Quantitative assessment of dosimetric effect of using alternative OAR delineations in treatment planning as functions of delineations, setup uncertainty and planning techniques using an alternative truth assessment method.

PURPOSE: This study aims to quantify the variation in dose-volume histogram (DVH) and normal tissue complication probability(NTCP) metrics for head-and-neck (HN) cancer patients when alternative organ-at-risk(OAR) delineations are used for treatment planning and for treatment plan evaluation. We particularly focus on the effects of daily patient positioning/setup variations(SV) in relation to treatment technique and delineation variability. MATERIALS AND METHODS: We generated two-arc VMAT, 5-beam IMRT, and 9-beam IMRT treatment plans for a cohort of 209 HN patients. These plans incorporated five different OAR delineation sets, including manual and four automated algorithms. Each treatment plan was assessed under various simulated per-fraction patient setup uncertainties, evaluating the potential clinical impacts through DVH and NTCP metrics. RESULTS: The study demonstrates that increasing SV generally reduces differences in DVH metrics between alternative delineations. However, in contrast, differences in NTCP metrics tend to increase with higher setup variability. This pattern is observed consistently across different treatment plans and delineator combinations, illustrating the intricate relationship between SV and delineation accuracy. Additionally, the need for delineation accuracy in treatment planning is shown to be case-specific and dependent on factors beyond geometric variations. CONCLUSIONS: The findings highlight the necessity for comprehensive Quality Assurance programs in radiotherapy, incorporating both dosimetric impact analysis and geometric variation assessment to ensure optimal delineation quality. The study emphasizes the complex dynamics of treatment planning in radiotherapy, advocating for personalized, case-specific strategies in clinical practice to enhance patient care quality and efficacy in the face of varying SV and delineation accuracies.

OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines.

Objective.To establish an open framework for developing plan optimization models for knowledge-based planning (KBP).Approach.Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models.Main results.The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P< 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model.Significance.This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.

AAPM WGTG51 Report 374: Guidance for TG-51 reference dosimetry.

Practical guidelines that are not explicit in the TG-51 protocol and its Addendum for photon beam dosimetry are presented for the implementation of the TG-51 protocol for reference dosimetry of external high-energy photon and electron beams. These guidelines pertain to: (i) measurement of depth-ionization curves required to obtain beam quality specifiers for the selection of beam quality conversion factors, (ii) considerations for the dosimetry system and specifications of a reference-class ionization chamber, (iii) commissioning a dosimetry system and frequency of measurements, (iv) positioning/aligning the water tank and ionization chamber for depth ionization and reference dose measurements, (v) requirements for ancillary equipment needed to measure charge (triaxial cables and electrometers) and to correct for environmental conditions, and (vi) translation from dose at the reference depth to that at the depth required by the treatment planning system. Procedures are identified to achieve the most accurate results (errors up to 8% have been observed) and, where applicable, a commonly used simplified procedure is described and the impact on reference dosimetry measurements is discussed so that the medical physicist can be informed on where to allocate resources.

Knowledge-based quality control of organ delineations in radiation therapy.

PURPOSE: To reduce the likelihood of errors in organ delineations used for radiotherapy treatment planning, a knowledge-based quality control (KBQC) system, which discriminates between valid and anomalous delineations is developed. METHOD AND MATERIALS: The KBQC is comprised of a group-wise inference system and anomaly detection modules trained using historical priors from 296 locally advanced lung and prostate cancer patient computational tomographies (CTs). The inference system discriminates different organs based on shape, relational, and intensity features. For a given delineated image set, the inference system solves a combinatorial optimization problem that results in an organ group whose relational features follow those of the training set considering the posterior probabilities obtained from support vector machine (SVM), discriminant subspace ensemble (DSE), and artificial neural network (ANN) classifiers. These classifiers are trained on nonrelational features with a 10-fold cross-validation scheme. The anomaly detection module is a bank of ANN autoencoders, each corresponding with an organ, trained on nonrelational features. A heuristic rule detects anomalous organs that exceed predefined organ-specific tolerances for the feature reconstruction error and the classifier's posterior probabilities. Independent data sets with anomalous delineations were used to test the overall performance of the KBQC system. The anomalous delineations were manually manipulated, computer-generated, or propagated based on a transformation obtained by imperfect registrations. Both peer-review-based scoring system and shape similarity coefficient (DSC) were used to label regions of interest (ROIs) as normal or anomalous in two independent test cohorts. RESULTS: The accuracy of the classifiers was ≥ $\ge$ 99.8%, and the minimum per-class F1-scores were 0.99, 0.99, and 0.98 for SVM, DSE, and ANN, respectively. The group-wise inference system reduced the miss-classification likelihood for the test data set with anomalous delineations compared to each individual classifier and a fused classifier that used the average posterior probability of all classifiers. For 15 independent locally advanced lung patients, the system detected > $>$ 79% of the anomalous ROIs. For 1320 auto-segmented abdominopelvic organs, the anomaly detection system identified anomalous delineations, which also had low Dice similarity coefficient values with respect to manually delineated organs in the training data set. CONCLUSION: The KBQC system detected anomalous delineations with superior accuracy compared to classification methods that judge only based on posterior probabilities.

A regression-based approach to compute the pixels sensitivity map of linear accelerator portal imaging devices.

PURPOSE: To determine the pixel sensitivity map (PSM) for amorphous silicon electronic portal imaging devices (EPIDs) using a single flood field signal. METHOD AND MATERIALS: A raw EPID signal results from the incident particle energy fluence, the inherent pixels response, and the background signal. In large open fields, particle energy fluence is a slow-varying signal that is locally considered spatially constant. Pixels response is a fast and abrupt varying behavior. The background signal is due to the EPID panel electronics, which is determined during radiation absence. To determine the PSM, after correcting for the background signal, we apply a model that captures the underlying smooth particle energy fluence-induced signal. This fluence signal-fitted model is then used to determine the PSM. Here, we use a polynomial-based regression surface model in both x and y dimensions. To validate the generated PSM, we measure beams and compute PSMs for multiple beam energies with and without flattening filters and for multiple source-to-imager distances. Since the PSM is a detector characteristic, it should be independent of those variables. We also intercompare measurements of fixed slit fields with the EPID being shifted between measurements. RESULTS: The fluence signal of the flattening filter-free (FFF) beams was optimally modeled as a 12th degree polynomial surfaces, which had ≤ 0.1% residuals near the central axis. The 6 and 10 MV FFF PSMs were within ˜0.1%, and independent of the EPID SID, suggesting that the PSM is energy independent. The 6, 10, and 15 MV flattened-beam PSMs were well modeled as 12th degree polynomial surfaces, which were equivalent within ˜0.24% but differed from the FFF PSM by up to 0.5% near the beam central axis. Applying the FFF PSMs to the flattened-beam measurements reduced the central-axis deviation between the raw and corrected signal to < 0.1%, confirming the PSM energy independence hypothesis. When the FFF PSM is utilized, output verification with shifted slit deliveries agreed within ˜0.5% for all beam energies, which is within the radiation delivery uncertainty of ˜0.57%. CONCLUSION: PSM for MV EPIDs can be determined by separating out the slowly varying, well-behaved fluence signal from the pixel-to-pixel sensitivity variations. The quality of the PSM is found to be dependent on the quality of the surface fit, which is best for the 6 MV FFF beam measured at SID equal to 180 cm. Within fitting errors, the PSM is independent of beam energy for 6, 10, and 15 MV beams with and without flattening filters. The PSM generation does not require shifting the EPID panel nor multiple EPID panel irradiations and should be usable for linacs with fixed geometry EPIDs.

An error detection method for real-time EPID-based treatment delivery quality assurance.

PURPOSE: To quantify the error detection power of a new treatment delivery error detection method. The method validates monitor unit (MU) resolved beam apertures using real-time EPID images. METHODS: The on-board EPID imager was used to measure cine-EPID (~10 Hz) images for 27 beams from 15 VMAT/SBRT clinical treatment plans and five nonclinical plans. For each frame acquisition, planned apertures were interpolated from the treatment plan multileaf collimator (MLC) positions expected during the frame acquisition interval. Inaccurate deliveries were identified by monitoring in-aperture missed fluence and out-of-aperture excess fluence beyond a specified buffer. Delivery errors were simulated by perturbing the planned MLC positions before comparison with nonperturbed measured apertures. Systematic 1-5 mm MLC leaf shifts were used to train a logistic regression model to determine the error detection threshold. Model accuracy was monitored using tenfold cross-validation. The model's error detection ability was tested with other error modes: plan control point (CP) weight perturbations, collimator rotations, random MLC leaf position errors, EPID imager shift, and stuck MLC leaf. The error detection accuracy was evaluated using the Matthews correlation coefficient (MCC) and the false positive rate (FPR). Per-beam error thresholds of >1, >5, and >10% errant frames were tested to label per-beam errors. The model also was tested for its ability to distinguish five cases with highly similar plans and compared with gamma analysis. RESULTS: Delivery errors were detected by monitoring intended per-frame images with a 2 mm MLC buffer. Frame-by-frame aperture errors were identified with an optimal threshold of 0.3% of the expected aperture area. The per-frame FPR was 0.02%. The MCC was 1.00 (perfect classification) for detection based on 1% of frames for random CP weight shift, 3 mm random MLC shifts, 90° and 180° collimator rotations, and an MLC leaf stuck after 10% of the beam delivery. The MCC for 2°, 4°, and 8° collimator rotation were 0.53, 0.76, and 0.96, respectively, for the 1% of beam delivery threshold. The 3 mm EPID shift had poor detection, with a minimum MCC of 0.14. The highly similar plans were reliably detected by the aperture check but were not detectable with gamma analysis. CONCLUSION: The high error detection sensitivity and low FPR makes the aperture check error detection method well suited to pretreatment and during-treatment beam delivery quality assurance (QA). The aperture check detects subtle beam delivery errors, including those resulting from MLC leaf positioning deviations, CP MU shifts, and stuck MLC leaves. Furthermore, the method can distinguish between highly similar treatment plans. Since the aperture check method monitors for the aperture shapes over a given MU interval, it is also sensitive to errors in MU per CP, without requiring dosimetric calibration of the EPID. The aperture check is one part of a Swiss cheese error detection scheme, which provides redundant error testing of multiple error modes, including nonaperture related errors. The rapid error detection, at 1% of a beam's delivery, make the aperture check a potential candidate for QA of on-line adaptive radiotherapy, or other situations in which pretreatment delivery QA is impractical.

An Automated Workflow to Improve Efficiency in Radiation Therapy Treatment Planning by Prioritizing Organs at Risk.

PURPOSE: Manual delineation (MD) of organs at risk (OAR) is time and labor intensive. Auto-delineation (AD) can reduce the need for MD, but because current algorithms are imperfect, manual review and modification is still typically used. Recognizing that many OARs are sufficiently far from important dose levels that they do not pose a realistic risk, we hypothesize that some OARs can be excluded from MD and manual review with no clinical effect. The purpose of this study was to develop a method that automatically identifies these OARs and enables more efficient workflows that incorporate AD without degrading clinical quality. METHODS AND MATERIALS: Preliminary dose map estimates were generated for n = 10 patients with head and neck cancers using only prescription and target-volume information. Conservative estimates of clinical OAR objectives were computed using AD structures with spatial expansion buffers to account for potential delineation uncertainties. OARs with estimated dose metrics below clinical tolerances were deemed low priority and excluded from MD and/or manual review. Final plans were then optimized using high-priority MD OARs and low-priority AD OARs and compared with reference plans generated using all MD OARs. Multiple different spatial buffers were used to accommodate different potential delineation uncertainties. RESULTS: Sixty-seven out of 201 total OARs were identified as low-priority using the proposed methodology, which permitted a 33% reduction in structures requiring manual delineation/review. Plans optimized using low-priority AD OARs without review or modification met all planning objectives that were met when all MD OARs were used, indicating clinical equivalence. CONCLUSIONS: Prioritizing OARs using estimated dose distributions allowed a substantial reduction in required MD and review without affecting clinically relevant dosimetry.

Multiobjective, Multidelivery Optimization for Radiation Therapy Treatment Planning.

PURPOSE: To introduce multiobjective, multidelivery optimization (MODO), which generates alternative patient-specific plans emphasizing dosimetric trade-offs and conformance to quasi-constrained (QC) conditions for multiple delivery techniques. METHODS AND MATERIALS: For M delivery techniques and N organs at risk (OARs), MODO generates M (N + 1) alternative treatment plans per patient. For 30 locally advanced lung cancer cases, the algorithm was investigated based on dosimetric trade-offs to 4 OARs: each lung, heart, and esophagus (N = 4) and 4 delivery techniques (4-field coplanar intensity modulated radiation therapy [IMRT], 9-field coplanar IMRT, 27-field noncoplanar IMRT, and noncoplanar arc IMRT) and conformance to QC conditions, including dose to 95% (D95) of the planning target volume (PTV), maximum dose (Dmax) to PTV (PTV-Dmax), and spinal cord Dmax. The MODO plan set was evaluated for conformance to QC conditions while simultaneously revealing dosimetric trade-offs. Statistically significant dosimetric trade-offs were defined such that the coefficient of determination was >0.8 with dosimetric indices that varied by at least 5 Gy. RESULTS: Plans varied mean dose by >5 Gy to ipsilateral lung for 24 of 30 patients, contralateral lung for 29 of 30 patients, esophagus for 29 of 30 patients, and heart for 19 of 30 patients. In the 600 plans, average PTV-D95 = 67.6 ± 2.1 Gy, PTV-Dmax = 79.8 ± 5.2 Gy, and spinal cord Dmax among all plans was 51.4 Gy. Statistically significant dosimetric trade-offs reducing OAR mean dose by >5 Gy were evident in 19 of 30 patients, including multiple OAR trade-offs of at least 5 Gy in 7 of 30 cases. The most common statistically significant trade-off was increasing PTV-Dmax to reduce dose to OARs (15 of 30). The average 4-field plan reduced total lung V20 by 10.4% ± 8.3% compared with 9-field plans, 7.7% ± 7.9% compared with 27-field noncoplanar plans, and 11.7% ± 10.3% compared with 2-arc noncoplanar plans, with corresponding increases in PTV-Dmax of 5.3 ± 5.9 Gy, 4.6 ± 5.6 Gy, and 9.3 ± 7.3 Gy. CONCLUSIONS: The proposed optimization method produces clinically relevant treatment plans that meet QC conditions and demonstrate variations in OAR doses.

Dose escalation in the definite target volume.

PURPOSE: To introduce the definite target volume (DTV) and evaluate dosimetric consequences of boosting dose to this region of high clinical target volume (CTV)- and low organs at risk (OAR)-probability. METHODS: This work defines the DTV via occupancy probability and via contraction of the CTV by margin M less any planning risk volume (PRV) volumes. The equivalence to within varying occupancy probability of the two methods is established for spherical target volumes. We estimate a margin for four radiation treatment sites based on modern images guided radiation therapy-literature utilizing repeat volumetric imaging. Based on margins and patient-specific DTV targets, the ability to dose escalate the DTV including the effects of spatial uncertainty was evaluated. We simulate delivery assuming violation of the underlying spatial uncertainty of 130%. RESULTS: Contracting the planning target volume (PTV) by M and excluding PRV volumes, the DTV ranged from 7.3 to 93.6 cc. In a brain treatment, DTV-Dmax increased to 66.8 Gy (145% of prescription isodose); in advanced lung DTV-Dmax increased to 122.2 Gy (204% of prescription isodose), in a pancreatic case DTV-Dmax was boosted up to 87.3 Gy (173% or prescription isodose), and in retroperitoneal sarcoma to 74.6 Gy (249% of prescription isodose). The high point doses were not associated with increased dose to OARs, even when considering the effects of spatial uncertainty. Simulated delivery at 130% of assumed spatial uncertainties revealed DTV-based planning can result in minor increases in OAR Dmean/Dmax of 2.7 ± 2.1 Gy/1.8 ± 2.2 Gy with duodenum Dmax > 110% of prescription isodose in the pancreatic case. These dose increases were consistent with simulation of clinical, homogenous PTV-dose distributions. CONCLUSION: We have proposed and tested a method to deliver extremely high doses to subvolumes of target volumes in multiple treatment sites by defining a new target volume, the DTV. Based on simulated delivery, the method does not result in significant increases in dose to OARs if spatial uncertainty can be estimated.

Beam modeling and beam model commissioning for Monte Carlo dose calculation-based radiation therapy treatment planning: Report of AAPM Task Group 157.

Dose calculation plays an important role in the accuracy of radiotherapy treatment planning and beam delivery. The Monte Carlo (MC) method is capable of achieving the highest accuracy in radiotherapy dose calculation and has been implemented in many commercial systems for radiotherapy treatment planning. The objective of this task group was to assist clinical physicists with the potentially complex task of acceptance testing and commissioning MC-based treatment planning systems (TPS) for photon and electron beam dose calculations. This report provides an overview on the general approach of clinical implementation and testing of MC-based TPS with a specific focus on models of clinical photon and electron beams. Different types of beam models are described including those that utilize MC simulation of the treatment head and those that rely on analytical methods and measurements. The trade-off between accuracy and efficiency in the various source-modeling approaches is discussed together with guidelines for acceptance testing of MC-based TPS from the clinical standpoint. Specific recommendations are given on methods and practical procedures to commission clinical beam models for MC-based TPS.

Quantifying the dosimetric impact of organ-at-risk delineation variability in head and neck radiation therapy in the context of patient setup uncertainty.

The purpose of this study was to quantify the potential dosimetric impact of delineation variability (DV) in head and neck radiation therapy (RT) when inherent patient setup variability (SV) is also considered. The impact of DV was assessed by generating plans with multiple structure sets, cross-evaluating them, including SV, across sets, and determining P PQM: the probability of achieving organ-specific plan quality metrics (PQM). DV was incorporated by: (1) using multiple organ at risk (OAR) structure sets delineated by independent manual observers; and (2) randomly perturbing manually generated OARs to generate alternatives with varying levels of uncertainty (low, medium, and high DV). For each structure set, independent VMAT plans were auto-generated to meet clinical PQMs. Each plan was cross-evaluated using OARs from multiple structure sets with simulated SV including per-fraction random (σ s) and per-treatment-course systematic (Σs) setup errors. The dosimetric impact of DV was assessed by examining P PQM with and without SV/DV. Clinically significant differences were defined by those that exceeded differences caused by a +2% output variation. Without including SV, simulated DV at the medium level reduced P PQM by an average of 5.5% for all OARs with D max PQMs. This reduction decreased to 2.8% for SV = 2 mm and 2.4% for SV = 4 mm (the average P PQM reduction due to 2% output errors was 2.7%). For OARs with D mean PQMs, the average P PQM reduction was 0.9% for SV = 0 and ⩽0.1% for SV ⩾ 2 mm. The effect of DV was larger for OARs that directly abutted a target volume than for those that did not. These trends were also observed with real DV from multi-observer delineations. The dosimetric impact of DV appeared to decrease when random and systematic SV was considered. Sensitivity to DV was affected by OAR objective type (i.e. D mean versus D max objectives) as well as distance from the target volume.

Robust radiotherapy planning.

Motion and uncertainty in radiotherapy is traditionally handled via margins. The clinical target volume (CTV) is expanded to a larger planning target volume (PTV), which is irradiated to the prescribed dose. However, the PTV concept has several limitations, especially in proton therapy. Therefore, robust and probabilistic optimization methods have been developed that directly incorporate motion and uncertainty into treatment plan optimization for intensity modulated radiotherapy (IMRT) and intensity modulated proton therapy (IMPT). Thereby, the explicit definition of a PTV becomes obsolete and treatment plan optimization is directly based on the CTV. Initial work focused on random and systematic setup errors in IMRT. Later, inter-fraction prostate motion and intra-fraction lung motion became a research focus. Over the past ten years, IMPT has emerged as a new application for robust planning methods. In proton therapy, range or setup errors may lead to dose degradation and misalignment of dose contributions from different beams - a problem that cannot generally be addressed by margins. Therefore, IMPT has led to the first implementations of robust planning methods in commercial planning systems, making these methods available for clinical use. This paper first summarizes the limitations of the PTV concept. Subsequently, robust optimization methods are introduced and their applications in IMRT and IMPT planning are reviewed.

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.

Beyond the margin recipe: the probability of correct target dosage and tumor control in the presence of a dose limiting structure.

In the past, hypothetical spherical target volumes and ideally conformal dose distributions were analyzed to establish the safety of planning target volume (PTV) margins. In this work we extended these models to estimate how alternative methods of shaping dose distributions could lead to clinical improvements. Based on a spherical clinical target volume (CTV) and Gaussian distributions of systematic and random geometrical uncertainties, idealized 3D dose distributions were optimized to exhibit specific stochastic properties. A nearby spherical organ at risk (OAR) was introduced to explore the benefit of non-spherical dose distributions. Optimizing for the same minimum dose safety criterion as implied by the generally accepted use of a PTV, the extent of the high dose region in one direction could be reduced by half provided that dose in other directions is sufficiently compensated. Further reduction of this unilateral dosimetric margin decreased the target dose confidence, however the actual minimum CTV dose at 90% confidence typically exceeded the minimum PTV dose by 20% of prescription. Incorporation of smooth dose-effect relations within the optimization led to more concentrated dose distributions compared to the use of a PTV, with an improved balance between the probability of tumor cell kill and the risk of geometrical miss, and lower dose to surrounding tissues. Tumor control rate improvements in excess of 20% were found to be common for equal integral dose, while at the same time evading a nearby OAR. These results were robust against uncertainties in dose-effect relations and target heterogeneity, and did not depend on 'shoulders' or 'horns' in the dose distributions.

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 model's 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 IC's 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.

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.

Insight gained from responses to surveys on reference dosimetry practices.

PURPOSE: To present the results and discuss potential insights gained through surveys on reference dosimetry practices. METHODS: Two surveys were sent to medical physicists to learn about the current state of reference dosimetry practices at radiation oncology clinics worldwide. A short survey designed to maximize response rate was made publicly available and distributed via the AAPM website and a medical physics list server. Another, much more involved survey, was sent to a smaller group of physicists to gain insight on detailed dosimetry practices. The questions were diverse, covering reference dosimetry practices on topics like measurements required for beam quality specification, the actual measurement of absorbed dose and ancillary equipment required like electrometers and environment monitoring measurements. RESULTS: There were 190 respondents to the short survey and seven respondents to the detailed survey. The diversity of responses indicates nonuniformity in reference dosimetry practices and differences in interpretation of reference dosimetry protocols. CONCLUSIONS: The results of these surveys offer insight on clinical reference dosimetry practices and will be useful in identifying current and future needs for reference dosimetry.

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 Pinnacle's 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.