A small number of objective function weight vectors is sufficient for automated treatment planning in prostate cancer.

Current practice for treatment planning optimization can be both inefficient and time consuming. In this paper, we propose an automated planning methodology that aims to combine both explorative and prescriptive approaches for improving the efficiency and the quality of the treatment planning process. Given a treatment plan, our explorative approach explores trade-offs between different objectives and finds an acceptable region for objective function weights via inverse optimization. Intuitively, the shape and size of these regions describe how 'sensitive' a patient is to perturbations in objective function weights. We then develop an integer programming-based prescriptive approach that exploits the information encoded by these regions to find a set of five representative objective function weight vectors such that for each patient there exists at least one representative weight vector that can produce a high quality treatment plan. Using 315 patients from Princess Margaret Cancer Centre, we show that the produced treatment plans are comparable and, for [Formula: see text] of cases, improve upon the inversely optimized plans that are generated from the historical clinical treatment plans.

Inverse optimization of objective function weights for treatment planning using clinical dose-volume histograms.

We developed and evaluated a novel inverse optimization (IO) model to estimate objective function weights from clinical dose-volume histograms (DVHs). These weights were used to solve a treatment planning problem to generate 'inverse plans' that had similar DVHs to the original clinical DVHs. Our methodology was applied to 217 clinical head and neck cancer treatment plans that were previously delivered at Princess Margaret Cancer Centre in Canada. Inverse plan DVHs were compared to the clinical DVHs using objective function values, dose-volume differences, and frequency of clinical planning criteria satisfaction. Median differences between the clinical and inverse DVHs were within 1.1 Gy. For most structures, the difference in clinical planning criteria satisfaction between the clinical and inverse plans was at most 1.4%. For structures where the two plans differed by more than 1.4% in planning criteria satisfaction, the difference in average criterion violation was less than 0.5 Gy. Overall, the inverse plans were very similar to the clinical plans. Compared with a previous inverse optimization method from the literature, our new inverse plans typically satisfied the same or more clinical criteria, and had consistently lower fluence heterogeneity. Overall, this paper demonstrates that DVHs, which are essentially summary statistics, provide sufficient information to estimate objective function weights that result in high quality treatment plans. However, as with any summary statistic that compresses three-dimensional dose information, care must be taken to avoid generating plans with undesirable features such as hotspots; our computational results suggest that such undesirable spatial features were uncommon. Our IO-based approach can be integrated into the current clinical planning paradigm to better initialize the planning process and improve planning efficiency. It could also be embedded in a knowledge-based planning or adaptive radiation therapy framework to automatically generate a new plan given a predicted or updated target DVH, respectively.

Vulnerability of white matter to insult during childhood: evidence from patients treated for medulloblastoma.

OBJECTIVE Craniospinal irradiation damages the white matter in children treated for medulloblastoma, but the treatment-intensity effects are unclear. In a cross-sectional retrospective study, the effects of treatment with the least intensive radiation protocol versus protocols that delivered more radiation to the brain, in addition to the effects of continuous radiation dose, on white matter architecture were evaluated. METHODS Diffusion tensor imaging was used to assess fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity. First, regional white matter analyses and tract-based spatial statistics were conducted in 34 medulloblastoma patients and 38 healthy controls. Patients were stratified according to those treated with 1) the least intensive radiation protocol, specifically reduced-dose craniospinal irradiation plus a boost to the tumor bed only (n = 17), or 2) any other dose and boost combination that delivered more radiation to the brain, which was also termed the "all-other-treatments" group (n = 17), and comprised patients treated with standard-dose craniospinal irradiation plus a posterior fossa boost, standard-dose craniospinal irradiation plus a tumor bed boost, or reduced-dose craniospinal irradiation plus a posterior fossa boost. Second, voxel-wise dose-distribution analyses were conducted on a separate cohort of medulloblastoma patients (n = 15). RESULTS The all-other-treatments group, but not the reduced-dose craniospinal irradiation plus tumor bed group, had lower fractional anisotropy and higher radial diffusivity than controls in all brain regions (all p < 0.05). The reduced-dose craniospinal irradiation plus tumor bed boost group had higher fractional anisotropy (p = 0.05) and lower radial diffusivity (p = 0.04) in the temporal region, and higher fractional anisotropy in the frontal region (p = 0.04), than the all-other-treatments group. Linear mixed-effects modeling revealed that the dose and age at diagnosis together 1) better predicted fractional anisotropy in the temporal region than models with either alone (p < 0.005), but 2) did not better predict fractional anisotropy in comparison with dose alone in the occipital region (p > 0.05). CONCLUSIONS Together, the results show that white matter damage has a clear association with increasing radiation dose, and that treatment with reduced-dose craniospinal irradiation plus tumor bed boost appears to preserve white matter in some brain regions.

Sample size requirements for knowledge-based treatment planning.

PURPOSE: To determine how training set size affects the accuracy of knowledge-based treatment planning (KBP) models. METHODS: The authors selected four models from three classes of KBP approaches, corresponding to three distinct quantities that KBP models may predict: dose-volume histogram (DVH) points, DVH curves, and objective function weights. DVH point prediction is done using the best plan from a database of similar clinical plans; DVH curve prediction employs principal component analysis and multiple linear regression; and objective function weights uses either logistic regression or K-nearest neighbors. The authors trained each KBP model using training sets of sizes n = 10, 20, 30, 50, 75, 100, 150, and 200. The authors set aside 100 randomly selected patients from their cohort of 315 prostate cancer patients from Princess Margaret Cancer Center to serve as a validation set for all experiments. For each value of n, the authors randomly selected 100 different training sets with replacement from the remaining 215 patients. Each of the 100 training sets was used to train a model for each value of n and for each KBT approach. To evaluate the models, the authors predicted the KBP endpoints for each of the 100 patients in the validation set. To estimate the minimum required sample size, the authors used statistical testing to determine if the median error for each sample size from 10 to 150 is equal to the median error for the maximum sample size of 200. RESULTS: The minimum required sample size was different for each model. The DVH point prediction method predicts two dose metrics for the bladder and two for the rectum. The authors found that more than 200 samples were required to achieve consistent model predictions for all four metrics. For DVH curve prediction, the authors found that at least 75 samples were needed to accurately predict the bladder DVH, while only 20 samples were needed to predict the rectum DVH. Finally, for objective function weight prediction, at least 10 samples were needed to train the logistic regression model, while at least 150 samples were required to train the K-nearest neighbor methodology. CONCLUSIONS: In conclusion, the minimum required sample size needed to accurately train KBP models for prostate cancer depends on the specific model and endpoint to be predicted. The authors' results may provide a lower bound for more complicated tumor sites.

Models for predicting objective function weights in prostate cancer IMRT.

PURPOSE: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. METHODS: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. RESULTS: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. CONCLUSIONS: The authors demonstrated that the KNN and MLR weight prediction methodologies perform comparably to the LR model and can produce clinical quality treatment plans by simultaneously predicting multiple weights that capture trade-offs associated with sparing multiple OARs.

The effect of radiation quality on the risks of second malignancies.

UNLABELLED: Abstract Purpose: Numerous studies have implicated elevated second cancer risks as a result of radiation therapy. Our aim in this paper was to contribute to an understanding of the effects of radiation quality on second cancer risks. In particular, we developed a biologically motivated model to study the effects of linear energy transfer (LET) of charged particles (including protons, alpha particles and heavy ions Carbon and Neon) on the risk of second cancer. MATERIALS AND METHODS: A widely used approach to estimate the risk uses the so-called initiation-inactivation-repopulation model. Based on the available experimental data for the LET dependence of radiobiological parameters and mutation rate, we generalized this formulation to include the effects of radiation quality. We evaluated the secondary cancer risks for protons in the clinical range of LET, i.e., around 4-10 (KeV/µm), which lies in the plateau region of the Bragg peak. RESULTS: For protons, at a fixed radiation dose, we showed that the increase in second cancer risks correlated directly with increasing values of LET to a certain point, and then decreased. Interestingly, we obtained a higher risk for proton LET of 10 KeV/µm compared to the lower LET of 4 KeV/µm in the low dose region. In the case of heavy ions, the risk was higher for Carbon ions than Neon ions (even though they have almost the same LET). We also compared protons and alpha particles with the same LET, and it was interesting to note that the second cancer risks were higher for protons compared to alpha particles in the low-dose region. CONCLUSION: Overall, this study demonstrated the importance of including LET dependence in the estimation of second cancer risk. Our theoretical risk predictions were noticeably high; however, the biological end points should be tested experimentally for multiple treatment fields and to improve theoretical predictions.

Automation and intensity modulated radiation therapy for individualized high-quality tangent breast treatment plans.

PURPOSE: To demonstrate the large-scale clinical implementation and performance of an automated treatment planning methodology for tangential breast intensity modulated radiation therapy (IMRT). METHODS AND MATERIALS: Automated planning was used to prospectively plan tangential breast IMRT treatment for 1661 patients between June 2009 and November 2012. The automated planning method emulates the manual steps performed by the user during treatment planning, including anatomical segmentation, beam placement, optimization, dose calculation, and plan documentation. The user specifies clinical requirements of the plan to be generated through a user interface embedded in the planning system. The automated method uses heuristic algorithms to define and simplify the technical aspects of the treatment planning process. RESULTS: Automated planning was used in 1661 of 1708 patients receiving tangential breast IMRT during the time interval studied. Therefore, automated planning was applicable in greater than 97% of cases. The time for treatment planning using the automated process is routinely 5 to 6 minutes on standard commercially available planning hardware. We have shown a consistent reduction in plan rejections from plan reviews through the standard quality control process or weekly quality review multidisciplinary breast rounds as we have automated the planning process for tangential breast IMRT. Clinical plan acceptance increased from 97.3% using our previous semiautomated inverse method to 98.9% using the fully automated method. CONCLUSIONS: Automation has become the routine standard method for treatment planning of tangential breast IMRT at our institution and is clinically feasible on a large scale. The method has wide clinical applicability and can add tremendous efficiency, standardization, and quality to the current treatment planning process. The use of automated methods can allow centers to more rapidly adopt IMRT and enhance access to the documented improvements in care for breast cancer patients, using technologies that are widely available and already in clinical use.

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning.

PURPOSE: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. METHODS: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. A regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. RESULTS: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, using l2 distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. CONCLUSIONS: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.

Phase 2 study of preoperative image-guided intensity-modulated radiation therapy to reduce wound and combined modality morbidities in lower extremity soft tissue sarcoma.

BACKGROUND: This study sought to determine if preoperative image-guided intensity-modulated radiotherapy (IG-IMRT) can reduce morbidity, including wound complications, by minimizing dose to uninvolved tissues in adults with lower extremity soft tissue sarcoma. METHODS: The primary endpoint was the development of an acute wound complication (WC). IG-IMRT was used to conform volumes to avoid normal tissues (skin flaps for wound closure, bone, or other uninvolved soft tissues). From July 2005 to June 2009, 70 adults were enrolled; 59 were evaluable for the primary endpoint. Median tumor size was 9.5 cm; 55 tumors (93%) were high-grade and 58 (98%) were deep to fascia. RESULTS: Eighteen (30.5%) patients developed WCs. This was not statistically significantly different from the result of the National Cancer Institute of Canada SR2 trial (P = .2); however, primary closure technique was possible more often (55 of 59 patients [93.2%] versus 50 of 70 patients [71.4%]; P = .002), and secondary operations for WCs were somewhat reduced (6 of 18 patients [33%] versus 13 of 30 patients [43%]; P = .55). Moderate edema, skin, subcutaneous, and joint toxicity was present in 6 (11.1%), 1 (1.9%), 5 (9.3%), and 3 (5.6%) patients, respectively, but there were no bone fractures. Four local recurrences (6.8%, none near the flaps) occurred with median follow-up of 49 months. CONCLUSIONS: The 30.5% incidence of WCs was numerically lower than the 43% risk derived from the National Cancer Institute of Canada SR2 trial, but did not reach statistical significance. Preoperative IG-IMRT significantly diminished the need for tissue transfer. RT chronic morbidities and the need for subsequent secondary operations for WCs were lowered, although not significantly, whereas good limb function was maintained.

Displaying 3D radiation dose on endoscopic video for therapeutic assessment and surgical guidance.

We have developed a method to register and display 3D parametric data, in particular radiation dose, on two-dimensional endoscopic images. This registration of radiation dose to endoscopic or optical imaging may be valuable in assessment of normal tissue response to radiation, and visualization of radiated tissues in patients receiving post-radiation surgery. Electromagnetic sensors embedded in a flexible endoscope were used to track the position and orientation of the endoscope allowing registration of 2D endoscopic images to CT volumetric images and radiation doses planned with respect to these images. A surface was rendered from the CT image based on the air/tissue threshold, creating a virtual endoscopic view analogous to the real endoscopic view. Radiation dose at the surface or at known depth below the surface was assigned to each segment of the virtual surface. Dose could be displayed as either a colorwash on this surface or surface isodose lines. By assigning transparency levels to each surface segment based on dose or isoline location, the virtual dose display was overlaid onto the real endoscope image. Spatial accuracy of the dose display was tested using a cylindrical phantom with a treatment plan created for the phantom that matched dose levels with grid lines on the phantom surface. The accuracy of the dose display in these phantoms was 0.8-0.99 mm. To demonstrate clinical feasibility of this approach, the dose display was also tested on clinical data of a patient with laryngeal cancer treated with radiation therapy, with estimated display accuracy of ∼2-3 mm. The utility of the dose display for registration of radiation dose information to the surgical field was further demonstrated in a mock sarcoma case using a leg phantom. With direct overlay of radiation dose on endoscopic imaging, tissue toxicities and tumor response in endoluminal organs can be directly correlated with the actual tissue dose, offering a more nuanced assessment of normal tissue toxicities following radiation therapy and accurate registration of radiation dose to the surgical field.

Individualized 3D reconstruction of normal tissue dose for patients with long-term follow-up: a step toward understanding dose risk for late toxicity.

PURPOSE: Understanding the relationship between normal tissue dose and delayed radiation toxicity is an important component of developing more effective radiation therapy. Late outcome data are generally available only for patients who have undergone 2-dimensional (2D) treatment plans. The purpose of this study was to evaluate the accuracy of 3D normal tissue dosimetry derived from reconstructed 2D treatment plans in Hodgkin's lymphoma (HL) patients. METHODS AND MATERIALS: Three-dimensional lung, heart, and breast volumes were reconstructed from 2D planning radiographs for HL patients who received mediastinal radiation therapy. For each organ, a reference 3D organ was modified with patient-specific structural information, using deformable image processing software. Radiation therapy plans were reconstructed by applying treatment parameters obtained from patient records to the reconstructed 3D volumes. For each reconstructed organ mean dose (Dmean) and volumes covered by at least 5 Gy (V5) and 20 Gy (V20) were calculated. This process was performed for 15 patients who had both 2D and 3D planning data available to compare the reconstructed normal tissue doses with those derived from the primary CT planning data and also for 10 historically treated patients with only 2D imaging available. RESULTS: For patients with 3D planning data, the normal tissue doses could be reconstructed accurately using 2D planning data. Median differences in Dmean between reconstructed and actual plans were 0.18 Gy (lungs), -0.15 Gy (heart), and 0.30 Gy (breasts). Median difference in V5 and V20 were less than 2% for each organ. Reconstructed 3D dosimetry was substantially higher in historical mantle-field treatments than contemporary involved-field mediastinal treatments: average Dmean values were 15.2 Gy vs 10.6 Gy (lungs), 27.0 Gy vs 14.3 Gy (heart), and 8.0 Gy vs 3.2 Gy (breasts). CONCLUSIONS: Three-dimensional reconstruction of absorbed dose to organs at risk can be estimated accurately many years after exposure by using limited 2D data. Compared to contemporary involved-field treatments, normal tissue doses were significantly higher in historical mantle-field treatments. These methods build capacity to quantify the relationship between 3D normal tissue dose and observed late effects.

Navigator channel adaptation to reconstruct three dimensional heart volumes from two dimensional radiotherapy planning data.

BACKGROUND: Biologically-based models that utilize 3D radiation dosimetry data to estimate the risk of late cardiac effects could have significant utility for planning radiotherapy in young patients. A major challenge arises from having only 2D treatment planning data for patients with long-term follow-up. In this study, we evaluate the accuracy of an advanced deformable image registration (DIR) and navigator channels (NC) adaptation technique to reconstruct 3D heart volumes from 2D radiotherapy planning images for Hodgkin's Lymphoma (HL) patients. METHODS: Planning CT images were obtained for 50 HL patients who underwent mediastinal radiotherapy. Twelve image sets (6 male, 6 female) were used to construct a male and a female population heart model, which was registered to 23 HL "Reference" patients' CT images using a DIR algorithm, MORFEUS. This generated a series of population-to-Reference patient specific 3D deformation maps. The technique was independently tested on 15 additional "Test" patients by reconstructing their 3D heart volumes using 2D digitally reconstructed radiographs (DRR). The technique involved: 1) identifying a matching Reference patient for each Test patient using thorax measurements, 2) placement of six NCs on matching Reference and Test patients' DRRs to capture differences in significant heart curvatures, 3) adapting the population-to-Reference patient-specific deformation maps to generate population-to-Test patient-specific deformation maps using linear and bilinear interpolation methods, 4) applying population-to-Test patient specific deformation to the population model to reconstruct Test-patient specific 3D heart models. The percentage volume overlap between the NC-adapted reconstruction and actual Test patient's true heart volume was calculated using the Dice coefficient. RESULTS: The average Dice coefficient expressed as a percentage between the NC-adapted and actual Test model was 89.4 ± 2.8%. The modified NC adaptation technique made significant improvements to the population deformation heart models (p = 0.01). As standard evaluation, the residual Dice error after adaptation was comparable to the volumetric differences observed in free-breathing heart volumes (p = 0.62). CONCLUSIONS: The reconstruction technique described generates accurate 3D heart models from limited 2D planning data. This development could potentially be used to retrospectively calculate delivered dose to the heart for historically treated patients and thereby provide a better understanding of late radiation-related cardiac effects.

Medical physics staffing for radiation oncology: a decade of experience in Ontario, Canada.

The January 2010 articles in The New York Times generated intense focus on patient safety in radiation treatment, with physics staffing identified frequently as a critical factor for consistent quality assurance. The purpose of this work is to review our experience with medical physics staffing, and to propose a transparent and flexible staffing algorithm for general use. Guided by documented times required per routine procedure, we have developed a robust algorithm to estimate physics staffing needs according to center-specific workload for medical physicists and associated support staff, in a manner we believe is adaptable to an evolving radiotherapy practice. We calculate requirements for each staffing type based on caseload, equipment inventory, quality assurance, educational programs, and administration. Average per-case staffing ratios were also determined for larger-scale human resource planning and used to model staffing needs for Ontario, Canada over the next 10 years. The workload specific algorithm was tested through a survey of Canadian cancer centers. For center-specific human resource planning, we propose a grid of coefficients addressing specific workload factors for each staff group. For larger scale forecasting of human resource requirements, values of 260, 700, 300, 600, 1200, and 2000 treated cases per full-time equivalent (FTE) were determined for medical physicists, physics assistants, dosimetrists, electronics technologists, mechanical technologists, and information technology specialists, respectively.

The relationship between local recurrence and radiotherapy treatment volume for soft tissue sarcomas treated with external beam radiotherapy and function preservation surgery.

PURPOSE: To examine the geometric relationship between local recurrence (LR) and external beam radiotherapy (RT) volumes for soft-tissue sarcoma (STS) patients treated with function-preserving surgery and RT. METHODS AND MATERIALS: Sixty of 768 (7.8%) STS patients treated with combined therapy within our institution from 1990 through 2006 developed an LR. Thirty-two received preoperative RT, 16 postoperative RT, and 12 preoperative RT plus a postoperative boost. Treatment records, RT simulation images, and diagnostic MRI/CT data sets of the original and LR disease were retrospectively compared. For LR location analysis, three RT target volumes were defined according to the International Commission on Radiation Units and Measurements 29 as follows: (1) the gross tumor or operative bed; (2) the treatment volume (TV) extending 5 cm longitudinally beyond the tumor or operative bed unless protected by intact barriers to spread and at least 1-2 cm axially (the TV was enclosed by the isodose curve representing the prescribed target absorbed dose [TAD] and accounted for target/patient setup uncertainty and beam characteristics), and (3) the irradiated volume (IRV) that received at least 50% of the TAD, including the TV. LRs were categorized as developing in field within the TV, marginal (on the edge of the IRV), and out of field (occurring outside of the IRV). RESULTS: Forty-nine tumors relapsed in field (6.4% overall). Nine were out of field (1.1% overall), and 2 were marginal (0.3% overall). CONCLUSIONS: The majority of STS tumors recur in field, indicating that the incidence of LR may be affected more by differences in biologic and molecular characteristics rather than aberrations in RT dose or target volume coverage. In contrast, only two patients relapsed at the IRV boundary, suggesting that the risk of a marginal relapse is low when the TV is appropriately defined. These data support the accurate delivery of optimal RT volumes in the most precise way using advanced technology and image guidance.

A set cover approach to fast beam orientation optimization in intensity modulated radiation therapy for total marrow irradiation.

The beam orientation optimization (BOO) problem in intensity modulated radiation therapy (IMRT) treatment planning is a nonlinear problem, and existing methods to obtain solutions to the BOO problem are time consuming due to the complex nature of the objective function and size of the solution space. These issues become even more difficult in total marrow irradiation (TMI), where many more beams must be used to cover a vastly larger treatment area than typical site-specific treatments (e.g., head-and-neck, prostate, etc). These complications result in excessively long computation times to develop IMRT treatment plans for TMI, so we attempt to develop methods that drastically reduce treatment planning time. We transform the BOO problem into the classical set cover problem (SCP) and use existing methods to solve SCP to obtain beam solutions. Although SCP is NP-Hard, our methods obtain beam solutions that result in quality treatments in minutes. We compare our approach to an integer programming solver for the SCP to illustrate the speed advantage of our approach.

Automated planning of tangential breast intensity-modulated radiotherapy using heuristic optimization.

PURPOSE: To present an automated technique for two-field tangential breast intensity-modulated radiotherapy (IMRT) treatment planning. METHOD AND MATERIALS: A total of 158 planned patients with Stage 0, I, and II breast cancer treated using whole-breast IMRT were retrospectively replanned using automated treatment planning tools. The tools developed are integrated into the existing clinical treatment planning system (Pinnacle(3)) and are designed to perform the manual volume delineation, beam placement, and IMRT treatment planning steps carried out by the treatment planning radiation therapist. The automated algorithm, using only the radio-opaque markers placed at CT simulation as inputs, optimizes the tangential beam parameters to geometrically minimize the amount of lung and heart treated while covering the whole-breast volume. The IMRT parameters are optimized according to the automatically delineated whole-breast volume. RESULTS: The mean time to generate a complete treatment plan was 6 min, 50 s ± 1 min 12 s. For the automated plans, 157 of 158 plans (99%) were deemed clinically acceptable, and 138 of 158 plans (87%) were deemed clinically improved or equal to the corresponding clinical plan when reviewed in a randomized, double-blinded study by one experienced breast radiation oncologist. In addition, overall the automated plans were dosimetrically equivalent to the clinical plans when scored for target coverage and lung and heart doses. CONCLUSION: We have developed robust and efficient automated tools for fully inversed planned tangential breast IMRT planning that can be readily integrated into clinical practice. The tools produce clinically acceptable plans using only the common anatomic landmarks from the CT simulation process as an input. We anticipate the tools will improve patient access to high-quality IMRT treatment by simplifying the planning process and will reduce the effort and cost of incorporating more advanced planning into clinical practice.

Effect of breathing motion on radiotherapy dose accumulation in the abdomen using deformable registration.

PURPOSE: To investigate the effect of breathing motion and dose accumulation on the planned radiotherapy dose to liver tumors and normal tissues using deformable image registration. METHODS AND MATERIALS: Twenty-one free-breathing stereotactic liver cancer radiotherapy patients, planned on static exhale computed tomography (CT) for 27-60 Gy in six fractions, were included. A biomechanical model-based deformable image registration algorithm retrospectively deformed each exhale CT to inhale CT. This deformation map was combined with exhale and inhale dose grids from the treatment planning system to accumulate dose over the breathing cycle. Accumulation was also investigated using a simple rigid liver-to-liver registration. Changes to tumor and normal tissue dose were quantified. RESULTS: Relative to static plans, mean dose change (range) after deformable dose accumulation (as % of prescription dose) was -1 (-14 to 8) to minimum tumor, -4 (-15 to 0) to maximum bowel, -4 (-25 to 1) to maximum duodenum, 2 (-1 to 9) to maximum esophagus, -2 (-13 to 4) to maximum stomach, 0 (-3 to 4) to mean liver, and -1 (-5 to 1) and -2 (-7 to 1) to mean left and right kidneys. Compared to deformable registration, rigid modeling had changes up to 8% to minimum tumor and 7% to maximum normal tissues. CONCLUSION: Deformable registration and dose accumulation revealed potentially significant dose changes to either a tumor or normal tissue in the majority of cases as a result of breathing motion. These changes may not be accurately accounted for with rigid motion.

Comparison of simple and complex liver intensity modulated radiotherapy.

BACKGROUND: Intensity-modulated radiotherapy (IMRT) may allow improvement in plan quality for treatment of liver cancer, however increasing radiation modulation complexity can lead to increased uncertainties and requirements for quality assurance. This study assesses whether target coverage and normal tissue avoidance can be maintained in liver cancer intensity-modulated radiotherapy (IMRT) plans by systematically reducing the complexity of the delivered fluence. METHODS: An optimal baseline six fraction individualized IMRT plan for 27 patients with 45 liver cancers was developed which provided a median minimum dose to 0.5 cc of the planning target volume (PTV) of 38.3 Gy (range, 25.9-59.5 Gy), in 6 fractions, while maintaining liver toxicity risk <5% and maximum luminal gastrointestinal structure doses of 30 Gy. The number of segments was systematically reduced until normal tissue constraints were exceeded while maintaining equivalent dose coverage to 95% of PTV (PTVD95). Radiotherapy doses were compared between the plans. RESULTS: Reduction in the number of segments was achieved for all 27 plans from a median of 48 segments (range 34-52) to 19 segments (range 6-30), without exceeding normal tissue dose objectives and maintaining equivalent PTVD95 and similar PTV Equivalent Uniform Dose (EUD(-20)) IMRT plans with fewer segments had significantly less monitor units (mean, 1892 reduced to 1695, p = 0.012), but also reduced dose conformity (mean, RTOG Conformity Index 1.42 increased to 1.53 p = 0.001). CONCLUSIONS: Tumour coverage and normal tissue objectives were maintained with simplified liver IMRT, at the expense of reduced conformity.