Dose-response modeling and treatment plan assessment with a python software toolkit.

This software assistant aims at calculating the dose-response relations of tumors and normal tissues, or clinically assessing already determined values by other researchers. It can also indicate the optimal dose prescription by optimizing the expected treatment outcome. The software is developed solely in python programming language, and it employs PSFL license for its Graphical User Interface (GUI), NUMPY, MATPLOTLIB, and SCIPY libraries. It comprises of two components. The first is the Dose-response relations derivation component, which takes as input the dose volume histograms (DVHs) of patients and their recorded responses regarding a given clinical endpoint to determine the parameters of different tumor control probability (TCP) or normal tissue complication probability (NTCP) models. The second is the Treatment Plan Assessment component, which uses the DVHs of a plan and the dose-response parameters values of the involved tumors and organs at risk (OARs) to calculate their expected responses. Additionally, the overall probabilities of benefit (PB), injury (PI) and complication-free tumor control (P+) are calculated. The software calculates rapidly the corresponding generalized equivalent uniform doses (gEUD) and biologically effective uniform doses (D‾‾) for the Lyman-Kutcher-Burman (LKB), parallel volume (PV) and relative seriality (RS) models respectively, determining the model parameters. In the Dose-Response Relations Derivation component, the software plots the dose-response curves of the irradiated organ with the relevant confidence internals along with the data of the patients with and without toxicity. It also calculates the odds ratio (OR) and the area under the curve (AUC) of different dose metrics or model parameter values against the individual patient outcomes to determine their discrimination capacity. It also performs a goodness-of-fit evaluation of any model parameter set. The user has the option of viewing plots like Scatter, 3D surfaces, and Bootstrap plots. In the Treatment Plan Assessment part, the software calculates the TCP and NTCP values of the involved tumors and OARs, respectively. Furthermore, it plots the dose-response curves of the TCPs, NTCPs, PB, PI, and P+ for a range of prescription doses for different treatment plans. The presented software is ideal for efficiently conducting studies of radiobiological modeling. Furthermore, it is ideal for performing treatment plan assessment, comparison, and optimization studies.

Objective Assessment of the Quality and Accuracy of Deformable Image Registration.

BACKGROUND: The increased use of deformable registration algorithms in clinical practice has also increased the need for their validation. AIMS AND OBJECTIVES: The purpose of the study was to investigate the quality, accuracy, and plausibility of three commercial image registration algorithms for 4-dimensional computed tomography (4DCT) datasets using various similarity measures. MATERIALS AND METHODS: 4DCT datasets were acquired for 10 lung cancer patients. 23 similarity measures were used to evaluate image registration quality. To ensure selected method's invertibility and assess resultant mechanical stress, the determinant of the Jacobian for the displacement field and 3-D Eulerian strain tensor were calculated. All the measures and calculations were applied on to extended deformable multi pass (EXDMP) and deformable multi pass (DMP) methods. RESULTS: The results indicate the same trend for several of the studied measures. The Jacobian determinant values were always positive for the DMP method. The Eulerian strain tensor had smaller values for the DMP method than EXDMP in all of the studied cases. The negative values of the Jacobian determinant point to non-physical behavior of the EXDMP method. The Eulerian strain tensor values indicate less tissue strain for the DMP method. Large differences were also observed in the results between complete and cropped datasets (coefficient of determination: 0.55 vs. 0.93). CONCLUSION: A number of error and distance measures showed the best performance among the tested measures. The evaluated measures might detect CT dataset differences with higher precision if the analysis is restricted to a smaller volume.

VMAT Optimization and Dose Calculation in the Presence of Metallic Hip Prostheses.

INTRODUCTION: This research quantifies and compares the effect of hip prostheses on dose distributions calculated using collapsed cone convolution superposition and Monte Carlo (with and without correcting for the density of the implant and surrounding tissues). The use of full volumetric modulated arc therapy arcs versus volumetric modulated arc therapy arcs avoiding the hip implants (skip arcs) was also studied. MATERIALS AND METHODS: Six prostate patients with hip prostheses were included in this study. The hip prostheses and the streaking artifacts on the computed tomography images were contoured by a single physician, and full volumetric modulated arc therapy arcs were created in the Pinnacle3 TPS. Copies of each plan were made, and the doses were recalculated with the densities of the prostheses and surrounding tissues overridden. The plans were then exported to Monaco and recalculated using a Monte Carlo dose calculation algorithm, with and without densities of the prosthesis and surrounding tissues overridden. RESULTS: With density overrides, Pinnacle3 had a 4.4% error for ion chamber measurements. Monaco was within 0.2% of ion chamber measurement when density overrides were used. On average, when density overrides were used in Pinnacle3 for patient dose calculations, the planning target volume D95 value dropped from 99.3% to 82.7%. Monaco also showed decreased planning target volume coverage when plans were recalculated with correct density information. Full arc plans (with density overrides) for the patient with a bilateral prosthesis provided significant bladder sparing and some rectal sparing compared to skip arc plans. CONCLUSION: When planning for prostate patients with hip prostheses, correct density information for implants and surrounding tissues should be used to optimize the plan and ensure optimal accuracy. If available, a Monte Carlo algorithm should be used as a second check. Full arcs could be used to spare dose to organs at risk, while maintaining adequate planning target volume coverage, when using a Monte Carlo dose calculation algorithm.

Prospective Assessment of Patient-Reported Dry Eye Syndrome After Whole Brain Radiation.

PURPOSE: Dry eye is not typically considered a toxicity of whole brain radiation therapy (WBRT). We analyzed dry eye syndrome as part of a prospective study of patient-reported outcomes after WBRT. METHODS AND MATERIALS: Patients receiving WBRT to 25 to 40 Gy were enrolled on a study with dry mouth as the primary endpoint and dry eye syndrome as a secondary endpoint. Patients received 3-dimensional WBRT using opposed lateral fields. Per standard practice, lacrimal glands were not prospectively delineated. Patients completed the Subjective Evaluation of Symptom of Dryness (SESoD, scored 0-4, with higher scores representing worse dry eye symptoms) at baseline, immediately after WBRT (EndRT), and at 1 month (1M), 3 months, and 6 months. Patients with baseline SESoD ≥3 (moderate dry eye) were excluded. The endpoints analyzed were ≥1-point and ≥2-point increase in SESoD score at 1M. Lacrimal glands were retrospectively delineated with fused magnetic resonance imaging scans. RESULTS: One hundred patients were enrolled, 70 were eligible for analysis, and 54 were evaluable at 1M. Median bilateral lacrimal V20Gy was 79%. At 1M, 17 patients (32%) had a ≥1-point increase in SESoD score, and 13 (24%) a ≥2-point increase. Lacrimal doses appeared to be associated with an increase in SESoD score of both ≥1 point (V10Gy: P = .042, odds ratio [OR] 1.09/%; V20Gy: P = .071, OR 1.03/%) and ≥2 points (V10Gy: P = .038, OR 1.15/%; V20Gy: P = .063, OR 1.04/%). The proportion with increase in dry eye symptoms at 1M for lacrimal V20Gy ≥79% versus <79% was 46% versus 15%, respectively, for ≥1 point SESoD increase (P = .02) and 36% versus 12%, respectively, for ≥2 point SESoD increase (P = .056). CONCLUSIONS: Dry eye appears to be a relatively common, dose/volume-dependent acute toxicity of WBRT. Minimization of lacrimal gland dose may reduce this toxicity, and patients should be counseled regarding the existence of this potential side effect and treatments for dry eye.

Treatment planning dose accuracy improvement in the presence of dental implants.

Streaking artifacts in computed tomography (CT) scans caused by metallic dental implants (MDIs) can lead to inaccuracies in dose calculations. This study quantifies and compares the effect of MDIs on dose distributions using the collapsed cone convolution superposition (CCCS) and Monte Carlo (MC) algorithms, with and without correcting for the density of the MDIs. Ion chamber measurements were taken to test the ability of the algorithms in Pinnacle3 and Monaco to calculate dose near high-Z materials. Nine previously treated patients with head and neck cancer were included in this study. The MDI and the streaking artifacts on the CT images were carefully contoured. For each patient, a plan was optimized and calculated using the Pinnacle3 treatment planning system (TPS). Two dose calculations were performed for each patient: one with overridden densities of the MDI and CT artifacts and one without overridden densities of the MDI and CT artifacts. The plans were then exported to the Monaco TPS and recalculated for the same number of monitor units (MUs) using its MC dose calculation algorithm. The changes in dose to the planning target volume (PTV) and surrounding healthy tissues were examined between all the plans using VelocityAI. For the ion chamber measurements, when correct density information was used, Monaco was within 3% of the measured values, whereas the doses calculated in Pinnacle3 varied up to 7%. The CCCS algorithm in Pinnacle3 calculated only a significant decrease in PTV coverage for 1 patient when the densities were overridden. The MC algorithm in Monaco was able to calculate a significant change in PTV coverage for five of the patients when the density was overridden. Additionally, when healthy tissues affected by streaking artifacts were assigned the correct density, cumulative (from all the fractions) point doses increased up to 46.2 Gy. Not properly accounting for MDIs can impact both the high-dose regions (PTVs) and surrounding healthy tissues. This study demonstrates that if MDIs and the artifacts are not appropriately accounted for by contouring and assigning to them the correct density, there is a potential risk of compromising the quality of the plan regarding PTV coverage and dose to healthy tissues.

Assessment of Lung Tumour Motion and Volume Size Dependencies Using Various Evaluation Measures.

The purpose of the study was to assess internal target volume changes through the breathing cycle and associated tumour motion for lung patients and to establish possible correlations between different parameters. Respiration-induced volume changes with breathing cycle and the associated tumour motion were analyzed for 11 patients. Selected phases were the maximum and average intensity projections and the 10 phases of equal duration and separation obtained through the respiratory cycle. Tumour centre of mass (COM) motion planes were generated using least square fitting, and their angles and orientations were then compared between the cases studied. Trajectories that are composed by the points of COM location in different phases were identified, and their interrelation was assessed through different similarity measures. The results were used to determine if there is any correlation between parameters chosen and if the margins conventionally used for the planning target volume creation successfully encompassed lung tumour motion and volume change. The results show that the extent of tumour motion was related to its volume and location. The tumour displacement was predominantly left and inferior. Planar fitting to COM motion data through respiratory phases demonstrated some correlation in best fit motion plane positions between different data sets. In the plane fit comparison, for each patient, the lower root mean square error values showed that a good planar fit can be achieved for the COM motion path. The evaluation of the inhale and exhale trajectories may allow, for certain tumour locations and size, contouring on only inhalation or exhalation phases, knowing that tumour motion will be adequately covered on the other phases. Taking all the data into account and knowing the tumour size and location, a good estimate can be made of the motion plane position in the three-dimensional space and the required dosimetric margins.

Performance of independent dose calculation in helical tomotherapy: implementation of the MCSIM code.

Currently, a software-based second check dose calculation for helical tomotherapy (HT) is not available. The goal of this study is to evaluate the dose calculation accuracy of the in-house software using EGS4/MCSIM Monte Carlo environment against the treatment planning system calculations. In-house software was used to convert HT treatment plan information into a non-helical format. The MCSIM dose calculation code was evaluated by comparing point dose calculations and dose profiles against those from the HT treatment plan. Fifteen patients, representing five treatment sites, were used in this comparison. Point dose calculations between the HT treatment planning system and the EGS4/MCSIM Monte Carlo environment had percent difference values below 5 % for the majority of this study. Vertical and horizontal planar profiles also had percent difference values below 5 % for the majority of this study. Down sampling was seen to improve speed without much loss of accuracy. EGS4/MCSIM Monte Carlo environment showed good agreement with point dose measurements, compared to the HT treatment plans. Vertical and horizontal profiles also showed good agreement. Significant time saving may be obtained by down-sampling beam projections. The dose calculation accuracy of the in-house software using the MCSIM code against the treatment planning system calculations was evaluated. By comparing point doses and dose profiles, the EGS4/MCSIM Monte Carlo environment was seen to provide an accurate independent dose calculation.

The radiobiological P(+) index for pretreatment plan assessment with emphasis on four-dimensional radiotherapy modalities.

PURPOSE: Radiation treatment modalities will continue to emerge that promise better clinical outcomes albeit technologically challenging to implement. An important question facing the radiotherapy community then is the need to justify the added technological effort for the clinical return. Mobile tumor radiotherapy is a typical example, where 4D tumor tracking radiotherapy (4DTRT) has been proposed over the simpler conventional modality for better results. The modality choice per patient can depend on a wide variety of factors. In this work, we studied the complication-free tumor control probability (P(+)) index, which combines the physical complexity of the treatment plan with the radiobiological characteristics of the clinical case at hand and therefore found to be useful in evaluating different treatment techniques and estimating the expected clinical effectiveness of different radiation modalities. METHODS: 4DCT volumes of 18 previously treated lung cancer patients with tumor motion and size ranging from 2 mm to 15 mm and from 4 cc to 462 cc, respectively, were used. For each patient, 4D treatment plans were generated to extract the 4D dose distributions, which were subsequently used with clinically derived radiobiological parameters to compute the P(+) index per modality. RESULTS: The authors observed, on average, a statistically significant increase in P(+) of 3.4% ± 3.8% (p < 0.003) in favor of 4DTRT. There was high variability among the patients with a <0.5% up to 13.4% improvement in P(+). CONCLUSIONS: The observed variability in the improvement of the clinical effectiveness suggests that the relative benefit of tracking should be evaluated on a per patient basis. Most importantly, this variability could be effectively captured in the computed P(+). The index can thus be useful to discriminate and hence point out the need for a complex modality like 4DTRT over another. Besides tumor mobility, a wide range of other factors, e.g., size, location, fractionation, etc., can affect the relative benefits. Application of the P(+) objective is a simple and effective way to combine these factors in the evaluation of a treatment plan.

Comments on 'Reconsidering the definition of a dose-volume histogram'--dose-mass histogram (DMH) versus dose-volume histogram (DVH) for predicting radiation-induced pneumonitis.

In a recently published paper (Nioutsikou et al 2005 Phys. Med. Biol. 50 L17) the authors showed that the use of the dose-mass histogram (DMH) concept is a more accurate descriptor of the dose delivered to lung than the traditionally used dose-volume histogram (DVH) concept. Furthermore, they state that if a functional imaging modality could also be registered to the anatomical imaging modality providing a functional weighting across the organ (functional mass) then the more general and realistic concept of the dose-functioning mass histogram (D[F]MH) could be an even more appropriate descriptor. The comments of the present letter to the editor are in line with the basic arguments of that work since their general conclusions appear to be supported by the comparison of the DMH and DVH concepts using radiobiological measures. In this study, it is examined whether the dose-mass histogram (DMH) concept deviated significantly from the widely used dose-volume histogram (DVH) concept regarding the expected lung complications and if there are clinical indications supporting these results. The problem was investigated theoretically by applying two hypothetical dose distributions (Gaussian and semi-Gaussian shaped) on two lungs of uniform and varying densities. The influence of the deviation between DVHs and DMHs on the treatment outcome was estimated by using the relative seriality and LKB models using the Gagliardi et al (2000 Int. J. Radiat. Oncol. Biol. Phys. 46 373) and Seppenwoolde et al (2003 Int. J. Radiat. Oncol. Biol. Phys. 55 724) parameter sets for radiation pneumonitis, respectively. Furthermore, the biological equivalent of their difference was estimated by the biologically effective uniform dose (D) and equivalent uniform dose (EUD) concepts, respectively. It is shown that the relation between the DVHs and DMHs varies depending on the underlying cell density distribution and the applied dose distribution. However, the range of their deviation in terms of the expected clinical outcome was proven to be very large. Concluding, the effectiveness of the dose distribution delivered to the patients seems to be more closely related to the radiation effects when using the DMH concept.

Clinical validation of the LKB model and parameter sets for predicting radiation-induced pneumonitis from breast cancer radiotherapy.

The choice of the appropriate model and parameter set in determining the relation between the incidence of radiation pneumonitis and dose distribution in the lung is of great importance, especially in the case of breast radiotherapy where the observed incidence is fairly low. From our previous study based on 150 breast cancer patients, where the fits of dose-volume models to clinical data were estimated (Tsougos et al 2005 Evaluation of dose-response models and parameters predicting radiation induced pneumonitis using clinical data from breast cancer radiotherapy Phys. Med. Biol. 50 3535-54), one could get the impression that the relative seriality is significantly better than the LKB NTCP model. However, the estimation of the different NTCP models was based on their goodness-of-fit on clinical data, using various sets of published parameters from other groups, and this fact may provisionally justify the results. Hence, we sought to investigate further the LKB model, by applying different published parameter sets for the very same group of patients, in order to be able to compare the results. It was shown that, depending on the parameter set applied, the LKB model is able to predict the incidence of radiation pneumonitis with acceptable accuracy, especially when implemented on a sub-group of patients (120) receiving [see text]|EUD higher than 8 Gy. In conclusion, the goodness-of-fit of a certain radiobiological model on a given clinical case is closely related to the selection of the proper scoring criteria and parameter set as well as to the compatibility of the clinical case from which the data were derived.