Radiobiological impact of dose calculation algorithms on biologically optimized IMRT lung stereotactic body radiation therapy plans.

BACKGROUND: The aim of this study is to evaluate the radiobiological impact of Acuros XB (AXB) vs. Anisotropic Analytic Algorithm (AAA) dose calculation algorithms in combined dose-volume and biological optimized IMRT plans of SBRT treatments for non-small-cell lung cancer (NSCLC) patients. METHODS: Twenty eight patients with NSCLC previously treated SBRT were re-planned using Varian Eclipse (V11) with combined dose-volume and biological optimization IMRT sliding window technique. The total dose prescribed to the PTV was 60 Gy with 12 Gy per fraction. The plans were initially optimized using AAA algorithm, and then were recomputed using AXB using the same MUs and MLC files to compare with the dose distribution of the original plans and assess the radiobiological as well as dosimetric impact of the two different dose algorithms. The Poisson Linear-Quadatric (PLQ) and Lyman-Kutcher-Burman (LKB) models were used for estimating the tumor control probability (TCP) and normal tissue complication probability (NTCP), respectively. The influence of the model parameter uncertainties on the TCP differences and the NTCP differences between AAA and AXB plans were studied by applying different sets of published model parameters. Patients were grouped into peripheral and centrally-located tumors to evaluate the impact of tumor location. RESULTS: PTV dose was lower in the re-calculated AXB plans, as compared to AAA plans. The median differences of PTV(D95%) were 1.7 Gy (range: 0.3, 6.5 Gy) and 1.0 Gy (range: 0.6, 4.4 Gy) for peripheral tumors and centrally-located tumors, respectively. The median differences of PTV(mean) were 0.4 Gy (range: 0.0, 1.9 Gy) and 0.9 Gy (range: 0.0, 4.3 Gy) for peripheral tumors and centrally-located tumors, respectively. TCP was also found lower in AXB-recalculated plans compared with the AAA plans. The median (range) of the TCP differences for 30 month local control were 1.6 % (0.3 %, 5.8 %) for peripheral tumors and 1.3 % (0.5 %, 3.4 %) for centrally located tumors. The lower TCP is associated with the lower PTV coverage in AXB-recalculated plans. No obvious trend was observed between the calculation-resulted TCP differences and tumor size or location. AAA and AXB yield very similar NTCP on lung pneumonitis according to the LKB model estimation in the present study. CONCLUSION: AAA apparently overestimates the PTV dose; the magnitude of resulting difference in calculated TCP was up to 5.8 % in our study. AAA and AXB yield very similar NTCP on lung pneumonitis based on the LKB model parameter sets we used in the present study.

Dosimetric Comparison of Craniospinal Irradiation Using Different Tomotherapy Techniques.

The objective of this study is to compare the new and conventional tomotherapy treatment techniques and to evaluate dosimetric differences between them. A dosimetric analysis was performed by comparing planning target volume (PTV) median dose, 95% of PTV dose coverage, Paddick conformity index (CI), homogeneity index (HI), whole-body integral dose, and OAR median doses. The beam on time (BOT) and the effect of different jaw sizes and pitch values was studied. The study results indicated that the PTV dose coverage for all the techniques was comparable. Treatment plans using dynamic jaw reduced OAR doses to structures located at the treatment field edge compared to fixed jaw plans. The HT-3DCRT plans resulted in higher OAR doses to kidney, liver, and lung compared to the other techniques, and TD-IMRT provided the best dose sparing to liver compared to other techniques. Whole-body integral dose differences were found to be insignificant among the techniques. BOT was found to be higher for fixed jaw treatment plan compared to dynamic jaw plan and comparable between all treatment techniques with 5-cm dynamic jaw. In studying effect of jaw size, better OAR sparing and HI were found for 2.5-cm jaw but at the expense of doubling of BOT as compared to 5-cm jaw. There was no significant improvement found in OAR sparing when the pitch value was increased. Increasing the pitch from 0.2 to 0.43, the CI was improved, HI improved only for 5-cm jaw size, and BOT decreased to approximately half of its original time.

MLC dosimetric characteristics for small field and IMRT applications.

The objective of this work was to measure the performance characteristics of a double-focus multileaf collimator (MLC) for intensity modulated radiation therapy (IMRT), specifically the variation in penumbra and leakage for narrow fields as a function of field position over a 20x27 cm space available for segmented MLC IMRT. Measurements were made with 6 MV x rays through a MLC containing 29 leaf pairs (27 pairs of 1 cm width), and EDR2 film at 10 cm depth in solid water at 100 cm SAD. Films were digitized with 0.17 mm resolution and converted to dose. Interleaf and intraleaf transmission were measured along 11 vertical profile locations. Leaf-end transmission was measured along horizontal profiles for each of 9 different leaf abutments, traveling over a 20 cm range. In-plane penumbra measurements were made through a single leaf retracted, for 7 different leaves. Cross-plane penumbra (leaf-end) measurements were made for all 27 leaf pairs, where the 1 cm field width was placed in 11 different off-axis positions (20 cm range). Interleaf leakage (range 1.0%-1.5%), intraleaf transmission (range 0.6%-0.8%), and leaf-end transmission (range 0.8%-2.7%) were consistent for all leaf pairs at a given abutment position. The penumbra for these 1-cm-wide fields was measured to be 0.36 cm+/-0.03 cm for 99% of the measurements. In conclusion, the penumbra and leakage of the double-focus MLC were remarkably consistent for the range of leaf positions studied, producing dosimetric characteristics that are well suited for IMRT segments where opposing leaf pairs are often separated by 10 mm or less.

MLC quality assurance techniques for IMRT applications.

Intensity modulated radiotherapy (IMRT) requires extensive knowledge of multileaf collimator (MLC) leaf positioning accuracy, precision, and long-term reproducibility. We have developed a technique to efficiently measure the absolute position of each MLC leaf, over the range of leaf positions utilized in IMRT, based on dosimetric information. A single radiographic film was exposed to 6 MV x-rays for twelve exposures: one open field with a radio-opaque marker tray present, and eleven fields (1 x 28 cm strips via 1 cm gaps between opposed leaf pairs) separated by 2 cm center to center. The process was repeated while varying direction of leaf travel; each film was digitized using a commercial film dosimetry system. The digital images were manipulated to remove translation and rotation of the film data with respect to the collimator coordinate system by extraction of radiation dose profiles perpendicular to the MLC leaf motion and measuring the center of the x-ray leakage between leaves. Radiation dose profiles in the direction of leaf motion were acquired through the center of each leaf pair (leaves 2-28), which provided leaf position information every 2 cm with 0.2 mm precision. Nine separate leaf reproducibility studies over a 90 day period which evaluated 600 measurement points on each film show 0.3 mm precision for 95% confidence, while hysteresis studies show 0.5 mm precision. Absolute leaf position error measurements demonstrated a radial dependence, with a maximum of 1.5 mm at 16.4 cm from central axis, due to rotational error at calibration. Recalibration of the MLC leaves based utilizing this tool yields absolute leaf position measurements where 91.5% of all leaves/positions were within 0.5 mm, with a mean error of 0.1 mm and a maximum error less than 1.0 mm.

Bi-modality (chemo-radiation) versus tri-modality (chemo-radiation followed by surgery) treatment for carcinoma of the esophagus.

The purpose of the study was to investigate the difference in overall survival in patients with localized carcinoma of esophagus treated using chemo-radiation (bi-modality, BM) or chemo-radiation followed by surgery (tri-modality, TM). From 1981 to 1999, 65 patients were identified who had localized carcinoma of the esophagus treated with either concurrent chemo-radiation (BM, n=22) or concurrent chemo-radiation followed by surgery (TM, n=43) at the University of Texas Medical Branch at Galveston. All 65 patients received concurrent chemotherapy and external beam radiation. Radiation was delivered by linear accelerators (greater-than-or-equal 6 MV), except in one patient who had part of his treatment given by a Co-60 machine. Chemotherapy consisted of 5-fluorouracil and cisplatin plus minus vinblastine under different regimens. Median follow-up time was 10 months (range=1-195 months) for all patients. Of the 14 patients still alive, the median follow-up time was 32 months (range=2-192 months). No difference in overall survival was detected between the two treatment groups, BM vs. TM (P=0.394) despite a selection bias favoring the TM group. Five-year survival rates of the BM and TM groups were 17% and 18%, respectively; 10-year survival rates were 17% and 12%, respectively. The presence of significant past medical history (P=0.017) and a complete pathologic response in the TM group (P < 0.001) were significant independent predictors of survival. We did not find any difference in survival between chemo-radiation or chemo-radiation followed by surgery in patients with localized carcinoma of the esophagus. Use of biologic markers and functional imaging should be explored in order to segregate patients with different tumor biology for treatment using different treatment strategies.

A test of the claim that plan rankings are determined by relative complication and tumor-control probabilities.

PURPOSE: This study tests an accepted claim regarding tumor control (TCP) and normal tissue complication (NTCP) probability functions. The claim is that treatment plans can be ranked using relative probabilities, even when the absolute probabilities are unknown. The assumption supports the use of probability models for plan optimization and the comparison of treatment techniques. METHODS: The claim was tested using a hypothetical model consisting of two tissues, and illustrated with clinical data. Plans were scored using the probability of uncomplicated tumor control. The scores of different plans were compared by fixing their relative risks for an individual tissue complication, but adjusting the absolute probability levels up or down. The tested claim is that the plan rankings should not change. RESULTS: In the two-tissue model, the rankings of competing plans were reversed by doubling all the probabilities. The preference ordering of lung cancer plans changed after the risk of pulmonary complication was reduced by 3-fold. In another site, the ranking of plans by overall complication-free probability was disturbed by errors that preserved the ordering of plans with respect to any individual complication. An adjustment of +/- 2.5% in the initial NTCP values for two tissues changed the direction in which a plan score moved in response to a fixed tradeoff in complication risk in an optimization search. CONCLUSIONS: Contrary to claims, plan rankings are not determined by the relative probabilities of adverse events. The effect on plan scores of trading one complication for another depends on the absolute levels of risk. Absolute errors in NTCP and TCP functions result in the wrong ranking of plans, even when relative probabilities are correct. An optimization routine based on TCP and NTCP calculations may be forced in the wrong direction by small errors in the probability estimates.

A comparison of mixed integer programming and fast simulated annealing for optimizing beam weights in radiation therapy.

Two competing methods for assigning intensities to radiation treatment beams were tested. One method was derived from mixed integer programming and the other was based on simulated annealing. The methods faced a common objective and identical constraints. The goal was to maximize the minimum tumor dose while keeping the dose in required fractions of normal organ volumes below a threshold for damage. The minimum tumor doses of the two methods were compared when all the dose-volume constraints were satisfied. A mixed integer linear program gave a minimum tumor dose that was at least 1.8 Gy higher than that given by simulated annealing in 7 of 19 trials. The difference was > or = 5.4 Gy in 4 of 19 trials. In no case was the mixed integer solution one fraction size (1.8 Gy) worse than that of simulated annealing. The better solution provided by the mixed integer program allows tumor dose to increase without violating the dose-volume limits of normal tissues.

A generic genetic algorithm for generating beam weights.

A genetic algorithm for generating beam weights is described. The algorithm improves an objective measure of the dose distribution while respecting dose volume constraints placed on critical structures. The algorithm was used to select beam weights for treatment of abdominal tumors. Weights were selected for up to 36 beams. Dose volume limits were placed on normal organs and a dose inhomogeneity limit was placed on tumor. Volumes were represented as sets of several hundred discrete points. The algorithm searched for the beam weights that would make the minimum tumor dose as high as the constraints would allow. The results were checked using dose volume histograms with standard sized grids. Nineteen trials were created using six patient cases by changing the required field margin or allowed beam position in each case. The sampling of points was sufficiently dense to yield solutions that strictly satisfied the constraints when the prescribed dose was renormalized by a factor of less than 6%. The genetic algorithm supplied solutions in 49 min on average, and in a maximum time of 87 min. The randomized search does not guarantee optimality, but high tumor doses were obtained. An example is shown for which the solution of the genetic algorithm gave a minimum tumor dose 7 Gy higher than the solution given by a simulated annealing algorithm under the same set of constraints. The genetic algorithm can be generalized to admit nonlinear functions of the beam intensities in the objective or in the constraints. These can include tumor control and normal tissue complication probabilities. The genetic algorithm is an attractive procedure for assigning beam weights in multifield plans. It improves the dose distribution while respecting specified rules for tissue tolerance.

Comparison of simulated annealing algorithms for conformal therapy treatment planning.

PURPOSE: The efficiency of four fast simulated annealing algorithms for optimizing conformal radiation therapy treatment plans was studied and the resulting plans were compared with each other and to optimized conventional plans. METHODS AND MATERIALS: Four algorithms were selected on the basis of their reported successes in solving other minimization problems: fast simulated annealing with a Cauchy generating function, fast simulated annealing with a Lorentzian generating function, variable step size generalized simulated annealing (VSGSA), and very fast simulated reannealing (VFSR). They were tested on six clinical cases using a multiple beam coplanar conformal treatment technique. Relative beam weights were computed that maximized the minimum tumor dose subject to dose-volume constraints on normal organ doses. Following some initial tuning of the annealing parameters, each algorithm was applied identically to each test case. Optimization tests were run using different random number sequences and different numbers of iterations. RESULTS: The VSGSA algorithm consistently produced the best results. Using long run times, it generated plans with the highest minimum tumor dose in five of the six cases. For the short run times, the VSGSA solutions averaged larger minimum tumor doses than those of the other algorithms for all six patients, with increases ranging from 0.4 to 5.9 Gy. For three of the patients, the conformal plan gave a clinically significant increase in the minimum tumor dose over the conventional plan, ranging from 8.2 to 13.0 Gy. In two other cases, there was little difference between the two treatment approaches. For one case, the optimized conventional plan was much better than the conformal plan because the conventional beam arrangement included wedges, which offset the multiple beam advantage of the conformal plans. CONCLUSIONS: For equal computing times of both long and short duration, the VSGSA algorithm consistently produced conformal plans that were superior to those produced by the other algorithms. The simple conformal technique used in this study showed a significant potential advantage in the treatment of abdominal tumors. In three of the cases, the conformal plans showed clinically important increases in tumor dose over optimized conventional plans.

Very fast simulated reannealing in radiation therapy treatment plan optimization.

PURPOSE: Very Fast Simulated Reannealing is a relatively new (1989) and sophisticated algorithm for simulated annealing applications. It offers the advantages of annealing methods while requiring shorter execution times. The purpose of this investigation was to adapt Very Fast Simulated Reannealing to conformal treatment planning optimization. METHODS AND MATERIALS: We used Very Fast Simulated Reannealing to optimize treatments for three clinical cases with two different cost functions. The first cost function was linear (minimum target dose) with nonlinear dose-volume normal tissue constraints. The second cost function (probability of uncomplicated local control) was a weighted product of normal tissue complication probabilities and the tumor control probability. RESULTS: For the cost functions used in this study, the Very Fast Simulated Reannealing algorithm achieved results within 5-10% of the final solution (100,000 iterations) after 1000 iterations and within 3-5% of the final solution after 5000-10000 iterations. These solutions were superior to those produced by a conventional treatment plan based on an analysis of the resulting dose-volume histograms. However, this technique is a stochastic method and results vary in a statistical manner. Successive solutions may differ by up to 10%. CONCLUSION: Very Fast Simulated Reannealing, with modifications, is suitable for radiation therapy treatment planning optimization. It produced results within 3-10% of the optimal solution, produced using another optimization algorithm (Mixed Integer Programming), in clinically useful execution times.

Tissue heterogeneity effects in treatment plan optimization.

PURPOSE: There is general agreement that tissue density correction factors improve the accuracy of dose calculations. However, there is disagreement over the proper heterogeneity correction algorithm and a lack of clinical experience in using them. Therefore, there has not been widespread implementation of density correction factors into clinical practice. Furthermore, the introduction of optimized conformal therapy leads to new and radically different treatment techniques outside the clinical experience of the physician. It is essential that the effects of tissue density corrections are understood so that these types of treatments can be safely delivered. METHODS AND MATERIALS: In this paper, we investigate the effect of tissue density corrections on optimized conformal type treatment planning in the thorax region. Specifically, we study the effects on treatment plans optimized without type treatment planning in the thorax region. Specifically, we study the effects on treatment plans optimized without tissue density corrections, when those corrections are applied to the resulting dose distributions. These effects are compared for two different conformal techniques. RESULTS: This study indicates that failure to include tissue density correction factors results in an increased dose of approximately 5-15%. This is consistent with published studies using conventional treatment techniques. Additionally, the high-dose region of the dose distribution expands laterally into the uninvolved lung and other normal structures. The use of dose-volume histograms to compare these distributions demonstrates that treatment plans optimized without tissue density corrections lead to an increased dose to uninvolved normal structures. This increase in dose often violates the constraints used to determine the optimal solution. CONCLUSIONS: The neglect of tissue density correction factors can result in a 5-15% increase in the delivered dose. In addition, suboptimal dose distributions are produced. To benefit from the advantages of optimized conformal therapy in the thorax, tissue density correction factors should be used.

Optimized dynamic rotation with wedges.

Dynamic rotation is a computer-controlled therapy technique utilizing an automated multileaf collimator in which the radiation beam shape changes dynamically as the treatment machine rotates about the patient so that at each instant the beam shape matches the projected shape of the target volume. In simple dynamic rotation, the dose rate remains constant during rotation. For optimized dynamic rotation, the dose rate is varied as a function of gantry angle. Optimum dose rate at each gantry angle is computed by linear programming. Wedges can be included in the optimized dynamic rotation therapy by using additional rotations. Simple and optimized dynamic rotation treatment plans, with and without wedges, for a pancreatic tumor have been compared using optimization cost function values, normal tissue complication probabilities, and positive difference statistic values. For planning purposes, a continuous rotation is approximated by static beams at a number of gantry angles equally spaced about the patient. In theory, the quality of optimized treatment planning solutions should improve as the number of static beams increases. The addition of wedges should further improve dose distributions. For the case studied, no significant improvements were seen for more than 36 beam angles. Open and wedged optimized dynamic rotations were better than simple dynamic rotation, but wedged optimized dynamic rotation showed no definitive improvement over open beam optimized dynamic rotation.

Treatment planning optimization using constrained simulated annealing.

A variation of simulated annealing optimization called 'constrained simulated annealing' is used with a simple annealing schedule to optimize beam weights and angles in radiation therapy treatment planning. Constrained simulated annealing is demonstrated using two contrasting objective functions which incorporate both biological response and dose-volume considerations. The first objective function maximizes the probability of a complication-free treatment (PCFT) by minimizing the normal tissue complications subject to the constraint that the entire target volume receives a prescribed minimum turmourcidal dose with a specified dose homogeneity. Probabilities of normal tissue complication are based on published normal tissue complication probability functions and computed from dose-volume histograms. The second objective function maximizes the isocentre dose subject to a set of customized normal tissue dose-volume and target volume dose homogeneity constraints (MVDL). Although the PCFT objective function gives consistently lower estimates of normal tissue complication probabilities, the ability to specify individualized dose-volume limits, and therefore the individualized probability of complication, for an individual organ makes the MDVL objective function more useful for treatment planning.

Treatment plan optimization using linear programming.

Linear programming is a versatile mathematical tool for optimizing radiation therapy treatment plans. For planning purposes, dose constraint points, possible treatment beams, and an objective function are defined. Dose constraint points are specified in and about the target volume and normal structures with minimum and maximum dose values assigned to each point. A linear objective function is designed that defines the goal of optimization. A list of potential treatment beams is defined by energy, angle, and wedge selection. Then, linear programming calculates the relative weights of all the potential beams such that the objective function is optimized and doses to all constraint points are within the prescribed limits. Historically, linear programming has been used to improve conventional treatment techniques. It can also be used to create sophisticated, complex treatment plans suitable for delivery by computer-controlled therapy techniques.

Constrained simulated annealing for optimized radiation therapy treatment planning.

A variation of simulated annealing optimization called 'constrained simulated annealing' is used with a simple annealing schedule to automatically optimize beam weights and beam angles in radiation therapy treatment planning. This optimization technique permits the straightforward utilization of any objective function and any set of dose constraints, even those described by non-analytic functions. Constrained simulated annealing is demonstrated using an objective function which minimizes the probability of normal tissue complications subject to the constraint that the entire target volume receive a tumoricidal dose within specified maximum and minimum limits. Target volume dose constraints are determined by points located on the perimeter of the target volume. Probabilities of normal tissue complications are based on published normal tissue complication probability functions and computed from dose-volume histograms calculated on points spread throughout the normal anatomy.

The influence of dose constraint point placement on optimized radiation therapy treatment planning.

To efficiently use linear and quadratic programming for treatment planning optimization on a routine basis, automated methods are needed for placing dose constraint points. We have investigated, for linear programming optimization, the minimum number of constraint points needed to achieve an acceptable approximation to the desired (ideal) solution. Seven different constraint point placement algorithms were evaluated for a given objective function. One of these algorithms was chosen for routine clinical use at our institution. This algorithm places constraint points on the perimeter of the target volume and on the perimeter and in the interior of each normal structure. Additional points are placed on the perimeter of a constant thickness buffer region surrounding the target volume. Excellent optimization results are obtained with 40-70 constraint points per treatment planning slice.