Improving treatment plan evaluation with automation.

The goal of this work is to evaluate the effectiveness of Plan-Checker Tool (PCT) which was created to improve first-time plan quality, reduce patient delays, increase the efficiency of our electronic workflow, and standardize and automate the phys-ics plan review in the treatment planning system (TPS). PCT uses an application programming interface to check and compare data from the TPS and treatment management system (TMS). PCT includes a comprehensive checklist of automated and manual checks that are documented when performed by the user as part of a plan readiness check for treatment. Prior to and during PCT development, errors identified during the physics review and causes of patient treatment start delays were tracked to prioritize which checks should be automated. Nineteen of 33checklist items were automated, with data extracted with PCT. There was a 60% reduction in the number of patient delays in the six months after PCT release. PCT was suc-cessfully implemented for use on all external beam treatment plans in our clinic. While the number of errors found during the physics check did not decrease, automation of checks increased visibility of errors during the physics check, which led to decreased patient delays. The methods used here can be applied to any TMS and TPS that allows queries of the database.

SafetyNet: Streamlining and Automating QA in radiotherapy.

Proper quality assurance (QA) of the radiotherapy process can be time-consuming and expensive. Many QA efforts, such as data export and import, are inefficient when done by humans. Additionally, humans can be unreliable, lose attention, and fail to complete critical steps that are required for smooth operations. In our group we have sought to break down the QA tasks into separate steps and to automate those steps that are better done by software running autonomously or at the instigation of a human. A team of medical physicists and software engineers worked together to identify opportunities to streamline and automate QA. Development efforts follow a formal cycle of writing software requirements, developing software, testing and commissioning. The clinical release process is separated into clinical evaluation testing, training, and finally clinical release. We have improved six processes related to QA and safety. Steps that were previously performed by humans have been automated or streamlined to increase first-time quality, reduce time spent by humans doing low-level tasks, and expedite QA tests. Much of the gains were had by automating data transfer, implementing computer-based checking and automation of systems with an event-driven framework. These coordinated efforts by software engineers and clinical physicists have resulted in speed improvements in expediting patient-sensitive QA tests.

A stochastic approach to estimate the uncertainty of dose mapping caused by uncertainties in b-spline registration.

PURPOSE: In fractionated radiation therapy, image guidance with daily tomographic imaging becomes more and more clinical routine. In principle, this allows for daily computation of the delivered dose and for accumulation of these daily dose distributions to determine the actually delivered total dose to the patient. However, uncertainties in the mapping of the images can translate into errors of the accumulated total dose, depending on the dose gradient. In this work, an approach to estimate the uncertainty of mapping between medical images is proposed that identifies areas bearing a significant risk of inaccurate dose accumulation. METHODS: This method accounts for the geometric uncertainty of image registration and the heterogeneity of the dose distribution, which is to be mapped. Its performance is demonstrated in context of dose mapping based on b-spline registration. It is based on evaluation of the sensitivity of dose mapping to variations of the b-spline coefficients combined with evaluation of the sensitivity of the registration metric with respect to the variations of the coefficients. It was evaluated based on patient data that was deformed based on a breathing model, where the ground truth of the deformation, and hence the actual true dose mapping error, is known. RESULTS: The proposed approach has the potential to distinguish areas of the image where dose mapping is likely to be accurate from other areas of the same image, where a larger uncertainty must be expected. CONCLUSIONS: An approach to identify areas where dose mapping is likely to be inaccurate was developed and implemented. This method was tested for dose mapping, but it may be applied in context of other mapping tasks as well.

A Safe and Practical Cycle for Team-Based Development and Implementation of In-House Clinical Software.

PURPOSE: Due to a gap in published guidance, we describe our robust cycle of in-house clinical software development and implementation, which has been used for years to facilitate the safe treatment of all patients in our clinics. METHODS AND MATERIALS: Our software development and implementation cycle requires clarity in communication, clearly defined roles, thorough commissioning, and regular feedback. Cycle phases include design requirements and use cases, development, physics evaluation testing, clinical evaluation testing, and full clinical release. Software requirements, release notes, test suites, and a commissioning report are created and independently reviewed before clinical use. Software deemed to be high-risk, such as those that are writable to a database, incorporate the use of a formal, team-based hazard analysis. Incident learning is used to both guide initial development and improvements as well as to monitor the safe use of the software. RESULTS: Our standard process builds in transparency and establishes high expectations in the development and use of custom software to support patient care. Since moving to a commercial planning system platform in 2013, we have applied our team-based software release process to 16 programs related to scripting in the treatment planning system for the clinic. CONCLUSIONS: The principles and methodology described here can be implemented in a range of practice settings regardless of whether or not dedicated resources are available for software development. In addition to teamwork with defined roles, documentation, and use of incident learning, we strongly recommend having a written policy on the process, using phased testing, and incorporating independent oversight and approval before use for patient care. This rigorous process ensures continuous monitoring for and mitigatation of any high risk hazards.

Evaluation of multiple breathing states using a multiple instance geometry approximation (MIGA) in inverse-planned optimization for locoregional breast treatment.

PURPOSE: Although previous work demonstrated superior dose distributions for left-sided breast cancer patients planned for intensity-modulated radiation therapy (IMRT) at deep inspiration breath hold compared with conventional techniques with free-breathing, such techniques are not always feasible to limit the impact of respiration on treatment delivery. This study assessed whether optimization based on multiple instance geometry approximation (MIGA) could derive an IMRT plan that is less sensitive to known respiratory motions. METHODS AND MATERIALS: CT scans were acquired with an active breathing control device at multiple breath-hold states. Three inverse optimized plans were generated for eight left-sided breast cancer patients: one static IMRT plan optimized at end exhale, two (MIGA) plans based on a MIGA representation of normal breathing, and a MIGA representation of deep breathing, respectively. Breast and nodal targets were prescribed 52.2 Gy, and a simultaneous tumor bed boost was prescribed 60 Gy. RESULTS: With normal breathing, doses to the targets, heart, and left anterior descending (LAD) artery were equivalent whether optimizing with MIGA or on a static data set. When simulating motion due to deep breathing, optimization with MIGA appears to yield superior tumor-bed coverage, decreased LAD mean dose, and maximum heart and LAD dose compared with optimization on a static representation. CONCLUSIONS: For left-sided breast-cancer patients, inverse-based optimization accounting for motion due to normal breathing may be similar to optimization on a static data set. However, some patients may benefit from accounting for deep breathing with MIGA with improvements in tumor-bed coverage and dose to critical structures.

Correlation between pretreatment FDG-PET biological target volume and anatomical location of failure after radiation therapy for head and neck cancers.

BACKGROUND AND PURPOSE: To assess whether the pretreatment FDG-PET-defined biologic target volume (PET-BTV) correlates with the anatomical sites of loco-regional failure (LRF) after RT for head and neck cancer (HNC). MATERIALS AND METHODS: We retrospectively identified 61 HNC patients treated definitively with either 3-D CRT or IMRT who had a pre-therapy PET/CT. The GTV and high-risk CTV(1) definitions included composite data obtained from diagnostic CT, PET/CT, physical examination, and MRI when available. The median CTV(1) dose was 70Gy. 95% received chemotherapy. For patients with LRF, a recurrence volume (V(r)) was identified and was mapped to the pretreatment planning CT and pretreatment PET scan. RESULTS: At a median follow-up of 22 months, 15% (9/61) patients had LRF. For patients with a LRF, 100% (9/9) of failures were inside the GTV. One of nine [11% (95% CI: 3-45%)] had V(r) which mapped outside of the pretreatment PET-BTV, while 8/9 patients had V(r) within the PET-BTV. Predictors of LRF in our series included GTV volume (p=0.003), but not mean SUV (p=0.13) or max SUV (p=0.25). CONCLUSIONS: Following treatment in which the GTV was defined based on the composite of imaging and physical examination, the majority, but not all, LRF occurred within the PET-BTV. These results support an important, but not exclusive, role of FDG-PET in defining the GTV.

Objective assessment of deformable image registration in radiotherapy: a multi-institution study.

The looming potential of deformable alignment tools to play an integral role in adaptive radiotherapy suggests a need for objective assessment of these complex algorithms. Previous studies in this area are based on the ability of alignment to reproduce analytically generated deformations applied to sample image data, or use of contours or bifurcations as ground truth for evaluation of alignment accuracy. In this study, a deformable phantom was embedded with 48 small plastic markers, placed in regions varying from high contrast to roughly uniform regional intensity, and small to large regional discontinuities in movement. CT volumes of this phantom were acquired at different deformation states. After manual localization of marker coordinates, images were edited to remove the markers. The resulting image volumes were sent to five collaborating institutions, each of which has developed previously published deformable alignment tools routinely in use. Alignments were done, and applied to the list of reference coordinates at the inhale state. The transformed coordinates were compared to the actual marker locations at exhale. A total of eight alignment techniques were tested from the six institutions. All algorithms performed generally well, as compared to previous publications. Average errors in predicted location ranged from 1.5 to 3.9 mm, depending on technique. No algorithm was uniformly accurate across all regions of the phantom, with maximum errors ranging from 5.1 to 15.4 mm. Larger errors were seen in regions near significant shape changes, as well as areas with uniform contrast but large local motion discontinuity. Although reasonable accuracy was achieved overall, the variation of error in different regions suggests caution in globally accepting the results from deformable alignment.

Respiratory motion of the heart and positional reproducibility under active breathing control.

PURPOSE: To reduce cardiotoxicity from breast radiotherapy (RT), innovative techniques are under investigation. Information about cardiac motion with respiration and positional reproducibility under active breathing control (ABC) is necessary to evaluate these techniques. METHODS AND MATERIALS: Patients requiring loco-regional RT for breast cancer were scanned by computed tomography using an ABC device at various breath-hold states, before and during treatment. Ten patients were studied. For each patient, 12 datasets were analyzed. Mutual information-based regional rigid alignment was used to determine the magnitude and reproducibility of cardiac motion as a function of breathing state. For each scan session, motion was quantified by evaluating the displacement of a point along the left anterior descending artery (LAD) with respect to its position at end expiration. Long-term positional reproducibility was also assessed. RESULTS: Displacement of the LAD was greatest in the inferior direction, moderate in the anterior direction, and lowest in the left-right direction. At shallow breathing states, the average displacement of LAD position was up to 6 mm in the inferior direction. The maximum displacement in any patient was 2.8 cm in the inferior direction, between expiration and deep-inspiration breath hold. At end expiration, the long-term reproducibility (SD) of the LAD position was 3 mm in the A-P, 6 mm in the S-I, and 4 mm in the L-R directions. At deep-inspiration breath hold, long-term reproducibility was 3 mm in the A-P, 7 mm in the S-I, and 3 mm in the L-R directions. CONCLUSIONS: These data demonstrate the extent of LAD displacement that occurs with shallow breathing and with deep-inspiration breath hold. This information may guide optimization studies considering the effects of respiratory motion and reproducibility of cardiac position on cardiac dose, both with and without ABC.

Technical note: a physical phantom for assessment of accuracy of deformable alignment algorithms.

The purpose of this study was to investigate the feasibility of a simple deformable phantom as a QA tool for testing and validation of deformable image registration algorithms. A diagnostic thoracic imaging phantom with a deformable foam insert was used in this study. Small plastic markers were distributed through the foam to create a lattice with a measurable deformation as the ground truth data for all comparisons. The foam was compressed in the superior-inferior direction using a one-dimensional drive stage pushing a flat "diaphragm" to create deformations similar to those from inhale and exhale states. Images were acquired at different compressions of the foam and the location of every marker was manually identified on each image volume to establish a known deformation field with a known accuracy. The markers were removed digitally from corresponding images prior to registration. Different image registration algorithms were tested using this method. Repeat measurement of marker positions showed an accuracy of better than 1 mm in identification of the reference marks. Testing the method on several image registration algorithms showed that the system is capable of evaluating errors quantitatively. This phantom is able to quantitatively assess the accuracy of deformable image registration, using a measure of accuracy that is independent of the signals that drive the deformation parameters.

How extensive of a 4D dataset is needed to estimate cumulative dose distribution plan evaluation metrics in conformal lung therapy?

The purpose of this study was to investigate the number of intermediate states required to adequately approximate the clinically relevant cumulative dose to deforming/moving thoracic anatomy in four-dimensional (4D) conformal radiotherapy that uses 6 MV photons to target tumors. Four patients were involved in this study. For the first three patients, computed tomography images acquired at exhale and inhale were available; they were registered using B-spline deformation model and the computed transformation was further used to simulate intermediate states between exhale and inhale. For the fourth patient, 4D-acquired, phase-sorted datasets were available and each dataset was registered with the exhale dataset. The exhale-inhale transformation was also used to simulate intermediate states in order to compare the cumulative doses computed using the actual and the simulated datasets. Doses to each state were calculated using the Dose Planning Method (DPM) Monte Carlo code and dose was accumulated for scoring on the exhale anatomy via the transformation matrices for each state and time weighting factors. Cumulative doses were estimated using increasing numbers of intermediate states and compared to simpler scenarios such as a "2-state" model which used only the exhale and inhale datasets or the dose received during the average phase of the breathing cycle. Dose distributions for each modeled state as well as the cumulative doses were assessed using dose volume histograms and several treatment evaluation metrics such as mean lung dose, normal tissue complication probability, and generalized uniform dose. Although significant "point dose" differences can exist between each breathing state, the differences decrease when cumulative doses are considered, and can become less significant yet in terms of evaluation metrics depending upon the clinical end point. This study suggests that for certain "clinical" end points of importance for lung cancer, satisfactory predictions of accumulated total dose to be received by the distorting anatomy can be achieved by calculating the dose to but a few (or even simply the average) phases of the breathing cycle.

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132.

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

Incorporating big data into treatment plan evaluation: Development of statistical DVH metrics and visualization dashboards.

PURPOSE: To develop statistical dose-volume histogram (DVH)-based metrics and a visualization method to quantify the comparison of treatment plans with historical experience and among different institutions. METHODS AND MATERIALS: The descriptive statistical summary (ie, median, first and third quartiles, and 95% confidence intervals) of volume-normalized DVH curve sets of past experiences was visualized through the creation of statistical DVH plots. Detailed distribution parameters were calculated and stored in JavaScript Object Notation files to facilitate management, including transfer and potential multi-institutional comparisons. In the treatment plan evaluation, structure DVH curves were scored against computed statistical DVHs and weighted experience scores (WESs). Individual, clinically used, DVH-based metrics were integrated into a generalized evaluation metric (GEM) as a priority-weighted sum of normalized incomplete gamma functions. Historical treatment plans for 351 patients with head and neck cancer, 104 with prostate cancer who were treated with conventional fractionation, and 94 with liver cancer who were treated with stereotactic body radiation therapy were analyzed to demonstrate the usage of statistical DVH, WES, and GEM in a plan evaluation. A shareable dashboard plugin was created to display statistical DVHs and integrate GEM and WES scores into a clinical plan evaluation within the treatment planning system. Benchmarking with normal tissue complication probability scores was carried out to compare the behavior of GEM and WES scores. RESULTS: DVH curves from historical treatment plans were characterized and presented, with difficult-to-spare structures (ie, frequently compromised organs at risk) identified. Quantitative evaluations by GEM and/or WES compared favorably with the normal tissue complication probability Lyman-Kutcher-Burman model, transforming a set of discrete threshold-priority limits into a continuous model reflecting physician objectives and historical experience. CONCLUSIONS: Statistical DVH offers an easy-to-read, detailed, and comprehensive way to visualize the quantitative comparison with historical experiences and among institutions. WES and GEM metrics offer a flexible means of incorporating discrete threshold-prioritizations and historic context into a set of standardized scoring metrics. Together, they provide a practical approach for incorporating big data into clinical practice for treatment plan evaluations.

Use of magnetic resonance imaging to assess blood-brain/blood-glioma barrier opening during conformal radiotherapy.

PURPOSE: For chemotherapy to act synergistically and safely with radiation against high-grade gliomas, drugs must pass the endothelial junctions of the blood-tumor barrier (BTB) to reach all tumor cells, and should not pass the blood-brain barrier (BBB) to cause toxicity to normal brain. The objective of this study was to assess BBB/BTB status using magnetic resonance imaging (MRI) during a course of radiotherapy of high-grade gliomas. PATIENTS AND METHODS: Sixteen patients with grade 3 or 4 supratentorial malignant glioma receiving conformal radiotherapy (RT) underwent contrast-enhanced MRI before, during, and after completion of RT. A gadolinium diethylenetriaminepentaacetic acid (Gd-DTPA) uptake index was analyzed with respect to the tumor and RT dose received. RESULTS: In the nonenhanced tumor region, contrast uptake increased significantly after the receipt of approximately 10 Gy (P < .01), and reached a maximum after the receipt of approximately 30 Gy. In the initially contrast-enhanced tumor region, contrast uptake decreased over the course of RT and became significant after completion of RT in patients without progressive disease. The healthy brain showed only nonsignificant changes during and after irradiation. CONCLUSION: Contrast MRI reveals increases in Gd-DTPA uptake in the initially nonenhanced tumor region but not in the remaining brain during the course of RT, suggesting opening of the BTB. This finding suggests that the effect of conformal radiation is more selective on the BTB than the BBB, and there may be a window extending from 1 week after the initiation of radiotherapy to 1 month after the completion of treatment during which a pharmaceutical agent has maximum access to high-grade gliomas.

Reporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning.

PURPOSE: To investigate methods of reporting and analyzing statistical uncertainties in doses to targets and normal tissues in Monte Carlo (MC)-based treatment planning. METHODS AND MATERIALS: Methods for quantifying statistical uncertainties in dose, such as uncertainty specification to specific dose points, or to volume-based regions, were analyzed in MC-based treatment planning for 5 lung cancer patients. The effect of statistical uncertainties on target and normal tissue dose indices was evaluated. The concept of uncertainty volume histograms for targets and organs at risk was examined, along with its utility, in conjunction with dose volume histograms, in assessing the acceptability of the statistical precision in dose distributions. The uncertainty evaluation tools were extended to four-dimensional planning for application on multiple instances of the patient geometry. All calculations were performed using the Dose Planning Method MC code. RESULTS: For targets, generalized equivalent uniform doses and mean target doses converged at 150 million simulated histories, corresponding to relative uncertainties of less than 2% in the mean target doses. For the normal lung tissue (a volume-effect organ), mean lung dose and normal tissue complication probability converged at 150 million histories despite the large range in the relative organ uncertainty volume histograms. For "serial" normal tissues such as the spinal cord, large fluctuations exist in point dose relative uncertainties. CONCLUSIONS: The tools presented here provide useful means for evaluating statistical precision in MC-based dose distributions. Tradeoffs between uncertainties in doses to targets, volume-effect organs, and "serial" normal tissues must be considered carefully in determining acceptable levels of statistical precision in MC-computed dose distributions.

The big data effort in radiation oncology: Data mining or data farming?

Although large volumes of information are entered into our electronic health care records, radiation oncology information systems and treatment planning systems on a daily basis, the goal of extracting and using this big data has been slow to emerge. Development of strategies to meet this goal is aided by examining issues with a data farming instead of a data mining conceptualization. Using this model, a vision of key data elements, clinical process changes, technology issues and solutions, and role for professional societies is presented. With a better view of technology, process and standardization factors, definition and prioritization of efforts can be more effectively directed.

Esophagus sparing with IMRT in lung tumor irradiation: an EUD-based optimization technique.

PURPOSE: The aim of this study was to evaluate (1) the use of generalized equivalent uniform dose (gEUD) to optimize dose escalation of lung tumors when the esophagus overlaps the planning target volume (PTV) and (2) the potential benefit of further dose escalation in only the part of the PTV that does not overlap the esophagus. METHODS AND MATERIALS: The treatment-planning computed tomography (CT) scans of patients with primary lung tumors located in different regions of the left and right lung were used for the optimization of beamlet intensity modulated radiation therapy (IMRT) plans. In all cases, the PTV overlapped part of the esophagus. The dose in the PTV was maximized according to 7 different primary cost functions: 2 plans that made use of mean dose (MD) (the reference plan, in which the 95% isodose surface covered the PTV and a second plan that had no constraint on the minimum isodose), 3 plans based on maximizing gEUD for the whole PTV with ever increasing assumptions for tumor aggressiveness, and 2 plans that used different gEUD values in 2 simultaneous, overlapping target volumes (the whole PTV and the PTV minus esophagus). Beam arrangements and NTCP-based costlets for the organs at risk (OARs) were kept identical to the original conformal plan for each case. Regardless of optimization method, the relative ranking of the resulting plans was evaluated in terms of the absence of cold spots within the PTV and the final gEUD computed for the whole PTV. RESULTS: Because the MD-optimized plans lacked a constraint on minimum PTV coverage, they resulted in cold spots that affected approximately 5% of the PTV volume. When optimizing over the whole PTV volume, gEUD-optimized plans resulted in higher equivalent uniform PTV doses than did the reference plan while still maintaining normal-tissue constraints. However, only under the assumption of extremely aggressive tumors could cold spots in the PTV be avoided. Generally, high-level overall results are obtained when optimization in the whole PTV is also associated with a second simultaneous optimization in the PTV minus overlapping portions of the esophagus. CONCLUSIONS: Intensity modulated radiation therapy optimizations that utilize gEUD-based cost functions for the PTV and NTCP-based constraints for the OARs result in increased doses to large portions of the PTV in cases where the PTV overlaps the esophagus, while still maintaining (and confining to the overlap region) minimum dose coverage equivalent to the homogeneous PTV optimization cases.

Benefit of using biologic parameters (EUD and NTCP) in IMRT optimization for treatment of intrahepatic tumors.

PURPOSE: To investigate whether intensity-modulated radiotherapy (IMRT), optimized using the generalized equivalent uniform dose (gEUD) and normal tissue complication probability (NTCP) models, can increase the safe dose to intrahepatic tumors compared with three-dimensional conformal RT (3D-CRT). A secondary objective was to investigate the optimal beam arrangement for liver IMRT plans. METHODS AND MATERIALS: Planning CT data of 15 patients with intrahepatic tumors, previously treated with 3D-CRT, were used as input. The dose delivered using 3D-CRT had been limited either by tolerance of adjacent organs, which were close to, or overlapped with, the planning target volume (PTV; overlap cases, n = 8), or liver toxicity (nonoverlap, n = 7). IMRT plans were created using the gEUD to maximize the dose across the PTV and the NTCP to maintain the organ-at-risk toxicity to that of the conformal plan. Increased heterogeneity was allowed across the PTV in overlap cases, without compromising the minimal PTV dose of the conformal plan and restricting the maximal dose to within 110% of the mean. Three different beam arrangements were used for each case: seven-field equidistant axial, six-field noncoplanar (predominantly right-sided beams), and a reproduction of the conformal gantry angles. gEUDs were also computed and used for evaluation of the plans (regardless of planning technique) to reflect the response of both high- and low-grade tumors. The IMRT plan that allowed the greatest gEUD across the PTV was used in the comparison with the 3D-CRT plan. RESULTS: The use of IMRT significantly increased the maximal gEUD achievable across the PTV compared with the 3D-CRT plans. This was the case for the assumptions of both high- and low-grade tumors, irrespective of the tumor position within the liver. The mean gEUD increase was 11 Gy (high grade) and 18.0 Gy (low grade) for overlap cases (p = 0.001 and p = 0.003, respectively) and 10 Gy for nonoverlap cases (p = 0.020). When comparing the IMRT beam arrangements, gEUDs were considered equivalent if they differed by less than one fraction (1.5 Gy). In overlap cases (n = 8), an equivalent "best" gEUD value was obtained in 3, 5, and 7 cases for the original conformal angle, seven-field axial, and six-field noncoplanar plan, respectively. The corresponding results were 5, 2, and 3 in the cases without an overlap (n = 7). CONCLUSION: We have successfully used mathematical/biologic models directly as cost functions within the optimizing process to produce IMRT plans that maximize the gEUD while maintaining compliance with a well-defined protocol for the treatment of intrahepatic cancer. For both PTV-organ-at-risk overlap and nonoverlap situations, IMRT has the capacity to improve the maximal dose achievable across the PTV, expressed in terms of the gEUD. The use of multiple noncoplanar beams appears to confer an advantage over fewer beams in cases with PTV-organ-at-risk overlap. When liver toxicity is the dose-limiting factor, high gEUD values are obtained most frequently when the field arrangement is chosen to provide the shortest possible transhepatic path length.

Use and uncertainties of mutual information for computed tomography/ magnetic resonance (CT/MR) registration post permanent implant of the prostate.

Post-implant dosimetric analysis for permanent implant of the prostate benefits from the use of a computed tomography (CT) dataset for optimal identification of the radioactive source (seed) positions and a magnetic resonance (MR) dataset for optimal description of the target and normal tissue volumes. The CT/MR registration process should be fast and sufficiently accurate to yield a reliable dosimetric analysis. Since critical normal tissues typically reside in dose gradient regions, small shifts in the dose distribution could impact the prediction of complication or complication severity. Standard procedures include the use of the seed distribution as fiducial markers (seed match), a time consuming process that relies on the proper identification of signals due to the same seed on both datasets. Mutual information (MI) is more efficient because it uses image data requiring minimal preparation effort. A comparison of MI registration and seed-match registration was performed for twelve patients. MI was applied to a volume limited to the prostate and surrounding structures, excluding most of the pelvic bone structures (margins around the prostate gland were approximately 2 cm right-left, approximately 1 cm anterior-posterior, and approximately 2 cm superior-inferior). Seeds were identified on a 2 mm slice CT dataset using an automatic seed identification procedure on reconstructed three-dimensional data. Seed positions on the 3 mm slice thickness T2 MR data set were identified using a point-and-click method on each image. Seed images were identified on more than one MR slice, and the results used to determine average seed coordinates for MR images and matched seed pairs between CT and MR images. On average, 42% (19%-64%) of the seeds (19-54 seeds) were identified and matched to their CT counterparts. A least-squares method applied to the CT and MR seed coordinates was used to produce the optimum seed-match registration. MI registration and seed match registration angle differences averaged 0.5 degrees, which was not significantly different from zero. Translation differences averaged 0.6 (1.2 standard deviation) mm right-left, -0.5(1.5) mm posterior-anterior, and -1.2(2.0) mm inferior-superior. Registration error estimates were approximately 2 mm for both the MI and seed-match methods. The observed standard deviations in the offset values were consistent with propagation of error. Registration methods as applied here using mutual information and seed matching are consistent, except for a small systematic difference in the inferior-superior axis for a minority of cases (approximately 15%). Cases registered with mutual information and with bony anatomy misregistration of greater than approximately 5 mm should be evaluated for rescan or seed-match registration. The improvement in efficiency of use for the MI registration method is substantial, approximately 30 min compared to several hours using seed match registration.

Dose reconstruction in deforming lung anatomy: dose grid size effects and clinical implications.

In this study we investigated the accumulation of dose to a deforming anatomy (such as lung) based on voxel tracking and by using time weighting factors derived from a breathing probability distribution function (p.d.f.). A mutual information registration scheme (using thin-plate spline warping) provided a transformation that allows the tracking of points between exhale and inhale treatment planning datasets (and/or intermediate state scans). The dose distributions were computed at the same resolution on each dataset using the Dose Planning Method (DPM) Monte Carlo code. Two accumulation/interpolation approaches were assessed. The first maps exhale dose grid points onto the inhale scan, estimates the doses at the "tracked" locations by trilinear interpolation and scores the accumulated doses (via the p.d.f.) on the original exhale data set. In the second approach, the "volume" associated with each exhale dose grid point (exhale dose voxel) is first subdivided into octants, the center of each octant is mapped to locations on the inhale dose grid and doses are estimated by trilinear interpolation. The octant doses are then averaged to form the inhale voxel dose and scored at the original exhale dose grid point location. Differences between the interpolation schemes are voxel size and tissue density dependent, but in general appear primarily only in regions with steep dose gradients (e.g., penumbra). Their magnitude (small regions of few percent differences) is less than the alterations in dose due to positional and shape changes from breathing in the first place. Thus, for sufficiently small dose grid point spacing, and relative to organ motion and deformation, differences due solely to the interpolation are unlikely to result in clinically significant differences to volume-based evaluation metrics such as mean lung dose (MLD) and tumor equivalent uniform dose (gEUD). The overall effects of deformation vary among patients. They depend on the tumor location, field size, volume expansion, tissue heterogeneity, and direction of tumor displacement with respect to the beam, and are more likely to have an impact on serial organs (such as esophagus), rather than on large parallel organs (such as lung).

Monte Carlo-based lung cancer treatment planning incorporating PET-defined target volumes.

Despite the well-known benefits of positron emission tomography (PET) imaging in lung cancer diagnosis and staging, the poor spatial resolution of PET has limited its use in radiotherapy planning. Methods used for segmenting tumor from normal tissue, such as threshold boundaries using a fraction of the standardized uptake value (SUV), are subject to uncertainties. The issue of respiratory motion in the thorax confounds the problem of accurate target definition. In this work, we evaluate how changing the PET-defined target volume by varying the threshold value in the segmentation process impacts target and normal lung tissue doses. For each of eight lung cancer patients we retrospectively generated multiple PET-target volumes; each target volume corresponds to those voxels with intensities above a given threshold level, defined by a percentage of the maximum voxel intensity. PET-defined targets were compared to those from CT; CT targets comprise a composite volume generated from breath-hold inhale and exhale datasets; the CT dataset therefore also includes the extents of tumor motion. Treatment plans using Monte Carlo dose calculation were generated for all targets; the dose uniformity was approximately 100+/-5% within the internal target volume (ITV) (formed by a uniform 8-mm expansion of the composite gross target volume (GTV)). In all cases differences were observed in the generalized equivalent uniform doses (gEUDs) to the targets and in the mean lung doses (MLDs) and normal tissue complication probabilities (NTCPs) to the normal lung tissues. The magnitudes of the dose differences were found to depend on the target volume, location, and amount of irradiated normal lung tissue, and in many instances were clinically meaningful (greater than a single 2 Gy fraction). For those patients studied, results indicate that accurate dosimetry using PET volumes is highly dependent on accurate target segmentation. Further study with correlation to clinical outcome will be helpful in determining how to apply these various PET and CT volumes in treatment planning, to potentially improve local tumor control and reduce normal tissue toxicities.