograms and tumor-control probability/normal tissue complication probability (TCP/NTCP) distributions. RESULTS Inherent contour variability resulted in unacceptable CTV coverage for the zero-PTV-margin plans for all patients. For plans designed to accommodate contour variability, 18/19 CP plans were most favored by achieving desirable D98,95 and TCP/NTCP values. The average improvement of probability of complication free control was 9.3% for CPCOP plans and 3.4% for CPOM plans. CONCLUSIONS When the delineation uncertainties need to be considered for prostate patients, CP techniques can produce more desirable plans than FM plans for most patients. The relative advantages between CPCOP and CPOM techniques are patient specific.

Quantitative ionization chamber alignment to a water surface: theory and simulation

Purpose To examine the response properties of cylindrical cavity ionization chambers (ICs) in the depth‐ionization buildup region so as to obtain a robust chamber‐signal — based method for definitive water surface identification, hence absolute ionization chamber depth localization. Method & materials An analytical model with simplistic physics and geometry is developed to explore the theoretical aspects of ionization chamber response near a phantom water surface. Monte Carlo simulations with full physics and ionization chamber geometry are utilized to extend the models findings to realistic ion chambers in realistic beams and to study the effects of IC design parameters on the entrance dose response. Design parameters studied include full and simplified IC designs with varying central electrode thickness, wall thickness, and outer chamber radius. Piecewise continuous fits to the depth‐ionization signal gradient are used to quantify potential deviation of the gradient discontinuity from the chamber outer radius. Exponential, power, and hyperbolic sine functional forms are used to model the gradient for chamber depths of zero to the depth of the gradient discontinuity. Results The depth‐ionization gradient as a function of depth is maximized and discontinuous when a submerged ICs outer radius coincides with the water surface. We term this depth the gradient chamber alignment point (gCAP). The maximum deviation between the gCAP location and the chamber outer radius is 0.13 mm for a hypothetical 4 mm thick wall, 6.45 mm outer radius chamber using the power function fit, however, the chamber outer radius is within the 95% confidence interval of the gCAP determined by this fit. gCAP dependence on the chamber wall thickness is possible, but not at a clinically relevant level. Conclusions The depth‐ionization gradient has a discontinuity and is maximized when the outer‐radius of a submerged IC coincides with the water surface. This feature can be used to auto‐align ICs to the water surface at the time of scanning and/or be applied retrospectively to scan data to quantify absolute IC depth. Utilization of the gCAP should yield accurate and reproducible depth calibration for clinical depth‐ionization measurements between setups and between users.

Clinical adequacy assessment of autocontours for prostate IMRT with meaningful endpoints

Purpose To determine if radiation treatment plans created based on autosegmented (AS) regions‐of‐interest (ROI)s are clinically equivalent to plans created based on manually segmented ROIs, where equivalence is evaluated using probabilistic dosimetric metrics and probabilistic biological endpoints for prostate IMRT. Method and materials Manually drawn contours and autosegmented ROIs were created for 167 CT image sets acquired from 19 prostate patients. Autosegmentation was performed utilizing Pinnacles Smart Probabilistic Image Contouring Engine. For each CT set, 78 Gy/39 fraction 7‐beam IMRT treatment plans with 1 cm CTV‐to‐PTV margins were created for each of the three contour scenarios; PMD using manually delineated (MD) ROIs, PAS using autosegmented ROIs, and PAM using autosegmented organ‐at‐risks (OAR)s and the manually drawn target. For each plan, 1000 virtual treatment simulations with different systematic errors for each simulation and a different random error for each fraction were performed. The statistical probability of achieving dose–volume metrics (coverage probability (CP)), expectation values for normal tissue complication probability (NTCP), and tumor control probability (TCP) metrics for all possible cross‐evaluation pairs of ROI types and planning scenarios were reported. In evaluation scenarios, the root mean square loss (RMSL) and maximum absolute loss (MAL) of coverage probability of dose–volume objectives, E[TCP], and E[NTCP] were compared with respect to the base plan created and evaluated with manually drawn contours. Results Femoral head dose objectives were satisfied in all situations, as well as the maximum dose objectives for all ROIs. Bladder metrics were within the clinical coverage tolerances except D35Gy for the autosegmented plan evaluated with the manual contours. Dosimetric indices for CTV and rectum could be highly compromised when the definition of the ROIs switched from manually delineated to autosegmented. Seventy‐two percent of CT image sets satisfied the worst‐case CP thresholds for all dosimetric objectives in all scenarios, the percentage dropped to 50% if biological indices were taken into account. Among evaluation scenarios, (MD,PAM) bore the highest resemblance to (MD,PMD) where 99% and 88% of cases met all CP thresholds for bladder and rectum, respectively. Conclusions When including daily setup variations in prostate IMRT, the dose–volume metric CP, and biological indices of ROIs were approximately equivalent for the plans created based on manually drawn targets and autosegmented OARs in 88% of cases. The accuracy of autosegmented prostates and rectums are impediment to attain statistically equivalent plans created based on manually drawn ROIs.

Quantitative ionization chamber alignment to a water surface: Performance of multiple chambers

Purpose The purpose of this study was to experimentally examine the reliability of the gradient chamber alignment point (gCAP) determination method for accurately identifying water surface location with a range of ionization chambers (ICs). Materials and methods Twelve cylindrical ICs were scanned from depth through a water surface into air using a customized high‐accuracy scanning system which allows for accurate alignment of the IC with respect to the true water surface. Thirteen other cylindrical ICs and five parallel‐plate ICs were scanned using a standard commercially available scanning system. The thirty different ICs used in this study represent 22 different IC models. Measurements were taken with different radiation field parameters such as incident photon beam energies and field sizes. The effects of scan direction and water surface tension were also investigated. The depth at which the gradient of the relative ionization was maximized and discontinuous, the gCAP, was found for each curve. Each measured gCAP depth was compared with the theoretically expected gCAP location, the depth at which the submerged IC outer radius (OR) coincides with the water surface. Results When scanning an IC from in water to air, the only parameter that affects the gCAP location is the IC OR. The gCAP location corresponds with the IC central axis positioned at a depth equal to the IC OR within the 0.1 mm measurement scan resolution for all eighteen ICs studied with the commercially available system. Using the customized scanning system, all but three ICs were identified exhibiting a gCAP within the scan resolution, with the other three within 0.25 mm of the expected location. This discrepancy was not observed in the same IC model when using the conventional scanning system. Altering the beam energy from 6 to 25 MV did not alter the gCAP location, nor did variations in the radiation field size or scan parameters. In‐air IC response is proportional to the IC wall thickness. Conclusion The water‐to‐air scanning method coupled with gCAP analysis identifies the alignment of the IC OR to the water surface within the scanning resolution for all ICs studied. The gCAP method can precisely and reproducibly align the physical center of a given cylindrical IC with the water surface, be applied prospectively or retrospectively, and provides the prospect for automated water surface identification for scanning systems. The gCAP method eliminates the visual subjectivity inherent to current IC‐to‐water surface alignment techniques, has been validated with a wide variety of commercially available ICs, and should be independent of the scanning system used for data acquisition.

Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach

PURPOSE To develop a quality assurance (QA) tool that identifies inaccurate organ at risk (OAR) delineations. METHODS The QA tool computed volumetric features from prior OAR delineation data from 73 thoracic patients to construct a reference database. All volumetric features of the OAR delineation are computed in three-dimensional space. Volumetric features of a new OAR are compared with respect to those in the reference database to discern delineation outliers. A multicriteria outlier detection system warns users of specific delineation outliers based on combinations of deviant features. Fifteen independent experimental sets including automatic, propagated, and clinically approved manual delineation sets were used for verification. The verification OARs included manipulations to mimic common errors. Three experts reviewed the experimental sets to identify and classify errors, first without; and then 1 week after with the QA tool. RESULTS In the cohort of manual delineations with manual manipulations, the QA tool detected 94% of the mimicked errors. Overall, it detected 37% of the minor and 85% of the major errors. The QA tool improved reviewer error detection sensitivity from 61% to 68% for minor errors (P = 0.17), and from 78% to 87% for major errors (P = 0.02). CONCLUSIONS The QA tool assists users to detect potential delineation errors. QA tool integration into clinical procedures may reduce the frequency of inaccurate OAR delineation, and potentially improve safety and quality of radiation treatment planning.

Dose to mass for evaluation and optimization of lung cancer radiation therapy

PURPOSE To evaluate potential organ at risk dose-sparing by using dose-mass-histogram (DMH) objective functions compared with dose-volume-histogram (DVH) objective functions. METHODS Treatment plans were retrospectively optimized for 10 locally advanced non-small cell lung cancer patients based on DVH and DMH objectives. DMH-objectives were the same as DVH objectives, but with mass replacing volume. Plans were normalized to dose to 95% of the PTV volume (PTV-D95v) or mass (PTV-D95m). For a given optimized dose, DVH and DMH were intercompared to ascertain dose-to-volume vs. dose-to-mass differences. Additionally, the optimized doses were intercompared using DVH and DMH metrics to ascertain differences in optimized plans. Mean dose to volume, Dv‾, mean dose to mass, DM‾, and fluence maps were intercompared. RESULTS For a given dose distribution, DVH and DMH differ by >5% in heterogeneous structures. In homogeneous structures including heart and spinal cord, DVH and DMH are nearly equivalent. At fixed PTV-D95v, DMH-optimization did not significantly reduce dose to OARs but reduced PTV-Dv‾ by 0.20±0.2Gy (p=0.02) and PTV-DM‾ by 0.23±0.3Gy (p=0.02). Plans normalized to PTV-D95m also result in minor PTV dose reductions and esophageal dose sparing (Dv‾ reduced 0.45±0.5Gy, p=0.02 and DM‾ reduced 0.44±0.5Gy, p=0.02) compared to DVH-optimized plans. Optimized fluence map comparisons indicate that DMH optimization reduces dose in the periphery of lung PTVs. CONCLUSIONS DVH- and DMH-dose indices differ by >5% in lung and lung target volumes for fixed dose distributions, but optimizing DMH did not reduce dose to OARs. The primary difference observed in DVH- and DMH-optimized plans were variations in fluence to the periphery of lung target PTVs, where low density lung surrounds tumor.

WE-AB-209-12: Quasi Constrained Multi-Criteria Optimization for Automated Radiation Therapy Treatment Planning

PURPOSE To introduce quasi-constrained Multi-Criteria Optimization (qcMCO) for unsupervised radiation therapy optimization which generates alternative patient-specific plans emphasizing dosimetric tradeoffs and conformance to clinical constraints for multiple delivery techniques. METHODS For N Organs At Risk (OARs) and M delivery techniques, qcMCO generates M(N+1) alternative treatment plans per patient. Objective weight variations for OARs and targets are used to generate alternative qcMCO plans. For 30 locally advanced lung cancer patients, qcMCO plans were generated for dosimetric tradeoffs to four OARs: each lung, heart, and esophagus (N=4) and 4 delivery techniques (simple 4-field arrangements, 9-field coplanar IMRT, 27-field non-coplanar IMRT, and non-coplanar Arc IMRT). Quasi-constrained objectives included target prescription isodose to 95% (PTV-D95), maximum PTV dose (PTV-Dmax)< 110% of prescription, and spinal cord Dmax<45 Gy. The algorithms ability to meet these constraints while simultaneously revealing dosimetric tradeoffs was investigated. Statistically significant dosimetric tradeoffs were defined such that the coefficient of determination between dosimetric indices which varied by at least 5 Gy between different plans was >0.8. RESULTS The qcMCO plans varied mean dose by >5 Gy to ipsilateral lung for 24/30 patients, contralateral lung for 29/30 patients, esophagus for 29/30 patients, and heart for 19/30 patients. In the 600 plans computed without human interaction, average PTV-D95=67.4±3.3 Gy, PTV-Dmax=79.2±5.3 Gy, and spinal cord Dmax was >45 Gy in 93 plans (>50 Gy in 2/600 plans). Statistically significant dosimetric tradeoffs were evident in 19/30 plans, including multiple tradeoffs of at least 5 Gy between multiple OARs in 7/30 cases. The most common statistically significant tradeoff was increasing PTV-Dmax to reduce OAR dose (15/30 patients). CONCLUSION The qcMCO method can conform to quasi-constrained objectives while revealing significant variations in OAR doses including mean dose reductions >5 Gy. Clinical implementation will facilitate patient-specific decision making based on achievable dosimetry as opposed to accept/reject models based on population derived objectives.

SU-C-BRB-05: Determining the Adequacy of Auto-Contouring Via Probabilistic Assessment of Ensuing Treatment Plan Metrics in Comparison with Manual Contours

PURPOSE To determine if auto-contour and manual-contour-based plans differ when evaluated with respect to probabilistic coverage metrics and biological model endpoints for prostate IMRT. METHODS Manual and auto-contours were created for 149 CT image sets acquired from 16 unique prostate patients. A single physician manually contoured all images. Auto-contouring was completed utilizing Pinnacles Smart Probabilistic Image Contouring Engine (SPICE). For each CT, three different 78 Gy/39 fraction 7-beam IMRT plans are created; PD with drawn ROIs, PAS with auto-contoured ROIs, and PM with auto-contoured OARs with the manually drawn target. For each plan, 1000 virtual treatment simulations with different sampled systematic errors for each simulation and a different sampled random error for each fraction were performed using our in-house GPU-accelerated robustness analyzer tool which reports the statistical probability of achieving dose-volume metrics, NTCP, TCP, and the probability of achieving the optimization criteria for both auto-contoured (AS) and manually drawn (D) ROIs. Metrics are reported for all possible cross-evaluation pairs of ROI types (AS,D) and planning scenarios (PD,PAS,PM). Bhattacharyya coefficient (BC) is calculated to measure the PDF similarities for the dose-volume metric, NTCP, TCP, and objectives with respect to the manually drawn contour evaluated on base plan (D-PD). RESULTS We observe high BC values (BC≥0.94) for all OAR objectives. BC values of max dose objective on CTV also signify high resemblance (BC≥0.93) between the distributions. On the other hand, BC values for CTVs D95 and Dmin objectives are small for AS-PM, AS-PD. NTCP distributions are similar across all evaluation pairs, while TCP distributions of AS-PM, AS-PD sustain variations up to %6 compared to other evaluated pairs. CONCLUSION No significant probabilistic differences are observed in the metrics when auto-contoured OARs are used. The prostate auto-contour needs improvement to achieve clinically equivalent plans.