Prediction of patient-specific quality assurance for volumetric modulated arc therapy using radiomics-based machine learning with dose distribution.

PURPOSE: We sought to develop machine learning models to predict the results of patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose-evaluation metrics-including the gamma passing rates (GPRs)-and criteria based on the radiomic features of 3D dose distribution in a phantom. METHODS: A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose-evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance-to-agreement in 1-mm and 2-mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient-specific QA. The machine learning regression models for predicting the values of the dose-evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross-validation in which four-fold cross-validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient. RESULTS: The RMSE and MAE for the developed machine learning models ranged from <1% to nearly <10% depending on the dose-evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom. CONCLUSIONS: The developed machine learning models showed high performance for predicting dose-evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics.

Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy.

PURPOSE: The purpose of this study was to create and evaluate deep learning-based models to detect and classify errors of multi-leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor (TF) and the dosimetric leaf gap (DLG). METHODS: A total of 33 clinical VMAT plans for prostate and head-and-neck cancer were used, assuming a cylindrical and homogeneous phantom, and error plans were created by altering the original value of the TF and the DLG by ± 10, 20, and 30% in the treatment planning system (TPS). The Gaussian filters of σ = 0.5 $\sigma = 0.5$ and 1.0 were applied to the planar dose maps of the error-free plan to mimic the measurement dose map, and thus dose difference maps between the error-free and error plans were obtained. We evaluated 3 deep learning-based models, created to perform the following detections/classifications: (1) error-free versus TF error, (2) error-free versus DLG error, and (3) TF versus DLG error. Models to classify the sign of the errors were also created and evaluated. A gamma analysis was performed for comparison. RESULTS: The detection and classification of TF and DLG error were feasible for σ = 0.5 $\sigma = 0.5$ ; however, a considerable reduction of accuracy was observed for σ = 1.0 $\sigma = 1.0$ depending on the magnitude of error and treatment site. The sign of errors was detectable by the specifically trained models for σ = 0.5 $\sigma = 0.5$ and 1.0. The gamma analysis could not detect errors. CONCLUSIONS: We demonstrated that the deep learning-based models could feasibly detect and classify TF and DLG errors in VMAT dose distributions, depending on the magnitude of the error, treatment site, and the degree of mimicked measurement doses.

Mathematical model combined with microdosimetric kinetic model for tumor volume calculation in stereotactic body radiation therapy.

We proposed a new mathematical model that combines an ordinary differential equation (ODE) and microdosimetric kinetic model (MKM) to predict the tumor-cell lethal effect of Stereotactic body radiation therapy (SBRT) applied to non-small cell lung cancer (NSCLC). The tumor growth volume was calculated by the ODE in the multi-component mathematical model (MCM) for the cell lines NSCLC A549 and NCI-H460 (H460). The prescription doses 48 Gy/4 fr and 54 Gy/3 fr were used in the SBRT, and the effect of the SBRT on tumor cells was evaluated by the MKM. We also evaluated the effects of (1) linear quadratic model (LQM) and the MKM, (2) varying the ratio of active and quiescent tumors for the total tumor volume, and (3) the length of the dose-delivery time per fractionated dose (tinter) on the initial tumor volume. We used the ratio of the tumor volume at 1 day after the end of irradiation to the tumor volume before irradiation to define the radiation effectiveness value (REV). The combination of MKM and MCM significantly reduced REV at 48 Gy/4 fr compared to the combination of LQM and MCM. The ratio of active tumors and the prolonging of tinter affected the decrease in the REV for A549 and H460 cells. We evaluated the tumor volume considering a large fractionated dose and the dose-delivery time by combining the MKM with a mathematical model of tumor growth using an ODE in lung SBRT for NSCLC A549 and H460 cells.

Multicomponent mathematical model for tumor volume calculation with setup error using single-isocenter stereotactic radiotherapy for multiple brain metastases.

We evaluated the tumor residual volumes considering six degrees-of-freedom (6DoF) patient setup errors in stereotactic radiotherapy (SRT) with multicomponent mathematical model using single-isocenter irradiation for brain metastases. Simulated spherical gross tumor volumes (GTVs) with 1.0 (GTV 1), 2.0 (GTV 2), and 3.0 (GTV 3)-cm diameters were used. The distance between the GTV center and isocenter (d) was set at 0-10 cm. The GTV was simultaneously translated within 0-1.0 mm (T) and rotated within 0°-1.0° (R) in the three axis directions using affine transformation. We optimized the tumor growth model parameters using measurements of non-small cell lung cancer cell lines' (A549 and NCI-H460) growth. We calculated the GTV residual volume at the irradiation's end using the physical dose to the GTV when the GTV size, d, and 6DoF setup error varied. The d-values that satisfy tolerance values (10%, 35%, and 50%) of the GTV residual volume rate based on the pre-irradiation GTV volume were determined. The larger the tolerance value set for both cell lines, the longer the distance to satisfy the tolerance value. In GTV residual volume evaluations based on the multicomponent mathematical model on SRT with single-isocenter irradiation, the smaller the GTV size and the larger the distance and 6DoF setup error, the shorter the distance that satisfies the tolerance value might need to be.

Relationship between absorbed dose and changes in liver volume after chemoradiotherapy for esophageal cancer.

PURPOSE: The liver is the largest organ in the abdomen and is often irradiated in radiotherapy for non-hepatic malignancies. As most of the studies on changes in liver volume are on hepatocellular carcinoma based on liver dysfunction, there are few studies on healthy liver. In this study, we investigated the relationship between absorbed dose and changes in liver volume after chemoradiotherapy for esophageal cancer in patients without apparent pre-treatment liver dysfunction. MATERIALS AND METHODS: Liver volume was compared between pre-treatment, acute (< 4 months) and late post-treatment (≥ 4 and < 13 months) phases in 12 patients using abdominal plain CT images. Volume changes were evaluated separately for the right and left lobes. We investigated the relationship between the volume change and VxGy (percentage of volume received x Gy or more dose). In addition, volume change for each absorbed dose was investigated using deformable image registration. RESULTS: The volume of the left lobe showed a significant decrease between pre-treatment and acute post-treatment phases (p < 0.001), while the volume of right lobe and between acute and late post-treatment phase of left lobe did not. The mean value of the volume reduction rate of the left lobe was 51.1% and equivalent to the mean value of V30Gy. As a result of the volume change for each absorbed dose, the volume reduction rate increased as the absorbed dose increased, and a significant volume loss was observed at doses above 11 Gy. CONCLUSION: Volume of the liver significantly decreased only in the acute phase after chemoradiotherapy for esophageal cancer. The tolerable dose for a healthy liver is generally considered to be 30 Gy, but attention should be paid to lower doses to avoid radiation-induced liver injury.

The impact of target positioning error and tumor size on radiobiological parameters in robotic stereotactic radiosurgery for metastatic brain tumors.

This study aimed to evaluate the effect of target positioning error (TPE) on radiobiological parameters, such as tumor control probability (TCP) and normal tissue complication probability (NTCP), in stereotactic radiosurgery (SRS) for metastatic brain tumors of different sizes using CyberKnife. The reference SRS plans were created using the circular cone of the CyberKnife for each spherical gross tumor volume (GTV) with diameters (φ) of 5, 7.5, 10, 15, and 20 mm, contoured on computed tomography images of the head phantom. Subsequently, plans involving TPE were created by shifting the beam center by 0.1-2.0 mm in three dimensions relative to the reference plans using the same beam arrangements. Conformity index (CI), generalized equivalent uniform dose (gEUD)-based TCP, and NTCP of estimated brain necrosis were evaluated for each plan. When the gEUD parameter "a" was set to - 10, the CI and TCP for the reference plan at the φ5-mm GTV were 0.90 and 80.8%, respectively. The corresponding values for plans involving TPE of 0.5-mm, 1.0-mm, and 2.0-mm were 0.62 and 77.4%, 0.40 and 62.9%, and 0.12 and 7.2%, respectively. In contrast, the NTCP for all GTVs were the same. The TCP for the plans involving a TPE of 2-mm was 7.2% and 68.8% at the φ5-mm and φ20-mm GTV, respectively. The TPEs corresponding to a TCP reduction rate of 3% at the φ5-mm and φ20-mm GTV were 0.41 and 0.99 mm, respectively. TPE had a significant effect on TCP in SRS for metastatic brain tumors using CyberKnife, particularly for small GTVs.

Radiobiological evaluation considering the treatment time with stereotactic radiosurgery for brain metastases.

Objective: We evaluated the radiobiological effect of the irradiation time with the interruption time of stereotactic radiosurgery (SRS) using CyberKnife® (CK) systemfor brain metastases. Methods: We used the DICOM data and irradiation log file of the 10 patients with brain metastases from non-small-cell lung cancer (NSCLC) who underwent brain SRS. We defined the treatment time as the sum of the dose-delivery time and the interruption time during irradiations, and we used a microdosimetric kinetic model (MKM) to evaluate the radiobiological effects of the treatment time. The biological parameters, i.e. α0, ß0, and the DNA repair constant rate (a + c), were acquired from NCI-H460 cell for the MKM. We calculated the radiobiological dose for the gross tumor volume (GTVbio) to evaluate the treatment time's effect compared with no treatment time as a reference. The D95 (%) and the Radiation Therapy Oncology Group conformity index (RCI) and Paddick conformity index (PCI) were calculated as dosimetric indices. We used several DNA repair constant rates (a + c) (0.46, 1.0, and 2.0) to assess the radiobiological effect by varying the DNA repair date (a + c) values. Results: The mean values of D95 (%), RCI, and PCI for GTVbio were 98.8%, 0.90, and 0.80, respectively, and decreased with increasing treatment time. The mean values of D95 (%), RCI, and PCI of GTVbio at 2.0 (a+c) value were 94.9%, 0.71, and 0.49, respectively. Conclusion: The radiobiological effect of the treatment time on tumors was accurately evaluated with brain SRS using CK. Advances in knowledge: There has been no published investigation of the radiobiological impact of the longer treatment time with multiple interruptions of SRS using a CK on the target dose distribution in a comparison with the use of a linac. Radiobiological dose assessment that takes into account treatment time in the physical dose in this study may allow more accurate dose assessment in SRS for metastatic brain tumors using CK.

Calculated relative biological effectiveness (RBE) for initial DNA double-strand breaks (DSB) from flattening filter and flattening filter-free 6 MV X-ray fields.

OBJECTIVES: We evaluated the radiobiological effectiveness based on the yields of DNA double-strand breaks (DSBs) of field induction with flattening filter (FF) and FF-free (FFF) photon beams. METHODS: We used the particle and heavy ion transport system (PHITS) and a water equivalent phantom (30 × 30 × 30 cm3) to calculate the physical qualities of the dose-mean lineal energy (yD) with 6 MV FF and FFF. The relative biological effectiveness based on the yields of DNA-DSBs (RBEDSB) was calculated for standard radiation such as 220 kVp X-rays by using the estimating yields of SSBs and DSBs. The measurement points used to calculate the in-field yD and RBEDSB were located at a depth of 3, 5, and 10 cm in the water equivalent phantom on the central axis. Measurement points at 6, 8, and 10 cm in the lateral direction of each of the three depths from the central axis were set to calculate the out-of-field yD and RBEDSB. RESULTS: The RBEDSB of FFF in-field was 1.7% higher than FF at each measurement depth. The RBEDSB of FFF out-of-field was 1.9 to 6.4% higher than FF at each depth measurement point. As the distance to out-of-field increased, the RBEDSB of FFF rose higher than those of FF. FFF has a larger RBEDSB than FF based on the yields of DNA-DSBs as the distance to out-of-field increased. CONCLUSIONS: The out-of-field radiobiological effect of FFF could thus be greater than that of FF since the spreading of the radiation dose out-of-field with FFF could be a concern compared to the FF. ADVANCES IN KNOWLEDGE: The RBEDSB of FFF of out-of-field might be larger than FF.

Radiobiological evaluation considering setup error on single-isocenter irradiation in stereotactic radiosurgery.

PURPOSE: We calculated the dosimetric indices and estimated the tumor control probability (TCP) considering six degree-of-freedom (6DoF) patient setup errors in stereotactic radiosurgery (SRS) using a single-isocenter technique. METHODS: We used simulated spherical gross tumor volumes (GTVs) with diameters of 1.0 cm (GTV 1), 2.0 cm (GTV 2), and 3.0 cm (GTV 3), and the distance (d) between the target center and isocenter was set to 0, 5, and 10 cm. We created the dose distribution by convolving the blur component to uniform dose distribution. The prescription dose was 20 Gy and the dose distribution was adjusted so that D95 (%) of each GTV was covered by 100% of the prescribed dose. The GTV was simultaneously rotated within 0°-1.0° (δR) around the x-, y-, and z-axes and then translated within 0-1.0 mm (δT) in the x-, y-, and z-axis directions. D95, conformity index (CI), and conformation number (CN) were evaluated by varying the distance from the isocenter. The TCP was estimated by translating the calculated dose distribution into a biological response. In addition, we derived the x-y-z coordinates with the smallest TCP reduction rate that minimize the sum of squares of the residuals as the optimal isocenter coordinates using the relationship between 6DoF setup error, distance from isocenter, and GTV size. RESULTS: D95, CI, and CN were decreased with increasing isocenter distance, decreasing GTV size, and increasing setup error. TCP of GTVs without 6DoF setup error was estimated to be 77.0%. TCP were 25.8% (GTV 1), 35.0% (GTV 2), and 53.0% (GTV 3) with (d, δT, δR) = (10 cm, 1.0 mm, 1.0°). The TCP was 52.3% (GTV 1), 54.9% (GTV 2), and 66.1% (GTV 3) with (d, δT, δR) = (10 cm, 1.0 mm, 1.0°) at the optimal isocenter position. CONCLUSION: The TCP in SRS for multiple brain metastases with a single-isocenter technique may decrease with increasing isocenter distance and decreasing GTV size when the 6DoF setup errors are exceeded (1.0 mm, 1.0°). Additionally, it might be possible to better maintain TCP for GTVs with 6DoF setup errors by using the optimal isocenter position.

A comparative study of high-dose-rate brachytherapy boost combined with external beam radiation therapy versus external beam radiation therapy alone for high-risk prostate cancer.

We aimed to compare the outcomes of high-dose-rate brachytherapy (HDR-BT) boost and external beam radiation therapy (EBRT) alone for high-risk prostate cancer. This was a single-center, retrospective and observational study. Consecutive patients who underwent initial radical treatment by HDR-BT boost or EBRT alone from June 2009 to May 2016 at the Niigata University Medical and Dental Hospital, Japan were included. A total of 96 patients underwent HDR-BT boost, and 61 underwent EBRT alone. The prescription dose of HDR-BT boost was set to 18 Gy twice a day with EBRT 39 Gy/13 fractions. The dose for EBRT alone was mostly 70 Gy/28 fractions. The high-risk group received >6 months of prior androgen deprivation therapy. Overall survival, biochemical-free survival, local control and distant metastasis-free survival rates at 5 years were analyzed. The incidence of urological and gastrointestinal late adverse events of Grade 2 and above was also summarized. In the National Comprehensive Cancer Network (NCCN) high-risk calssification, HDR-BT boost had a significantly higher biochemical-free survival rate at 5 years (98.9% versus 90.7%, P = 0.04). Urethral strictures were more common in the HDR-BT boost group. We will continuously observe the progress of the study patients and determine the longer term results.

Maximum distance in single-isocenter technique of stereotactic radiosurgery with rotational error using margin-based analysis.

Through geometrical simulation, we evaluated the effect of rotational error in patient setup on geometrical coverage and calculated the maximum distance between the isocenter and target, where the clinical PTV margin secures geometrical coverage with a single-isocenter technique. We used simulated spherical GTVs with diameters of 1.0 (GTV 1), 1.5 (GTV 2), 2.0 (GTV 3), and 3.0 cm (GTV 4). The location of the target center was set such that the distance between the target and isocenter ranged from 0 to 15 cm. We created geometrical coverage vectors so that each target was entirely covered by 100% of the prescribed dose. The vectors of the target positions were simultaneously rotated within a range of 0°-2.0° around the x-, y-, and z-axes. For each rotational error, the reduction in geometrical coverage of the targets was calculated and compared with that obtained for a rotational error of 0°. The tolerance value of the geometrical coverage reduction was defined as 5% of the GTV. The maximum distance that satisfied the 5% tolerance value for different values of rotational error at a clinical PTV margin of 0.1 cm was calculated. When the rotational errors were 0.5° for a 0.1 cm PTV margin, the maximum distances were as follows: GTV 1: 7.6 cm; GTV 2: 10.9 cm; GTV 3: 14.3 cm; and GTV 4: 21.4 cm. It might be advisable to exclude targets that are > 7.6 cm away from the isocenter with a single-isocenter technique to satisfy the tolerance value for all GTVs.

Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.

PURPOSE: We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) with an electric portal imaging device (EPID). METHODS: Fluence maps measured with EPID for 38 beams from 19 clinical IMRT plans were assessed. Plans with various degrees of error in MLC modeling parameters [i.e., MLC transmission factor (TF) and dosimetric leaf gap (DLG)] and plans with an MLC positional error for comparison were created. For a total of 152 error plans for each type of error, we calculated fluence difference maps for each beam by subtracting the calculated maps from the measured maps. A total of 837 radiomic features were extracted from each fluence difference map, and we determined the number of features used for the training dataset in the machine learning models by using random forest regression. Machine learning models using the five typical algorithms [decision tree, k-nearest neighbor (kNN), support vector machine (SVM), logistic regression, and random forest] for binary classification between the error-free plan and the plan with the corresponding error for each type of error were developed. We used part of the total dataset to perform fourfold cross-validation to tune the models, and we used the remaining test dataset to evaluate the performance of the developed models. A gamma analysis was also performed between the measured and calculated fluence maps with the criteria of 3%/2 and 2%/2 mm for all of the types of error. RESULTS: The radiomic features and its optimal number were similar for the models for the TF and the DLG error detection, which was different from the MLC positional error. The highest sensitivity was obtained as 0.913 for the TF error with SVM and logistic regression, 0.978 for the DLG error with kNN and SVM, and 1.000 for the MLC positional error with kNN, SVM, and random forest. The highest specificity was obtained as 1.000 for the TF error with a decision tree, SVM, and logistic regression, 1.000 for the DLG error with a decision tree, logistic regression, and random forest, and 0.909 for the MLC positional error with a decision tree and logistic regression. The gamma analysis showed the poorest performance in which sensitivities were 0.737 for the TF error and the DLG error and 0.882 for the MLC positional error for 3%/2 mm. The addition of another type of error to fluence maps significantly reduced the sensitivity for the TF and the DLG error, whereas no effect was observed for the MLC positional error detection. CONCLUSIONS: Compared to the conventional gamma analysis, the radiomics-based machine learning models showed higher sensitivity and specificity in detecting a single type of the MLC modeling error and the MLC positional error. Although the developed models need further improvement for detecting multiple types of error, radiomics-based IMRT QA was shown to be a promising approach for detecting the MLC modeling error.

Proton dose calculation based on converting dual-energy CT data to stopping power ratio (DEEDZ-SPR): a beam-hardening assessment.

To achieve an accurate stopping power ratio (SPR) prediction in particle therapy treatment planning, we previously proposed a simple conversion to the SPR from dual-energy (DE) computed tomography (CT) data via electron density and effective atomic number (Z eff) calibration (DEEDZ-SPR). This study was conducted to carry out an initial implementation of the DEEDZ-SPR conversion method with a clinical treatment planning system (TPS; VQA, Hitachi Ltd., Tokyo) for proton beam therapy. Consequently, this paper presents a proton therapy plan for an anthropomorphic phantom to evaluate the stability of the dose calculations obtained by the DEEDZ-SPR conversion against the variation of the calibration phantom size. Dual-energy x-ray CT images were acquired using a dual-source CT (DSCT) scanner. A single-energy CT (SECT) scan using the same DSCT scanner was also performed to compare the DEEDZ-SPR conversion with the SECT-based SPR (SECT-SPR) conversion. The scanner-specific parameters necessary for the SPR calibration were obtained from the CT images of tissue substitutes in a calibration phantom. Two calibration phantoms with different sizes (a 33 cm diameter phantom and an 18 cm diameter phantom) were used for the SPR calibrations to investigate the beam-hardening effect on dosimetric uncertainties. Each set of calibrated SPR data was applied to the proton therapy plan designed using the VQA TPS with a pencil beam algorithm for the anthropomorphic phantom. The treatment plans with the SECT-SPR conversion exhibited discrepancies between the dose distributions and the dose-volume histograms (DVHs) of the 33 cm and 18 cm phantom calibrations. In contrast, the corresponding dose distributions and the DVHs obtained using the DEEDZ-SPR conversion method coincided almost perfectly with each other. The DEEDZ-SPR conversion appears to be a promising method for providing proton dose plans that are stable against the size variations of the calibration phantom and the patient.

Radiobiological effects of the interruption time with Monte Carlo Simulation on multiple fields in photon beams.

PURPOSE: The interruption time is the irradiation interruption that occurs at sites and operations such as the gantry, collimator, couch rotation, and patient setup within the field in radiotherapy. However, the radiobiological effect of prolonging the treatment time by the interruption time for tumor cells is little evaluated. We investigated the effect of the interruption time on the radiobiological effectiveness with photon beams based on a modified microdosimetric kinetic (mMK) model. METHODS: The dose-mean lineal energy yD (keV/µm) of 6-MV photon beams was calculated by the particle and heavy ion transport system (PHITS). We set the absorbed dose to 2 or 8 Gy, and the interruption time (τ) was set to 1, 3, 5, 10, 30, and 60 min. The biological parameters such as α0, ß0, and DNA repair constant rate (a + c) values were acquired from a human non-small-cell lung cancer cell line (NCI-H460) for the mMK model. We used two-field and four-field irradiation with a constant dose rate (3 Gy/min); the photon beams were paused for interruption time τ. We calculated the relative biological effectiveness (RBE) to evaluate the interruption time's effect compared with no interrupted as a reference. RESULTS: The yD of 6-MV photon beams was 2.32 (keV/µm), and there was little effect by changing the water depth (standard deviation was 0.01). The RBE with four-field irradiation for 8 Gy was decreased to 0.997, 0.975, 0.900, and 0.836 τ = 1, 10, 30, 60 min, respectively. In addition, the RBE was affected by the repair constant rate (a + c) value, the greater the decrease in RBE with the longer the interruption time when the (a + c) value was large. CONCLUSION: The ~10-min interruption of 6-MV photon beams did not significantly impact the radiobiological effectiveness, since the RBE decrease was <3%. Nevertheless, the RBE's effect on tumor cells was decreased about 30% by increasing the 60 min interruption time at 8 Gy with four-field irradiation. It is thus necessary to make the interruption time as short as possible.

Effect of setup error in the single-isocenter technique on stereotactic radiosurgery for multiple brain metastases.

In conventional stereotactic radiosurgery (SRS), treatment of multiple brain metastases using multiple isocenters is time-consuming resulting in long dose delivery times for patients. A single-isocenter technique has been developed which enables the simultaneous irradiation of multiple targets at one isocenter. This technique requires accurate positioning of the patient to ensure optimal dose coverage. We evaluated the effect of six degrees of freedom (6DoF) setup errors in patient setups on SRS dose distributions for multiple brain metastases using a single-isocenter technique. We used simulated spherical gross tumor volumes (GTVs) with diameters ranging from 1.0 to 3.0 cm. The distance from the isocenter to the target's center was varied from 0 to 15 cm. We created dose distributions so that each target was entirely covered by 100% of the prescribed dose. The target's position vectors were rotated from 0°-2.0° and translated from 0-1.0 mm with respect to the three axes in space. The reduction in dose coverage for the targets for each setup error was calculated and compared with zero setup error. The calculated margins for the GTV necessary to satisfy the tolerance values for loss of GTV coverage of 3% to 10% were defined as coverage-based margins. In addition, the maximum isocenter to target distance for different 6DoF setup errors was calculated to satisfy the tolerance values. The dose coverage reduction and coverage-based margins increased as the target diameter decreased, and the distance and 6DoF setup error increased. An increase in setup error when a single-isocenter technique is used may increase the risk of missing the tumor; this risk increases with increasing distance from the isocenter and decreasing tumor size.

Dosimetric comparison of analytic anisotropic algorithm and Acuros XB algorithm in VMAT plans for high-grade glioma.

PURPOSE: To investigate the dosimetric impact between the anisotropic analytical algorithm (AAA) and the Acuros XB (AXB) algorithm in volumetric-modulated arc therapy (VMAT) plans for high-grade glioma (HGG). METHODS: We used a heterogeneous phantom to quantify the agreement between the measured and calculated doses from the AAA and from the AXB. We then analyzed 14 patients with HGG treated by VMAT, using the AAA. We newly created AXB plans for each corresponding AAA plan under the following conditions: (1) re-calculation for the same number of monitor units with an identical beam and leaf setup, and (2) re-optimization under the same conditions of dose constraints. The dose coverage for the planning target volume (PTV) was evaluated by dividing the coverage into the skull, air, and soft-tissue regions. RESULTS: Compared to the results obtained with the AAA, the AXB results were in good agreement with the measured profiles. The dose differences in the PTV between the AAA and re-calculated AXB plans were large in the skull region contained in the target. The dose difference in the PTV in both types of plan was significantly correlated with the volume of the skull contained in the target (r = 0.71, p = 0.0042). A re-optimized AXB plan's dose difference was lower vs. the re-calculated AXB plan's. CONCLUSIONS: We observed dose differences between the AAA and AXB plans, in particular in the cases in which the skull region of the target was large. Considering the phantom measurement results, the AXB algorithm should be used in VMAT plans for HGG.

The association between oral candidiasis and severity of chemoradiotherapy-induced dysphagia in head and neck cancer patients: A retrospective cohort study.

BACKGROUND AND PURPOSE: Concurrent chemoradiotherapy (CCRT) for head and neck cancer (HNC) is a risk factor for oral candidiasis (OC). As Candida spp. are highly virulent, we conducted a retrospective study to determine whether OC increases the severity of dysphagia related to mucositis in HNC patients. PATIENTS AND METHODS: We retrospectively analyzed the cases of consecutive patients with carcinomas of the oral cavity, pharynx, and larynx who underwent CCRT containing cisplatin (CDDP) at our hospital. The diagnosis of OC was based on gross mucosal appearance. We performed a multivariate analysis to determine whether OC was associated with the development of grade 3 dysphagia in the Radiation Therapy Oncology Group (RTOG) Acute Toxicity Criteria. The maximum of the daily opioid doses was compared between the patients with and without OC. RESULTS: We identified 138 HNC patients. OC was observed in 51 patients (37%). By the time of their OC diagnosis, 19 (37%) had already developed grade 3 dysphagia. Among the 30 patients receiving antifungal therapy, 12 (40%) showed clinical deterioration. In the multivariate analysis, OC was independently associated with grade 3 dysphagia (OR 2.75; 95%CI 1.22-6.23; p = 0.015). The patients with OC required significantly higher morphine-equivalent doses of opioids (45 vs. 30 mg/day; p = 0.029). CONCLUSION: Candida infection causes refractory dysphagia. It is worth investigating whether antifungal prophylaxis reduces severe dysphagia related to candidiasis.