I Jurkovic

Characterization of a two-dimensional liquid-filled ion chamber detector array used for verification of the treatments in radiotherapy

PURPOSE The purpose of the study is to investigate the characteristics of a two-dimensional (2D) liquid-filled ion chamber detector array, which is used for the verification of radiotherapy treatment plans that use small field sizes of up to 10 × 10 cm. METHODS The device used in this study was Octavius 1000 SRS model (PTW, Freiburg, Germany). Its 2D array of detectors consists of 977 liquid-filled ion chambers arranged over an area of 11 × 11 cm. The size of the detectors is 2.3 × 2.3 × 0.5 mm (volume of 0.003 cm(3)) and their spacing in the inner area of 5.5 × 5.5 cm is 2.5 mm center-to-center, whereas in the outer area it is 5 mm center-to-center. The detector reproducibility, dose linearity, and sensitivity to positional changes of the collimator were tested. Also, the output factors of field sizes ranging from 0.5 × 0.5 to 10 × 10 cm(2) both for open and wedged fields have been measured and compared against those measured by a pin-point ionization chamber, liquid filled microchamber, SRS diode, and EDR2 film. RESULTS Its short-term reproducibility was within 0.2% and its medium and long-term reproducibility was within 0.5% (verified with air ionization chamber absolute dose measurements), which is an excellent result taking into account the daily fluctuation of the linear accelerator and the errors in the device setup reproducibility. The dose linearity and dose rate dependence were measured in the range of 0.5-85 Gy and 0.5-10 Gy min(-1), respectively, and were verified with air ionization chamber absolute dose measurements was within 3%. The measurements of the sensitivity showed that the 2D Array could detect millimetric collimator positional changes. The measured output factors showed an agreement of better than 0.3% with the pinpoint chamber and microliquid filled chamber for the field sizes between 3 × 3 and 10 × 10 cm(2). For field sizes down to 1 × 1 cm(2), the agreement with SRS diode and microliquid filled chamber is better than 2%. The measurements of open and wedge-modulated field profiles were compared to the film and ionization chamber in water measurements. CONCLUSIONS The Octavius Detector 1000 SRS is an accurate, precise, and reliable detector, very useful for the daily performance of the patient specific quality assurance of radiotherapy treatment plans.

SU‐FF‐T‐131: Clinical Use of Linear Array MOSFET for Urethral Dose Verification in Prostate High Dose Rate Brachytherapy

Purpose: To investigate the use of linear array MOSFET as in vivo dosimetry detector to determine the urethral dose for a single and multiple fraction during the prostate HDR treatment. Method and Materials: Commercially available Linear Array MOSFETs with 5 individual MOSFET was inserted into the 18 gage Foley catheter right after the HDR prostate implant. Measurements were performed in 25 patients receiving total of 2400cGy HDR boost in 4 fractions with 600cGy per fraction. The urethra dose was measured right after first fraction for all the patients and also subsequent fraction in 5 patients in terms of reproducibility of urethra dose. The exact location of the MOSFET was determined using radio‐opaque marker and the point dose for each MOSFET was determined using CT‐base treatment planning.Results: A Linear Array MOSFETs was placed in such a way that the first MOSFET being slightly above the bladder neck with the average reading of 75%±18% of the prescribed dose since it is beyond the base of the prostate. The dose was increased to maximum of 128% of the total dose within the prostate gland and decreased to 40% or less of the total dose beyond the apex of the gland. There was an excellent correlation of 2.8% between the MOSFET reading and treatment planningdose calculations. The MOSFET reading comparison between first and second fraction also correlated within 2.3%. Conclusion:MOSFETs are suitable for in vivodosimetry during prostate high dose rate brachyhterpy not only to verify the dose across the urethra but also to verify that the needles are maintained in its exact same position as the first fraction. Any unexpected variation in urethra dose compared to initial treatment plan can be corrected in the subsequent fraction as a result of this dose verification procedure.

Prediction of lung tumor motion extent through artificial neural network (ANN) using tumor size and location data

The aim of this study is to assess the possibility of developing novel predictive models based on data mining algorithms which would provide an automatic tool for the calculation of the extent of lung tumor motion characterized by its known location and size. Data mining is an analytic process designed to explore data in search of regular patterns and relationships between variables. The ultimate goal of data mining is prediction of the future behavior. Artificial neural network (ANN) data-mining algorithm was used to develop an automatic model, which was trained to predict extent of the tumor motion using the data set obtained from the available 4D CT imaging data. The accuracy of the designed neural network was tested by using longer training time, different input values and/or more neurons in its hidden layer. An optimized ANN best fit the training and test datasets with a regression value (R) of 0.97 and mean squared error value of 0.0039 cm2. The maximum error that was recorded for the best network performance was 0.32 cm in the craniocaudal direction. The overall prediction error was largest in this direction for 70% of the studied cases. In this study, the concepts of neural networks were discussed and an ANN algorithm is proposed to be used with clinical lung tumor information for the prediction of the tumor motion extent. The results of optimized ANN are promising and can be a reliable tool for motion pattern calculation. It is an automated tool, which may assist radiation oncologists in defining the tumor margins needed in lung cancer radiation therapy.

Dosimetric and radiobiological evaluation of patient setup accuracy in head-and-neck radiotherapy using daily computed tomography-on-rails-based corrections

Introduction: This study evaluates treatment plans aiming at determining the expected impact of daily patient setup corrections on the delivered dose distribution and plan parameters in head-and-neck radiotherapy. Materials and Methods: In this study, 10 head-and-neck cancer patients are evaluated. For the evaluation of daily changes of the patient internal anatomy, image-guided radiation therapy based on computed tomography (CT)-on-rails was used. The daily-acquired CT-on-rails images were deformedly registered to the CT scan that was used during treatment planning. Two approaches were used during data analysis (“cascade” and “one-to-all”). The dosimetric and radiobiological differences of the dose distributions with and without patient setup correction were calculated. The evaluation is performed using dose–volume histograms; the biologically effective uniform dose ([INSIDE:1]) and the complication-free tumor control probability (P+) were also calculated. The dose–response curves of each target and organ at risk (OAR), as well as the corresponding P+ curves, were calculated. Results: The average difference for the “one-to-all” case is 0.6 ± 1.8 Gy and for the “cascade” case is 0.5 ± 1.8 Gy. The value of P+ was lowest for the cascade case (in 80% of the patients). Discussion: Overall, the lowest PIis observed in the one-to-all cases. Dosimetrically, CT-on-rails data are not worse or better than the planned data. Conclusions: The differences between the evaluated “one-to-all” and “cascade” dose distributions were small. Although the differences of those doses against the “planned” dose distributions were small for the majority of the patients, they were large for given patients at risk and OAR.

SU-E-T-174: Evaluation of the Optimal Intensity Modulated Radiation Therapy Plans Done On the Maximum and Average Intensity Projection CTs

PURPOSE To determine the difference in coverage between plans done on average intensity projection and maximum intensity projection CT data sets for lung patients and to establish correlations between different factors influencing the coverage. METHODS For six lung cancer patients, 10 phases of equal duration through the respiratory cycle, the maximum and average intensity projections (MIP and AIP) from their 4DCT datasets were obtained. MIP and AIP datasets had three GTVs delineated (GTVaip - delineated on AIP, GTVmip - delineated on MIP and GTVfus - delineated on each of the 10 phases and summed up). From the each GTV, planning target volumes (PTV) were then created by adding additional margins. For each of the PTVs an IMRT plan was developed on the AIP dataset. The plans were then copied to the MIP data set and were recalculated. RESULTS The effective depths in AIP cases were significantly smaller than in MIP (p < 0.001). The Pearson correlation coefficient of r = 0.839 indicates strong degree of positive linear relationship between the average percentage difference in effective depths and average PTV coverage on the MIP data set. The V2 0 Gy of involved lung depends on the PTV coverage. The relationship between PTVaip mean CT number difference and PTVaip coverage on MIP data set gives r = 0.830. When the plans are produced on MIP and copied to AIP, r equals -0.756. CONCLUSION The correlation between the AIP and MIP data sets indicates that the selection of the data set for developing the treatment plan affects the final outcome (cases with high average percentage difference in effective depths between AIP and MIP should be calculated on AIP). The percentage of the lung volume receiving higher dose depends on how well PTV is covered, regardless of on which set plan is done.

SU-E-J-79: Internal Tumor Volume Motion and Volume Size Assessment Using 4D CT Lung Data

PURPOSE To assess internal tumor volume change through breathing cycle and associated tumor motion using the 4DCT data. METHODS Respiration induced volume change through breathing cycle and associated motion was analyzed for nine patients that were scanned during the different respiratory phases. The examined datasets were the maximum and average intensity projections (MIP and AIP) and the 10 phases of the respiratory cycle. The internal target volume (ITV) was delineated on each of the phases and the planning target volume (PTV) was then created by adding setup margins to the ITV. Tumor motion through the phases was assessed using the acquired 4DCT dataset, which was then used to determine if the margins used for the ITV creation successfully encompassed the tumor in three dimensions. RESULTS Results showed that GTV motion along the superior inferior axes was the largest in all the cases independent of the tumor location and/or size or the use of abdomen compression. The extent of the tumor motion was found to be connected with the size of the GTV. The smallest GTVs exhibited largest motion vector independent of the tumor location. The motion vector size varied through the phases depending on the tumor size and location and it was smallest for phases 20 and 30. The smaller the volume of the delineated GTV, the greater its volume difference through the different respiratory phases was. The average GTV volume change was largest for the phases 60 and 70. CONCLUSION Even if GTV is delineated using both AIP and MIP datasets, its motion extent will exceed the used margins especially for the very small GTV volumes. When the GTV size is less than 10 cc it is recommended to use fusion of the GTVs through all the phases to create the planning ITV.

SU‐E‐T‐303: Radiobiological Evaluation of the Impact of Lung Density Variation in the Effectiveness of IMRT Treatment Plans

PURPOSE To radiobiologically quantify the differences in the clinical effectiveness of lung cancer IMRT plans, due to lung heterogeneity corrections. METHODS In this study, three patients were selected for each of which seven IMRT plans were generated. The first plan was produced accounting for the heterogeneity correction and it was optimized using the same dose prescription. This plan was also used as a reference for the default number of monitor units per beam. Three plans were produced with the lung densities forced to 0.25, 0.5 and 1.0, respectively while keeping the same number of monitor units per beam. Finally, three additional plans were produced using the same lung densities and reoptimizing the plans to achieve the same target coverage. The uniform dose that causes the same tumor control probability or normal tissue complication rate as the actual dose given to the patient was calculated using the biologically effective uniform dose, BEUD. RESULTS The treatment plans with the forced lung density and with the same number of monitor units as the heterogeneous plans had different target coverage values. The plans with the lung densities of 0.25 and 1.0 had the most and least comparable coverage to the reference plan. From the radiobiological assessment, the percentage differences of the complication-free tumor control rate (P+) were largest for the lung density of 1.0 (up to 18%) and smallest for the lung density of 0.25 (up to 3.5%). CONCLUSION The dose deviation resulting from the lack of the lung heterogeneity corrections can be quantified into a difference in clinical effectiveness using the P+ index. The variability of the average lung density increased the discrepancy between the different plans in terms of complication-free tumor control rates. Radiobiological evaluation of the treatment plans can provide much closer association of the treatment delivered with the clinical outcome.

SU‐E‐T‐610: Evaluation of the Performance of the Segmentation Methods Used in the CMS XiO Step‐And‐Shoot IMRT

PURPOSE The aim of this work is to evaluate the performance of the segmentation methods used in the CMS XiO radiotherapy treatment planning system. METHODS For this study, fifteen patients with different types of cancer (prostate, head and neck, pancreas, esophagus, lung) were selected. For each patient, two treatment plans were produced using the sliding window segmentation and the smart sequencing methods. The plans were optimized based on the same clinical objectives. The analysis was based on the dose volume histograms (DVHs) of the PTV and organs at risk, the isodose distributions, maximum global dose, total number of segments, total number of monitor units (MU) per plan, and the average number of MUs per segment. RESULTS The study showed a statistically significant reduction in the total number of monitor units (mean:25.1%, range:7.0%- 35.5%) when the smart sequencing segmentation method was used (p=1.2E-7). However, this reduction in the total number of monitor units did not reduce the total number of MLC segments per plan. The number of MLC segments per plan varied regardless of the site being treated. The plans that used the smart sequencing segmentation method had higher global maximum doses ranging between 106%-118% of the prescription dose, while the corresponding range for the sliding window method was 104%- 111%. The average number of monitor units per MLC segment was 2.5 for the smart sequencing method (range:2.0-3.1), and 3.7 for the sliding window segmentation method (range:2.6-5.0). CONCLUSION This study shows that while smart sequencing segmentation method significantly reduced the total number of monitor units it did not reduce the total number of segments per plan. The smart sequencing segmentation method also produced a higher global maximum dose in all the cases, but with no additional sparing of the relevant organs at risk.

SU-FF-T-160: Development and Use of a Dose-Volume Histogram Analysis Tool

Purpose: To develop an automated tool providing rapid, consistent analysis of the dose‐volume histograms (DVH) generated by commercial treatment planning systems (TPS). This tool has been used for comparative analysis of competing plans and is currently being used to study and mimic physician decision criteria. Method and Materials:Software was developed to import DVH information stored in RTOG submission format, making it relatively independent of the TPS used to generate the plan. Analysis tools are provided to generate conformity, uniformity and radiobiological quantifiers which describe each treatment plan. These quantifiers are presented separately and as overall plan evaluation values, to include CTI, CN and COIN. Radiobiological quantifiers include NTCP, EUD and EUBED. Results: The software has been used to evaluate competing techniques—(1) conventional, (2) two‐field tangential inverse‐planned IMRT and (3) multiple (3 or more) beam IMRT—of breast irradiation on 20 patients. Plans were adjusted to provide 90% of the prescription dose, 50.4 Gy in 28 fractions, to 90% of the PTV. The superiority of the dose distribution of the IMRT methods was clearly demonstrated as more conformal (CN 0.79 vs. 0.83, p < 0.001) with reduced doses to the lung (mean dose 5.4 Gy vs. 4.6 Gy, p = 0.004) and heart (mean dose 2.3 Gy vs. 1.5 Gy, p = 0.02). The radiobiological advantages of IMRT, although better, were less dramatic (EUD, not significant, NTCP effective volumes significant, but NTCP too small to draw conclusions). Conclusion: : Use of this tool enables easy, consistent interpretation of the DVH and the overall treatment scheme. Choosing between competing plans, developing and adjusting dose limits and weighting used in objective functions, and the ability to describe and mimic the physician decision making process are possible uses of the tool.