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.

A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy.

BACKGROUND: Early death after a treatment can be seen as a therapeutic failure. Accurate prediction of patients at risk for early mortality is crucial to avoid unnecessary harm and reducing costs. The goal of our work is two-fold: first, to evaluate the performance of a previously published model for early death in our cohorts. Second, to develop a prognostic model for early death prediction following radiotherapy. MATERIAL AND METHODS: Patients with NSCLC treated with chemoradiotherapy or radiotherapy alone were included in this study. Four different cohorts from different countries were available for this work (N = 1540). The previous model used age, gender, performance status, tumor stage, income deprivation, no previous treatment given (yes/no) and body mass index to make predictions. A random forest model was developed by learning on the Maastro cohort (N = 698). The new model used performance status, age, gender, T and N stage, total tumor volume (cc), total tumor dose (Gy) and chemotherapy timing (none, sequential, concurrent) to make predictions. Death within 4 months of receiving the first radiotherapy fraction was used as the outcome. RESULTS: Early death rates ranged from 6 to 11% within the four cohorts. The previous model performed with AUC values ranging from 0.54 to 0.64 on the validation cohorts. Our newly developed model had improved AUC values ranging from 0.62 to 0.71 on the validation cohorts. CONCLUSIONS: Using advanced machine learning methods and informative variables, prognostic models for early mortality can be developed. Development of accurate prognostic tools for early mortality is important to inform patients about treatment options and optimize care.

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.

Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

PURPOSE: Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. METHODS AND MATERIALS: Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. RESULTS: Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). CONCLUSIONS: Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that distributed learning is the future of sharing data in health care.

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.

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.

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.

Practice-based evidence to evidence-based practice: building the National Radiation Oncology Registry.

The National Radiation Oncology Registry (NROR), sponsored by the Radiation Oncology Institute and the American Society for Radiation Oncology, is designed to collect standardized information on cancer care delivery among patients treated with radiotherapy in the United States and will focus on patients with prostate cancer. Stakeholders were engaged through a forum that emphasized the need for patient-centered outcomes, minimal data burden, and maximal connectivity to existing registries and databases. An electronic infrastructure is under development to provide connectivity across radiation oncology and hospital information systems. The NROR Gateway features automatic abstraction as well as aggregation of treatment and outcome data. The prostate cancer data dictionary provides standardized elements in four domains: facility, physician, patient, and treatment. The pilot phase will consist of clinical centers chosen to provide a representative mix of radiation treatment modalities, facility types, population-based settings, and regional locations. The initial set of radiation practice metrics includes physician board certification and maintenance, ordering of staging scans, active surveillance discussion, dose prescriptions for low-risk/high-risk disease, radiation fields for low-risk/high-risk disease, image-guided radiation therapy use, androgen deprivation therapy use, post-brachytherapy implant computed tomography dosimetry, collection of toxicity assessments, and longitudinal patient follow-up. The NROR pilot study will provide the framework for expansion to a nationwide electronic registry for radiation oncology.

Stereotactic body radiation therapy for primary and metastatic liver tumors.

OBJECTIVES: The full potential of stereotactic body radiation therapy (SBRT), in the treatment of unresectable intrahepatic malignancies, has yet to be realized as our experience is still limited. Thus, we evaluated SBRT outcomes for primary and metastatic liver tumors, with the goal of identifying factors that may aid in optimization of therapy. METHODS: From 2005 to 2010, 62 patients with 106 primary and metastatic liver tumors were treated with SBRT to a median biologic effective dose (BED) of 100 Gy (42.6-180). The majority of patients received either three (47%) or five fractions (48%). Median gross tumor volume (GTV) was 8.8 cm(3) (0.2-222.4). RESULTS: With a median follow-up of 18 months (0.46-46.8), freedom from local progression (FFLP) was observed in 97 of 106 treated tumors, with 1- and 2-year FFLP rates of 93% and 82%. Median overall survival (OS) for all patients was 25.2 months, with 1- and 2-year OS of 81% and 52%. Neither BED nor GTV significantly predicted for FFLP. Local failure was associated with a higher risk of death [hazard ratio (HR) = 5.1, P = .0007]. One Child-Pugh Class B patient developed radiation-induced liver disease. There were no other significant toxicities. CONCLUSIONS: SBRT provides excellent local control for both primary and metastatic liver lesions with minimal toxicity. Future studies should focus on appropriate selection of patients and on careful assessment of liver function to maximize both the safety and efficacy of treatment.

Is there a dose-response relationship for heart disease with low-dose radiation therapy?

PURPOSE: To quantify cardiac radiation therapy (RT) exposure using sensitive measures of cardiac dysfunction; and to correlate dysfunction with heart doses, in the setting of adjuvant RT for left-sided breast cancer. METHODS AND MATERIALS: On a randomized trial, 32 women with node-positive left-sided breast cancer underwent pre-RT stress single photon emission computed tomography (SPECT-CT) myocardial perfusion scans. Patients received RT to the breast/chest wall and regional lymph nodes to doses of 50 to 52.2 Gy. Repeat SPECT-CT scans were performed 1 year after RT. Perfusion defects (PD), summed stress defects scores (SSS), and ejection fractions (EF) were evaluated. Doses to the heart and coronary arteries were quantified. RESULTS: The mean difference in pre- and post-RT PD was -0.38% ± 3.20% (P=.68), with no clinically significant defects. To assess for subclinical effects, PD were also examined using a 1.5-SD below the normal mean threshold, with a mean difference of 2.53% ± 12.57% (P=.38). The mean differences in SSS and EF before and after RT were 0.78% ± 2.50% (P=.08) and 1.75% ± 7.29% (P=.39), respectively. The average heart Dmean and D95 were 2.82 Gy (range, 1.11-6.06 Gy) and 0.90 Gy (range, 0.13-2.17 Gy), respectively. The average Dmean and D95 to the left anterior descending artery were 7.22 Gy (range, 2.58-18.05 Gy) and 3.22 Gy (range, 1.23-6.86 Gy), respectively. No correlations were found between cardiac doses and changes in PD, SSS, and EF. CONCLUSIONS: Using sensitive measures of cardiac function, no clinically significant defects were found after RT, with the average heart Dmean <5 Gy. Although a dose response may exist for measures of cardiac dysfunction at higher doses, no correlation was found in the present study for low doses delivered to cardiac structures and perfusion, SSS, or EF.

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.

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.

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.

A pilot study of [18F]fluorodeoxyglucose positron emission tomography scans during and after radiation-based therapy in patients with non small-cell lung cancer.

PURPOSE: To study whether changes of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) during treatment correlate with post-treatment responses in tumor and normal lung in patients with non-small-cell lung cancer (NSCLC). PATIENTS AND METHODS: Patients with stage I to III NSCLC requiring a definitive dose of fractionated radiation therapy (RT) were eligible. FDG-PET/computed tomography scans were acquired before, during, and after RT. Tumor and lung metabolic responses were assessed qualitatively by physicians and quantitatively by normalized peak FDG activity (the ratio of the maximum FDG activity divided by the mean of the aortic arch background). RESULTS: The study reached the goal of recruiting 15 patients between February 2004 and August 2005. Of these, 11 patients had partial metabolic response, two patients had complete metabolic response, and two patients had stable disease at approximately 45 Gy during RT. The mean peak tumor FDG activity was 5.2 (95% CI, 4.0 to 6.4), 2.5 (95% CI, 2.0 to 3.0), and 1.7 (95% CI, 1.3 to 2.0) on pre-, during, and post-RT scans, respectively. None of the patients had appreciable changes in the lung during RT. The peak FDG activity of the lung was 0.47 (95% CI, 0.36 to 0.59), 0.52 (95% CI, 0.40 to 0.64), and 1.29 (95% CI, 0.82 to 1.76), on pre-, during-, and post-RT scans, respectively. The qualitative response during RT correlated with the overall response post-RT (P = .03); the peak tumor FDG activity during RT correlated with those 3 months post-RT (R2 = 0.7; P < .001). CONCLUSION: This pilot study suggests a significant correlation in tumor metabolic response and no association in lung FDG activity between during RT scans and 3 months post-RT scans in patients with NSCLC. Additional study with a large number of patients is needed to validate these findings.

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.

Imaging and alignment for image-guided radiation therapy.

Image-guided radiation therapy is an exciting new area that focuses heavily on the potential benefit of advanced imaging and image registration to improve precision, thus limiting morbidity and potentially allowing for safe delivery of increased dose. This review explores the issues surrounding the use of imaging and image registration for treatment planning and verification, with emphasis on the underlying patient model and alignment algorithms.

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.