Development of a log file analysis tool for proton patient QA, system performance tracking, and delivered dose reconstruction.

PURPOSE/OBJECTIVE(S): To describe a log file-based patient-specific quality assurance (QA) method and develop an in-house tool for system performance tracking and dose reconstruction in pencil-beam scanning proton therapy that can be used for pre-treatment plan review. MATERIALS/METHODS: The software extracts beam-specific information from the treatment delivery log file and automatically compares the monitor units (MU), lateral position, and size of each spot against the intended values in the treatment plan to identify any discrepancies in the beam delivery. The software has been used to analyze 992 patients, 2004 plans, 4865 fields, and more than 32 million proton spots from 2016 to 2021. The composite doses of 10 craniospinal irradiation (CSI) plans were reconstructed based on the delivered spots and compared with the original plans as an offline plan review method. RESULTS: Over the course of 6 years, the proton delivery system has proved stable in delivering patient QA fields with proton energies of 69.4-221.3 MeV and an MU range of 0.003-1.473 MU per spot. The planned mean and standard deviation (SD) of the energy and spot MU were 114.4 ± 26.4 MeV and 0.010 ± 0.009 MU, respectively. The mean and SD of the differences in MU and position between the delivered and planned spots were 9.56 × 10-8 ± 2.0 × 10-4 MU and 0.029/-0.007 ± 0.049/0.044 mm on the X/Y-axis for random differences and 0.005/0.125 ± 0.189/0.175 mm on the X/Y-axis for systematic differences. The mean and SD of the difference between the commissioning and delivered spot sizes were 0.086/0.089 ± 0.131/0.166 mm on the X/Y-axis. CONCLUSION: A tool has been developed to extract crucial information about the performance of the proton delivery and monitor system and provide a dose reconstruction based on delivered spots for quality improvement. Each patient's plan was verified before treatment to ensure accurate and safe delivery within the delivery tolerance of the machine.

Dosimetric evaluation of phantoms including metal objects with high atomic number for use in intensity modulated radiation therapy.

The dosimetric effect of artefacts caused by metal hip prostheses in computed tomography imaging is most commonly encountered in the planning of prostate cancer treatment. In this study, a phantom, containing a metal with high atomic number, was prepared for intensity-modulated radiotherapy (IMRT) treatment plans to be used in quality assurance (QA) procedures. Two sets of image files, one without metal artefact correction (ORG) and another with MAR correction (MAR+), were sent to the treatment planning system. In this study, 12 IMRT treatment plans with different fields and segment numbers were calculated. The normal tissue complication probability (NTCP) values of imaginary organs at risk (OARs), such as the rectum and bladder, were investigated, as was the difference in dose maps for ORG and MAR+ derived by calculating gamma passing rates (GPRs). The MatriXX was used for the gamma evaluation of patient-specific IMRT QA measurements. The gamma evaluation was repeated, based on the measurements using an EBT3 gafchromic film, for the plan showing the lowest GPR. The mean relative difference in NTCP values between the two sets of image files was found to be 2.5, 2.1 and 1.4 for the rectum; and 5.33, 6.80 and 9.82 for the bladder, for the investigated 5-, 7- and 9-field beam arrangements, respectively. The relative differences and the standard deviations in GPRs for the standard and metal-containing phantoms were calculated for the MAR+ and ORG sets. The maximum difference found was 7.69% ± 0.88 for the 9-field beam arrangement calculated without metal artefact correction. In the IMRT QA procedures for prostate patients with hip prostheses, the application of a metal-containing phantom that is both easy and inexpensive to prepare, is considered to be a useful method for examining any dose changes involved in introducing a hip prosthesis. Therefore, it is recommended for use in clinics that do not have MAR correction algorithms.

Comparison of MLC error sensitivity of various commercial devices for VMAT pre-treatment quality assurance.

The purpose of this study was to compare the MLC error sensitivity of various measurement devices for VMAT pre-treatment quality assurance (QA). This study used four QA devices (Scandidos Delta4, PTW 2D-array, iRT systems IQM, and PTW Farmer chamber). Nine retrospective VMAT plans were used and nine MLC error plans were generated for all nine original VMAT plans. The IQM and Farmer chamber were evaluated using the cumulative signal difference between the baseline and error-induced measurements. In addition, to investigate the sensitivity of the Delta4 device and the 2D-array, global gamma analysis (1%/1, 2%/2, and 3%/3 mm), dose difference (1%, 2%, and 3%) were used between the baseline and error-induced measurements. Some deviations of the MLC error sensitivity for the evaluation metrics and MLC error ranges were observed. For the two ionization devices, the sensitivity of the IQM was significantly better than that of the Farmer chamber (P < 0.01) while both devices had good linearly correlation between the cumulative signal difference and the magnitude of MLC errors. The pass rates decreased as the magnitude of the MLC error increased for both Delta4 and 2D-array. However, the small MLC error for small aperture sizes, such as for lung SBRT, could not be detected using the loosest gamma criteria (3%/3 mm). Our results indicate that DD could be more useful than gamma analysis for daily MLC QA, and that a large-area ionization chamber has a greater advantage for detecting systematic MLC error because of the large sensitive volume, while the other devices could not detect this error for some cases with a small range of MLC error.

Comparison of DVH-based plan verification methods for VMAT: ArcCHECK-3DVH system and dynalog-based dose reconstruction.

The purpose of this study was comparing dose-volume histogram (DVH)-based plan verification methods for volumetric modulated arc therapy (VMAT) pretreatment QA. We evaluated two 3D dose reconstruction systems: ArcCHECK-3DVH system (Sun Nuclear corp.) and Varian dynalog-based dose reconstruction (DBDR) system, developed in-house. Fifteen prostate cancer patients (67.6 Gy/26 Fr), four head and neck cancer patient (66 Gy/33 Fr), and four esophagus cancer patients (60 Gy/30 Fr) treated with VMAT were studied. First, ArcCHECK measurement was performed on all plans; simultaneously, the Varian dynalog data sets that contained the actual delivered parameters (leaf positions, gantry angles, and cumulative MUs) were acquired from the Linac control system. Thereafter, the delivered 3D patient dose was reconstructed by 3DVH software (two different calculating modes were used: High Sensitivity (3DVH-HS) and Normal Sensitivity (3DVH-NS)) and in-house DBDR system. We evaluated the differences between the TPS-calculated dose and the reconstructed dose using 3D gamma passing rates and DVH dose index analysis. The average 3D gamma passing rates (3%/3 mm) between the TPS-calculated dose and the reconstructed dose were 99.1 ± 0.6%, 99.7 ± 0.3%, and 100.0 ± 0.1% for 3DVH-HS, 3DVH-NS, and DBDR, respectively. For the prostate cases, the average differences between the TPS-calculated dose and reconstructed dose in the PTV mean dose were 1.52 ± 0.50%, -0.14 ± 0.55%, and -0.03 ± 0.07% for 3DVH-HS, 3DVH-NS, and DBDR, respectively. For the head and neck and esophagus cases, the dose difference to the TPS-calculated dose caused by an effect of heterogeneity was more apparent under the 3DVH dose reconstruction than the DBDR. Although with some residual dose reconstruction errors, these dose reconstruction methods can be clinically used as effective tools for DVH-based QA for VMAT delivery.

Evaluation of a novel patient-specific quality assurance phantom for robotic single-isocentre, multiple-target stereotactic radiosurgery, and stereotactic radiotherapy.

OBJECTIVES: To evaluate patient-specific quality assurance (PSQA) of 3 targets in a single delivery using a novel film-based phantom. METHODS: The phantom was designed to rotate freely as a sphere and could measure 3 targets with film in a single delivery. After identifying the coordinates of 3 targets in the skull, the rotation angles about the equator and meridian were computed for optimal phantom setup, ensuring the film plane intersected the 3 targets. The plans were delivered on the CyberKnife system using fiducial tracking. The irradiated films were scanned and processed. All films were analysed using 3 gamma criteria. RESULTS: Fifteen CyberKnife test plans with 3 different modalities were delivered on the phantom. Both automatic and marker-based registration methods were applied when registering the irradiated film and dose plane. Gamma analysis was performed using a 3%/1 mm, 2%/1 mm, and 1%/1 mm criteria with a 10% threshold. For the automatic registration method, the passing rates were 98.2% ± 1.9%, 94.2% ± 3.7%, and 80.9% ± 6.3%, respectively. For the marker-based registration approach, the passing rates were 96.4% ± 2.7%, 91.7% ± 4.3%, and 78.4% ± 6.2%, respectively. CONCLUSIONS: A novel spherical phantom was evaluated for the CyberKnife system and achieved acceptable PSQA passing rates using TG218 recommendations. The phantom can measure true-composite dose and offers high-resolution results for PSQA, making it a valuable device for robotic radiosurgery. ADVANCES IN KNOWLEDGE: This is the first study on PSQA of 3 targets concurrently on the CyberKnife system.

Assessment of the deep learning-based gamma passing rate prediction system for 1.5 T magnetic resonance-guided linear accelerator.

Measurement-based verification is impossible for the patient-specific quality assurance (QA) of online adaptive magnetic resonance imaging-guided radiotherapy (oMRgRT) because the patient remains on the couch throughout the session. We assessed a deep learning (DL) system for oMRgRT to predict the gamma passing rate (GPR). This study collected 125 verification plans [reference plan (RP), 100; adapted plan (AP), 25] from patients with prostate cancer treated using Elekta Unity. Based on our previous study, we employed a convolutional neural network that predicted the GPRs of nine pairs of gamma criteria from 1%/1 mm to 3%/3 mm. First, we trained and tested the DL model using RPs (n = 75 and n = 25 for training and testing, respectively) for its optimization. Second, we tested the GPR prediction accuracy using APs to determine whether the DL model could be applied to APs. The mean absolute error (MAE) and correlation coefficient (r) of the RPs were 1.22 ± 0.27% and 0.29 ± 0.10 in 3%/2 mm, 1.35 ± 0.16% and 0.37 ± 0.15 in 2%/2 mm, and 3.62 ± 0.55% and 0.32 ± 0.14 in 1%/1 mm, respectively. The MAE and r of the APs were 1.13 ± 0.33% and 0.35 ± 0.22 in 3%/2 mm, 1.68 ± 0.47% and 0.30 ± 0.11 in 2%/2 mm, and 5.08 ± 0.29% and 0.15 ± 0.10 in 1%/1 mm, respectively. The time cost was within 3 s for the prediction. The results suggest the DL-based model has the potential for rapid GPR prediction in Elekta Unity.

The effect of measurement geometry on patient specific QA pass/fail rates for stereotactic body radiation therapy (SBRT) Plans.

Patient quality assurance (QA) is a required part of the treatment care path, and plan failure can lead to increased personnel hours or delay of treatment. The recommendation by the American Association of Physicists in Medicine is that gamma analysis be used to evaluate measured volumetric modulated arc therapy plans. Vendors have developed many different measurement geometries for patient QA devices which could yield varying pass rates when used with the recommended tolerances, normalization, and criterion. For this study, clinically treated stereotactic body radiation therapy plans were used to evaluate differences in gamma dose tolerances and sampled dose distribution complexity for centralized or peripheral measurement geometries on a cylindrical phantom. Random errors were then introduced into a subset of these plans, and the differences in pass rates between the geometries were correlated with differences in the observed mathematical differences. Finally, a single clinically relevant target coverage deviation was introduced to another subset of plans to evaluate whether a particular geometry is measurably better at identifying clinically relevant errors. It was found that centralized geometries resulted in more lenient dose tolerances and less complex sampled dose distributions compared to peripheral geometries. Pass rates were uniformly lower in the peripheral measurement geometry, and the difference in pass rates between the geometries correlated strongly with the difference in dose tolerance and weakly with the difference in the chosen complexity metrics. However, neither of the geometries were sufficiently sensitive enough to detect clinically relevant changes to target coverage when using recommended tolerances and criteria, and no statistically significant difference was found between their pass rates. Given these findings, the authors concluded that stereotactic body radiation therapy plans could fail patient QA when measured in the peripheral geometry but pass in the centralized geometry, with possibly neither having correlation to true clinical deviation.

Deep learning-enabled EPID-based 3D dosimetry for dose verification of step-and-shoot radiotherapy.

PURPOSE: The study aims at a novel dosimetry methodology to reconstruct a 3D dose distribution as imparted to a virtual cylindrical phantom using an electronic portal imaging device (EPID). METHODS: A deep learning-based signal processing strategy, referred to as 3DosiNet, is utilized to learn a mapping from an EPID image to planar dose distributions at given depths. The network was trained with the volumetric dose exported from the clinical treatment planning system (TPS). Given the latent inconsistency between measurements and corresponding TPS calculations, unsupervised learning is formulated in 3DosiNet to capture abstractive image features that are less sensitive to the potential variations. RESULTS: Validation experiments were performed using five regular fields and three clinical intensity-modulated radiation therapy (IMRT) cases. The measured dose profiles and percentage depth dose (PDD) curves were compared with those measured using standard tools in terms of the 1D gamma index. The mean gamma pass rates (2%/2 mm) over the regular fields are 100% and 97.3% for the dose profile and PDD measurements, respectively. The measured volumetric dose was compared to the corresponding TPS calculation in terms of the 3D gamma index. The mean 2%/2 mm gamma pass rates are 97.9% for square fields and 94.9% for the IMRT fields. CONCLUSIONS: The system promises to be a practical 3D dosimetric tool for pre-treatment patient-specific quality assurance and further developed for in-treatment patient dose monitoring.

A noise correction of the γ-index method for Monte Carlo dose distribution comparison.

PURPOSE: Due to the increasing complexity of IMRT/IMPT treatments, quality assurance (QA) is essential to verify the quality of the dose distribution actually delivered. In this context, Monte Carlo (MC) simulations are more and more often used to verify the accuracy of the treatment planning system (TPS). The most common method of dose comparison is the γ-test, which combines dose difference and distance-to-agreement (DTA) criteria. However, this method is known to be dependent on the noise level in dose distributions. We propose here a method to correct the bias of the γ passing rate (GPR) induced by MC noise. METHODS: The GPR amplitude was studied as a function of the MC noise level. A model of this noise effect was mathematically derived. This model was then used to predict the time-consuming low-noise GPR by fitting multiple fast MC dose calculations. MC dose maps with a noise level between 2% and 20% were computed, and the GPR was predicted at a noise level of 0.3%. Due to the asymmetry of the γ-test, two different cases were considered: the MC dose was first set as reference dose, then as evaluated dose in the γ-test. Our method was applied on six proton therapy plans including analytical doses from the TPS or patient-specific QA measurements. RESULTS: An average absolute error of 4.31% was observed on the GPR computed for MC doses with 2% statistical noise. Our method was able to improve the accuracy of the gamma passing rate by up to 13%. The method was found especially efficient to correct the noise bias when the DTA criterion is low. CONCLUSIONS: We propose a method to enhance the γ-evaluation of a treatment plan when there is noise in one of the compared distributions. The method allows, in a tractable time, to detect the cases for which a correction is necessary and can improve the accuracy of the resulting passing rates.

Derivative-based gamma index: a novel methodology for stringent patient-specific quality assurance in the stereotactic treatment planning of liver cancer.

Objective:The development of a stringent derivative-based gamma (DBG) index for patient-specific QA in stereotactic radiotherapy treatment planning (SRTP) to account for the spatial change in dose.Methods:Twenty-five patients of liver SBRT were selected retrospectively for this study. Deliberately, two different kinds of treatment planning approaches were used for each patient. Firstly, the treatment plans were generated using a conventional treatment planning (CTP) approach in which the target was covered with a homogeneous dose along with the nominal dose fall-off around the treatment field. Subsequently, the other treatment plans were generated using an SRTP approach with the intent of heterogeneous dose within the target region along with a steeper dose gradient outside the treatment field as much as possible. For both kinds of treatment plans, two dimensional (2D) conventional gamma (CG) and DBG analysis were performed using the 2D ion chamber array and radiochromic film.Results:Difference in the DBG index was statistically significant whereas, for CG analysis, the difference in CG index was insignificant for both types of treatment plans (CTP and SRTP). A significant positive correlation was observed between the difference in the DBG index and the difference in HI for high gamma criteria.Conclusion:The DBG evaluation is found to be more rigorous, and sensitive to the only SRTP. The proposed method could be opted-in the routine clinical practice in addition to CG.Advances in knowledge:DBG is more sensitive to detect the spatial change of dose, especially in high dose gradient regions.

Platform for automatic patient quality assurance via Monte Carlo simulations in proton therapy.

For radiation therapy, it is crucial to ensure that the delivered dose matches the planned dose. Errors in the dose calculations done in the treatment planning system (TPS), treatment delivery errors, other software bugs or data corruption during transfer might lead to significant differences between predicted and delivered doses. As such, patient specific quality assurance (QA) of dose distributions, through experimental validation of individual fields, is necessary. These measurement based approaches, however, are performed with 2D detectors, with limited resolution and in a water phantom. Moreover, they are work intensive and often impose a bottleneck to treatment efficiency. In this work, we investigated the potential to replace measurement-based approach with a simulation-based patient specific QA using a Monte Carlo (MC) code as independent dose calculation engine in combination with treatment log files. Our developed QA platform is composed of a web interface, servers and computation scripts, and is capable to autonomously launch simulations, identify and report dosimetric inconsistencies. To validate the beam model of independent MC engine, in-water simulations of mono-energetic layers and 30 SOBP-type dose distributions were performed. Average Gamma passing ratio 99 ± 0.5% for criteria 2%/2 mm was observed. To demonstrate feasibility of the proposed approach, 10 clinical cases such as head and neck, intracranial indications and craniospinal axis, were retrospectively evaluated via the QA platform. The results obtained via QA platform were compared to QA results obtained by measurement-based approach. This comparison demonstrated consistency between the methods, while the proposed approach significantly reduced in-room time required for QA procedures.

Multi-institution validation of a new high spatial resolution diode array for SRS and SBRT plan pretreatment quality assurance.

PURPOSE: The SRS MapCHECK® , a recently developed patient-specific quality assurance (PSQA) tool for end-to-end testing of stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT), was evaluated in a multi-institution study and compared with radiochromic film. METHODS: The SRS MapCHECK was used to collect data on 84 SBRT or SRS PSQA plans/fields at nine institutions on treatment delivery devices (TDD) manufactured by Varian and Elekta. PSQA plans from five different treatment planning software (TPS) were selected and executed on TDDs operating at beam energies of 6 and 10 MV with and without a flattening filter. The patient plans were all VMAT except for ten conformal arc therapy fields. The plans were selected to encompass a range of size and tumor sites including brain, lung, spine, abdomen, ear, pancreas, and liver. Corresponding radiochromic film data was acquired in 50 plans/fields. Results were evaluated using gamma analysis with absolute dose criterion of 3% global dose-difference (DD) and 1 mm distance-to-agreement (DTA). RESULTS: The mean 3% DD/1 mm DTA Gamma pass rate of SRS MapCHECK in comparison to film was 95.9%, whereas comparison of SRS MapCHECK to the treatment planning software was 94.7%. 80% of SRS MapCHECK comparisons against film exceed 95% pass rate, and about 30% of SRS MapCHECK comparisons against film exceed 99% pass rate. To maintain good agreement between SRS MapCHECK and film or TPS, authors recommend avoiding plans with a modified modulation complexity score (MMCS) <0.1 arbitrary units (a.u.). In the examples presented, this coincides with avoiding plans with a mu/dose limit of >3 µ/cGy. CONCLUSIONS: Stereotactic radiosurgery MapCHECK has been validated for PSQA for a variety of clinical SRS/SBRT plans in a wide range of treatment delivery conditions. The SRS MapCHECK comparison with film demonstrates near-equivalence for analysis of patient-specific QA deliveries comprised of small field measurements.

Multichannel Film Dosimetry for Quality Assurance of Intensity Modulated Radiotherapy Treatment Plans Under 0.35 T Magnetic Field.

Purpose To evaluate the intensity modulated radiotherapy (IMRT) quality assurance (QA) results of the multichannel film dosimetry analysis with single scan method by using Gafchromic™ EBT3 (Ashland Inc., Covington, KY, USA) film under 0.35 T magnetic field. Methods Between September 2018 and June 2019, 70 patients were treated with ViewRay MRIdian® (ViewRay Inc., Mountain View, CA) linear accelerator (Linac). Film dosimetry QA plans were generated for all IMRT treatments. Multichannel film dosimetry for red, green and blue (RGB) channels were compared with treatment planning system (TPS) dose maps by gamma evaluation analysis. Results The mean gamma passing rates of RGB channels are 97.3% ± 2.26%, 96.0% ± 3.27% and 96.2% ± 3.14% for gamma evaluation with 2% DD/2 mm distance to agreement (DTA), respectively. Moreover, the mean gamma passing rates of RGB channels are 99.7% ± 0.41%, 99.6% ± 0.59% and 99.5% ± 0.67% for gamma evaluation with 3% DD/3 mm DTA, respectively. Conclusion The patient specific QA using Gafchromic™ EBT3 film with multichannel film dosimetry seems to be a suitable tool to implement for MR-guided IMRT treatments under 0.35 T magnetic field. Multichannel film dosimetry with Gafchromic™ EBT3 is a consistent QA tool for gamma evaluation of the treatment plans even with 2% DD/2 mm DTA under 0.35 T magnetic field presence.

Total-body irradiation using linac-based volumetric modulated arc therapy: Its clinical accuracy, feasibility and reliability.

PURPOSE: To report the feasibility, accuracy, and reliability of volumetric modulated arc therapy (VMAT)-based total-body irradiation (TBI) treatment in patients with acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL). MATERIALS AND METHODS: From 2015 to 2018, 30 patients with AML or ALL were planned and treated with VMAT-based TBI, which consisted of three isocenters and three overlapping arcs. TBI dose was prescribed to 90% of the planning treatment volume (PTV) receiving 12 Gy in six fractions, at two fractions per day. Mean lung and kidney doses were restricted less than 10 Gy, and maximum lens dose less than 6 Gy. Quality assurance (QA) comprised the verification of the irradiation plans via dose-volume histogram (DVH) based 3D patient QA system. RESULTS: Average mean lung dose was 9.7 ±â€¯0.2 Gy, mean kidney dose 9.6 ±â€¯0.2 Gy, maximum lens dose 4.5 ±â€¯0.4 Gy, mean PTV dose 12.7 ±â€¯0.1 Gy, and heterogeneity index of PTV was 1.16 ±â€¯0.02 in all patients. Grade 3 or more acute radiation toxicity was not observed. When comparing plan and DVH-based 3D patient QA results, average differences of 3.3% ±â€¯1.3 in mean kidney doses, 1.1% ±â€¯0.7 in mean lung doses, and 0.9% ±â€¯0.4 in mean target doses were observed. CONCLUSION: Linac-based VMAT increased the dose homogeneity of TBI treatment more than extended SSD techniques. Partial cone-beam CT and optical surface-guided system assure patient positioning. DVH-based 3D patient dose verification QA was possible with linac-based VMAT showing small differences between planned and delivered doses. It is feasible, accurate, and reliable.

A deep learning-based prediction model for gamma evaluation in patient-specific quality assurance.

PURPOSE: Patient-specific quality assurance (QA) measurement is conducted to confirm the accuracy of dose delivery. However, measurement is time-consuming and places a heavy workload on the medical physicists and radiological technologists. In this study, we proposed a prediction model for gamma evaluation, based on deep learning. We applied the model to a QA measurement dataset of prostate cancer cases to evaluate its practicality. METHODS: Sixty pretreatment verification plans from prostate cancer patients treated using intensity modulated radiation therapy were collected. Fifteen-layer convolutional neural networks (CNN) were developed to learn the sagittal planar dose distributions from a RT-3000 QA phantom (R-TECH.INC., Tokyo, Japan). The percentage gamma passing rate (GPR) was measured using GAFCHROMIC EBT3 film (Ashland Specialty Ingredients, Covington, USA). The input training data also included the volume of the PTV (planning target volume), rectum, and overlapping region, measured in cm3 , and the monitor unit values for each field. The network produced predicted GPR values at four criteria: 2%(global)/2 mm, 3%(global)/2 mm, 2%(global)/3 mm, and 3%(global)/3 mm. Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, was used for learning and for optimizing the CNN-based model. Fivefold cross-validation was applied to validate the performance of the proposed method. Forty cases were used for training and validation set in fivefold cross-validation, and the remaining 20 cases were used for the test set. The predicted and measured GPR values were compared. RESULTS: A linear relationship was found between the measured and predicted values, for each of the four criteria. Spearman rank correlation coefficients in validation set between measured and predicted GPR values at four criteria were 0.73 at 2%/2 mm, 0.72 at 3%/2 mm, 0.74 at 2%/3 mm, and 0.65 at 3%/3 mm, respectively (P < 0.01). The Spearman rank correlation coefficients in the test set were 0.62 (P < 0.01) at 2%/2 mm, 0.56 (P < 0.01) at 3%/2 mm, 0.51 (P = 0.02) at 2%/3 mm, and 0.32 (P = 0.16) at 3%/3 mm. These results demonstrated a strong or moderate correlation between the predicted and measured values. CONCLUSIONS: We developed a CNN-based prediction model for patient-specific QA of dose distribution in prostate treatment. Our results suggest that deep learning may provide a useful prediction model for gamma evaluation of patient-specific QA in prostate treatment planning.

Quantification of residual dose estimation error on log file-based patient dose calculation.

PURPOSE: The log file-based patient dose estimation includes a residual dose estimation error caused by leaf miscalibration, which cannot be reflected on the estimated dose. The purpose of this study is to determine this residual dose estimation error. METHODS AND MATERIALS: Modified log files for seven head-and-neck and prostate volumetric modulated arc therapy (VMAT) plans simulating leaf miscalibration were generated by shifting both leaf banks (systematic leaf gap errors: ±2.0, ±1.0, and ±0.5mm in opposite directions and systematic leaf shifts: ±1.0mm in the same direction) using MATLAB-based (MathWorks, Natick, MA) in-house software. The generated modified and non-modified log files were imported back into the treatment planning system and recalculated. Subsequently, the generalized equivalent uniform dose (gEUD) was quantified for the definition of the planning target volume (PTV) and organs at risks. RESULTS: For MLC leaves calibrated within ±0.5mm, the quantified residual dose estimation errors that obtained from the slope of the linear regression of gEUD changes between non- and modified log file doses per leaf gap are in head-and-neck plans 1.32±0.27% and 0.82±0.17Gy for PTV and spinal cord, respectively, and in prostate plans 1.22±0.36%, 0.95±0.14Gy, and 0.45±0.08Gy for PTV, rectum, and bladder, respectively. CONCLUSIONS: In this work, we determine the residual dose estimation errors for VMAT delivery using the log file-based patient dose calculation according to the MLC calibration accuracy.

Evaluation of PTW Seven29 for tomotherapy patient-specific quality assurance and comparison with ScandiDos Delta(4).

For routine quality assurance of helical tomotherapy plans, an alternative method, as opposed to the TomoTherapy suggested cylindrical solid water phantom with film and ionization chamber, is proposed using the PTW Seven29 2D-ARRAY inserted in a dedicated octagonal phantom, called Octavius. First, the sensitivity of the array to pitch was studied by varying the pitch during planning to 0.287, 0.433, 1.0, and 2.0. For each pitch selected, the dependence on field size was investigated by generating plans with field widths (FWs) of 1.06 cm, 2.49 cm, and 5.02 cm, for a total of 12 plans. Secondly, a total of 15 patient QA plans were delivered using helical tomotherapy with the Delta(4) and Seven29/Octavius for comparison. Using the clinical gamma criteria, 3% and 3 mm, all FW and pitch plans had a passing percentage of >90%. For patient QA plans, the average gamma pass percentage was 97.0% (94.4-99.8%) for the Delta(4) and 97.6% (92.5-100.0%) for the Seven29/Octavius. Both the Seven29/Octavius and Delta(4) performed to a high standard of measurement accuracy and had a 90% or greater gamma percent for all plans and were considered clinically acceptable.