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Objective:: Radiobiological models have been used to calculate the outcomes of treatment plans based on dose-volume relationship. This study examines several radiobiological models for the calculation of tumor control probability (TCP) of intensity modulated radiotherapy plans for the treatment of lung, prostate, and head and neck (H&N) cancers., Methods:: Dose volume histogram (DVH) data from the intensity modulated radiotherapy plans of 36 lung, 26 prostate, and 87  H&N cases were evaluated. The Poisson, Niemierko, and Marsden models were used to calculate the TCP of each disease group treatment plan. The calculated results were analyzed for correlation and discrepancy among the three models, as well as different treatment sites under study., Results:: The median value of calculated TCP in lung plans was 61.9% (34.1-76.5%), 59.5% (33.5-73.9%) and 32.5% (0.0-93.9%) with the Poisson, Niemierko, and Marsden models, respectively. The median value of calculated TCP in prostate plans was 85.1% (56.4-90.9%), 81.2% (56.1-88.7%) and 62.5% (28.2-75.9%) with the Poisson, Niemierko, and Marsden models, respectively. The median value of calculated TCP in H&N plans was 94.0% (44.0-97.8%) and 94.3% (0.0-97.8%) with the Poisson and Niemierko models, respectively. There were significant differences between the calculated TCPs with the Marsden model in comparison with either the Poisson or Niemierko model (p < 0.001) for both lung and prostate plans. The TCPs calculated by the Poisson and Niemierko models were significantly correlated for all three tumor sites., Conclusion:: There are variations with different radiobiological models. Understanding of the correlation and limitation of a TCP model with dosimetric parameters can help develop the meaningful objective functions for plan optimization, which would lead to the implementation of outcome-based planning. More clinical data are needed to refine and consolidate the model for accuracy and robustness., Advances in Knowledge:: This study has tested three radiobiological models with varied disease sites. It is significant to compare different models with the same data set for better understanding of their clinical applicability.

Radiation oncologists need reliable estimates of risk for various fractionation schemes for all critical anatomical structures throughout the body, in a clinically convenient format. Reliable estimation theory can become fairly complex, however, and estimates of risk continue to evolve as the literature matures. To navigate through this efficiently, a dose-volume histogram (DVH) Risk Map was created, which provides a comparison of radiation tolerance limits as a function of dose, fractionation, volume, and risk level. The graphical portion of the DVH Risk Map helps clinicians to easily visualize the trends, whereas the tabular portion provides quantitative precision for clinical implementation. The DVH Risk Map for rib tolerance from stereotactic ablative body radiotherapy (SABR) and stereotactic body radiation therapy (SBRT) is used as an example in this overview; the 5% and 50% risk levels for 1-5 fractions for 5 different volumes are given. Other articles throughout this issue of Seminars in Radiation Oncology present analysis of new clinical datasets including the DVH Risk Maps for other anatomical structures throughout the body., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Understanding dose constraints for critical structures in stereotactic body radiation therapy (SBRT) is essential to generate a plan for optimal efficacy and safety. Published dose constraints are derived by a variety of methods, including crude statistics, actuarial analysis, modeling, and simple biologically effective dose (BED) conversion. Many dose constraints reported in the literature are not consistent with each other, secondary to differences in clinical and dosimetric parameters. Application of a dose constraint without discriminating the variation of all the factors involved may result in suboptimal treatment. This issue of Seminars in Radiation Oncology validates dose tolerance limits for 10 critical anatomic structures based on dose response modeling of clinical outcomes data to include detailed dose-volume metrics. This article presents a logistic dose-response model for aorta and major vessels based on 238 cases from the literature in addition to 387 cases from MD Anderson Cancer Center at Cooper University Hospital, for a total of 625 cases. The Radiation Therapy Oncology Group (RTOG) 0813 dose-tolerance limit of Dmax = 52.5Gy in 5 fractions was found to have a 1.2% risk of grade 3-5 toxicity, and the Timmerman 2008 limit of Dmax = 45Gy in 3 fractions had 2.3% risk. From the model, the 1% and 2% risk levels for D4cc, D1cc, and D0.5cc are also provided in 1-5 fractions, in the form of a dose-volume histogram (DVH) Risk Map., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Almost 20 years ago, Emami et al. presented a comprehensive set of dose tolerance limits for normal tissue organs to therapeutic radiation, which has proven essential to the field of radiation oncology. The paradigm of stereotactic body radiotherapy (SBRT) has dramatically different dosing schemes but, to date, there has still been no comprehensive set of SBRT normal organ dose tolerance limits. As an initial step toward that goal, we performed an extensive review of the literature to compare dose limits utilized and reported in existing publications. The impact on dose tolerance limits of some key aspects of the methods and materials of the various authors is discussed. We have organized a table of 500 dose tolerance limits of normal structures for SBRT. We still observed several dose limits that are unknown or not validated. Data for SBRT dose tolerance limits are still preliminary and further clinical trials and validation are required. This manuscript presents an extensive collection of normal organ dose tolerance limits to facilitate both clinical application and further research.

Purpose: To study the impact of seed localization, as performed by different observers using linked (125)I seeds, on postimplant dosimetry in prostate brachytherapy and, to compare transrectal ultrasound (TRUS)-based with CT-based approach for the dosimetric outcomes., Methods and Materials: Nineteen permanent prostate implants were conducted using linked (125)I seeds. Postimplant TRUS and CT images were acquired and prostate glands were, after implantation, delineated on all images by a single oncologist, who had performed all 19 seeding procedures. Six observers independently localized the seeds on both TRUS and CT images, from which the principle dosimetric parameters V(100) (volume of prostate that received the prescribed dose), V(150) (volume of prostate that received 150% of the prescribed dose), and D(90) (minimal dose delivered to 90% of the prostate) were directly calculated for each patient. A single-factor analysis of variance was first applied to determine interobserver variability in seed localization. A nonparametric comparison of the approach using TRUS and CT was then carried out by the Wilcoxon paired-sample test., Results: Analysis from the analysis of variance for TRUS showed that the null hypothesis for equal means, could not be rejected for all six observers based on a significance level alpha=0.05. TRUS-based and CT-based approaches were then cross compared by the Wilcoxon paired-sample test, which suggested that the null hypothesis was insignificant for V(100) and D(90), but was significant for V(150)., Conclusions: Both TRUS- and CT-imaging modalities provided indistinguishable postimplant dosimetry results as far as V(100) and D(90) were concerned. There was comparable observer independence between TRUS- and CT-based seed localization for linked-seed implant procedures. With other advantages that TRUS-imaging modality had over CT in the evaluation of postimplant dosimetry, TRUS would be a preferred choice in conjunction with linked seeds for intraoperative procedures in prostate brachytherapy.

Purpose: To investigate interobserver variability in contouring the prostate on postimplant transrectal ultrasonography (TRUS) images and its effect on dosimetric parameters that quantify implant quality., Methods and Materials: Twenty preplanned peripherally loaded prostate implants were performed using 125I seeds and spacers linked together in linear arrays that maintain precise seed spacing and prevent seeds from rotating about their longitudinal axis. A set of two-dimensional transverse images spaced at 0.50-cm intervals was obtained with a high-resolution TRUS probe at the conclusion of the procedure with the patient still under anesthesia. A high percentage of the seeds (> 85%) were localized based on their visible echoes. The remaining seeds were identified based on the known locations of the "missing" seeds in the arrays. Two experienced ultrasonographers and a prostate brachytherapist independently contoured the prostate on the postimplant TRUS images. The prostate volumes defined by each observer were used to calculate the minimal dose received by 90% of the prostate volume (D90) and the percentage of the prostate volume receiving 100% of the prescribed minimal peripheral dose (V100). The observers also contoured the prostate on six preimplant TRUS studies to compare the variability in defining the prostate on pre- and postimplant TRUS images., Results: The mean postimplant prostate volumes ranged from 20.8 to 66.9 cm3 (median: 45.7 cm3). The standard deviations (SDs), which reflect the variation in the volumes of the three observers, ranged from 1.4% to 26.1% of the mean (median: 11%). Multiple pairwise comparisons showed that the prostate volumes delineated by observer 3 differed significantly from those of observers 1 and 2 (p < 0.003). The volumes of observers 1 and 2 were not significantly different (p > 0.5). The mean values of D90 ranged from 124.2 to 171.1 Gy (median: 154.7 Gy) having SDs that ranged from 0.6% to 24.4% of the mean D90 (median: 7.8%). The mean values of V100 ranged from 82.3% to 95.1% (median: 92.8%) having SDs that ranged from 0.4% to 11.2% of the mean V100 (median: 4.0%). The values of both D90 and V100 calculated from the volumes of observer 3 were significantly (p < 0.003) different from those of observers 1 and 2, which did not differ significantly (p > 0.5). There was less interobserver variability in contouring the preimplant TRUS volumes. The mean volumes ranged from 20.3 to 54.3 cm3 having SDs that ranged from 1.9% to 14.1% (median: 8.6%)., Conclusions: Significant interobserver differences in delineating the prostate volume on postimplant TRUS images were observed; however, these differences were less than generally reported for postimplant CT images. The interobserver differences in contouring the prostate in both TRUS and CT images produced significant differences in the dosimetric parameters, D90 and V100.

Stepped leaf edges are the major limitation of conforming to the prescribed treatment contour defined by the conventional multileaf collimator (MLC), which produces a scalloped dose pattern. The commercial HD-270 MLC (HDI) technique provides a software solution of the conventional MLC to achieve smoothed edge and optimal penumbra of the MLC shaped field. We implemented the HDI functionality on a 3D treatment planning system and compared the dosimetric effects of the HDI delivery in simulation with those in experiment for a number of the MLC fields. The fields from the contour of varied shapes with different sizes of the leaf stepping were tested for the HDI delivery. There is a good agreement of the dose distribution between the calculation as implemented in the planning system and the measurement performed on the treatment machine. It has been shown that the HDI delivery significantly smooths the stepped field edge with the reduced isodose undulation and effective penumbra. A problem may be present when the HDI is applied for the treatment of the circular contour of smaller diameter, and the conformity of the MLC shaping may not be achievable satisfactorily with the existing system. The optimization of leaf configuration is suggested to improve the conformity of the HDI technique. The HDI planning then can be used to assist in the decision making of applying the HDI treatment delivery.