Preliminary result of combined treatment with scanning carbon-ion radiotherapy and image-guided brachytherapy for locally advanced cervical adenocarcinoma.

Although there is growing evidence of the efficacy of carbon-ion radiotherapy (CIRT) for locally advanced cervical adenocarcinoma, reports on combined treatment with CIRT and image-guided brachytherapy (IGBT) are scarce. We retrospectively analyzed patients with International Federation of Gynecology and Obstetrics (2008) stage II-IVA locally advanced cervical adenocarcinoma who received combined scanning CIRT (sCIRT) and IGBT between April 2019 and March 2022. sCIRT consisted of whole-pelvic irradiation with 36 Gy (relative biological effectiveness [RBE]) in 12 fractions and subsequent local boost irradiation with 19.2 Gy (RBE) in 4 fractions. Three sessions of IGBT were administered after completion of sCIRT. Concurrent chemotherapy using weekly cisplatin (40 mg/m2/week) was also administered. Efficacy, toxicity and dose-volume parameters were analyzed. Fifteen patients were included in the analysis. The median follow-up period was 25 months. The 2-year overall survival, progression-free survival and local control rates were 92.3% (95% confidence interval [CI] = 77.8-100%), 52.5% (95% CI = 26.9-78.1%) and 84.8% (95% CI = 65.2-100%), respectively. Neither severe acute toxicity necessitating treatment cessation nor grade 3 or higher late toxicity were observed. The sigmoid D2cm3 of the patient who developed grade 2 late sigmoid hemorrhage was 65.6 Gy, which exceeded the standard deviation and target dose. The combination of sCIRT and IGBT for locally advanced cervical adenocarcinoma showed acceptable efficacy and safety. Further large-scale and long-term studies are warranted to confirm the efficacy and safety of this treatment.

Enhancing algal growth and nutrient recovery from anaerobic digestion piggery effluent by an integrated pretreatment strategy of ammonia stripping and flocculation.

Anaerobic digestion piggery effluent (ADPE) with a quite high ammonium (NH4 +) concentration and turbidity (dark brown color) generally requires high dilution before microalgae cultivation, owing to its NH4 + toxicity and color inhibition to algal growth. An integrated pretreatment strategy of ammonia stripping and chemical flocculation may be a more practical pretreatment procedure for enhancing algae yield and nutrient recovery from anaerobic digestion piggery effluent. In this study, we determined the optimum pretreatment strategy of anaerobic digestion piggery effluent for subsequent microalgae cultivation and nutrient recovery. The results showed that the integrated anaerobic digestion piggery effluent pretreatment strategy of high-temperature ammonia stripping and chemical flocculation at a mixed dosage of 2 g L-1 polyaluminum chloride (PAC) and 40 mg L-1 cationic polyacrylamide (C-PAM), and 50 mg L-1 ammonium nitrogen (NH4 +-N) enrichment provided maximum algal yield (optical density = 1.8) and nutrient removal (95.2%, 98.7%, 99.3%, and 78.5% for the removal efficiencies of total nitrogen, NH4 +-N, total phosphorus, and chemical oxygen demand, respectively) from anaerobic digestion piggery effluent. The integrated pretreatment strategy is expected to become a more practical pretreatment procedure for enhancing algae yield and nutrient recovery from anaerobic digestion piggery effluent.

Robust treatment planning in scanned carbon-ion radiotherapy for pancreatic cancer: Clinical verification using in-room computed tomography images.

Purpose: Carbon-ion beam (C-beam) has a sharp dose distribution called the Bragg peak. Carbon-ion radiation therapy, such as stereotactic body radiotherapy in photon radiotherapy, can be completed in a short period by concentrating the radiation dose on the tumor while minimizing the dose to organs at-risk. However, the stopping position of C-beam is sensitive to density variations along the beam path and such variations can lower the tumor dose as well as cause the delivery of an unexpectedly high dose to the organs at risk. We evaluated the clinical efficacy of a robust planning technique considering gastrointestinal gas (G-gas) to deliver accurate radiation doses in carbon-ion radiotherapy for pancreatic cancer. Materials and methods: We focused on the computed tomography (CT) value replacement method. Replacement signifies the overwriting of CT values in the CT images. The most effective replacement method for robust treatment planning was determined by verifying the effects of the three replacement patterns. We selected 10 consecutive patients. Pattern 1 replaces the CT value of the G-gas contours with the value of the region without G-gas (P1). This condition indicates a no-gas state. Pattern 2 replaces each gastrointestinal contour using the mean CT value of each contour (P2). The effect of G-gas was included in the replacement value. Pattern 3 indicates no replacement (P3). We analyzed variations in the target coverage (TC) and homogeneity index (HI) from the initial plan using in-room CT images. We then performed correlation analysis on the variations in G-gas, TC, and HI to evaluate the robustness against G-gas. Results: Analysis of variations in TC and HI revealed a significant difference between P1 and P3 and between P2 and P3. Although no statistically significant difference was observed between P1 and P2, variations, including the median, tended to be fewer in P2. The correlation analyses for G-gas, TC, and HI showed that P2 was less likely to be affected by G-gas. Conclusion: For a treatment plan that is robust to G-gas, P2 mean replacement method should be used. This method does not necessitate any particular software or equipment, and is convenient to implement in clinical practice.

Carbon-Ion Radiotherapy Using Metal Artifact Reduction Computed Tomography in a Patient with Prostate Cancer with Bilateral Hip Prostheses: A Case Report.

Carbon-ion radiotherapy (CIRT) for prostate cancer is both safe and efficacious; beam range calculations use relative stopping power ratio, which is derived from computed tomography (CT) values. However, hip prostheses are made of high atomic number materials and show severe artifacts on CT images. Therefore, it is not possible to accurately calculate dose distribution for CIRT in patients with prostate cancer with hip prostheses. Here, we describe the management of a 77-year-old man with prostate cancer who had previously undergone bilateral hip replacement. CIRT, in combination with androgen deprivation therapy, was recommended as definitive treatment for prostate cancer. Planning CT, magnetic resonance (MRI), and CT images with metal artifact reduction (MAR) were acquired for CIRT planning. MRI and MAR images were superimposed on the planning CT to delineate target volume and organs at risk. The radiation treatment plan consisted of a total dose of 51.6 Gy (relative biological effect) to be delivered in 12 fractions over 3 weeks, and the patient was irradiated in the supine and prone positions with a vertical beam, on alternating days. CIRT was completed as scheduled. No adverse events were observed during treatment or at 3 months after treatment initiation. While we show that CIRT may be a treatment option for patients with prostate cancer with bilateral hip prostheses, further studies are needed to evaluate treatment efficacy and late toxicity and to determine how CIRT can be administered to patients with prostate cancer with bilateral hip prostheses.

Comparison of Dose Distribution Between VMAT-SBRT and Scanning Carbon-ion Radiotherapy for Early-stage NSCLC.

BACKGROUND/AIM: The purpose of this study was to compare the dose distribution between scanning carbon-ion radiotherapy (sCIRT) and volumetric-modulated arc therapy with stereotactic body radiation therapy (VMAT-SBRT) for stage I non-small cell lung cancer (NSCLC). PATIENTS AND METHODS: Fifteen patients with early-stage NSCLC who underwent sCIRT at Kanagawa Cancer Center between 2018-2020 were enrolled. Dose-volume histogram parameters of the planned target volume and normal organs for sCIRT and VMAT-SBRT were evaluated. RESULTS: The homogeneity index of the target volume of sCIRT was significantly lower than that of VMAT-SBRT. The dose of sCIRT was significantly lower than that of VMAT-SBRT at low volumes in the lung, heart, spinal cord, and esophagus. CONCLUSION: The dose distribution of sCIRT for early-stage NSCLC was better than that of VMAT-SBRT.

Interfractional robustness of scanning carbon ion radiotherapy for prostate cancer: An analysis based on dose distribution from daily in-room CT images.

PURPOSE: We analyzed interfractional robustness of scanning carbon ion radiotherapy (CIRT) for prostate cancer based on the dose distribution using daily in-room computed tomography (CT) images. MATERIALS AND METHODS: We analyzed 11 consecutive patients treated with scanning CIRT for localized prostate cancer in our hospital between December 2015 and January 2016. In-room CT images were taken under treatment conditions in every treatment session. The dose distribution on each in-room CT image was recalculated, while retaining the pencil beam arrangement of the initial treatment plan. Then, the dose-volume histogram (DVH) parameters including the percentage of the clinical target volume (CTV) with 95% and 90% of the prescribed dose area (V95% of CTV, V90% of CTV) and V80% of rectum were calculated. The acceptance criteria for the CTV and rectum were set at V95% of CTV ≥95%, V90% of CTV ≥98%, and V80% of rectum < 10 ml. RESULTS: V95% of CTV, V90% of CTV, and V80% of rectum for the reproduced plans were 98.8 ± 3.49%, 99.5 ± 2.15%, and 4.39 ± 3.96 ml, respectively. Acceptance of V95% of CTV, V90% of CTV, and V80% of rectum was obtained in 123 (94%), 125 (95%) and 117 sessions (89%), respectively. Acceptance of the mean dose of V95% of CTV, V90% of CTV, and V80% of rectum for each patient was obtained in 10 (91%), 10 (91%), and 11 patients (100%), respectively. CONCLUSIONS: We demonstrated acceptable interfractional robustness based on the dose distribution in scanning CIRT for prostate cancer.

Dosimetric Comparison Between Carbon-ion Radiotherapy and Photon Radiotherapy for Stage I Esophageal Cancer.

BACKGROUND/AIM: The present study aimed to compare the radiation dose distribution of carbon-ion radiotherapy (CIRT) for stage I esophageal cancer with three-dimensional conformal radiotherapy (3DCRT) and volumetric modulated arc therapy (VMAT). PATIENTS AND METHODS: Fifteen patients with cT1bN0M0 esophageal cancer who received 3DCRT at Kanagawa Cancer Center between January 2014 and April 2019 were enrolled. The dose-volume histogram parameters of the target volume and normal organs planned with CIRT, 3DCRT, and VMAT were evaluated. RESULTS: The homogeneity index for the target volume of CIRT was significantly lower than that of 3DCRT and VMAT. In addition, the radiation dose of CIRT to the heart, lungs, spinal cord, and skin was significantly lower than that of 3DCRT and VMAT. CONCLUSION: Favorable dose distributions with CIRT were demonstrated compared with 3DCRT and VMAT for esophageal cancer.

Comparison of predictive performance for toxicity by accumulative dose of DVH parameter addition and DIR addition for cervical cancer patients.

We compared predictive performance between dose volume histogram (DVH) parameter addition and deformable image registration (DIR) addition for gastrointestinal (GI) toxicity in cervical cancer patients. A total of 59 patients receiving brachytherapy and external beam radiotherapy were analyzed retrospectively. The accumulative dose was calculated by three methods: conventional DVH parameter addition, full DIR addition and partial DIR addition. ${D}_{2{cm}^3}$, ${D}_{1{cm}^3}$ and ${D}_{0.1{cm}^3}$ (minimum doses to the most exposed 2 cm3, 1cm3 and 0.1 cm3 of tissue, respectively) of the rectum and sigmoid were calculated by each method. V50, V60 and V70 Gy (volume irradiated over 50, 60 and 70 Gy, respectively) were calculated in full DIR addition. The DVH parameters were compared between toxicity (≥grade1) and non-toxicity groups. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves were compared to evaluate the predictive performance of each method. The differences between toxicity and non-toxicity groups in ${D}_{2{cm}^3}$ were 0.2, 5.7 and 3.1 Gy for the DVH parameter addition, full DIR addition and partial DIR addition, respectively. The AUCs of ${D}_{2{cm}^3}$ were 0.51, 0.67 and 0.57 for DVH parameter addition, full DIR addition and partial DIR addition, respectively. In full DIR addition, the difference in dose between toxicity and non-toxicity was the largest and AUC was the highest. AUCs of V50, V60 and V70 Gy were 0.51, 0.63 and 0.62, respectively, and V60 and V70 were high values close to the value of ${D}_{2{cm}^3}$ of the full DIR addition. Our results suggested that the full DIR addition may have the potential to predict toxicity more accurately than the conventional DVH parameter addition, and that it could be more effective to accumulate to all pelvic irradiation by DIR.

Quantitative analysis of intra-fractional variation in CT-based image guided brachytherapy for cervical cancer patients.

We quantified intra-fractional dose variation and organ movement during CT-based 3D-image guided brachytherapy (3D-IGBT) in cervical cancer patients. Fifteen patients who underwent CT-based 3D-IGBT were studied. For all patients, pre-delivery CT for treatment planning after applicator insertion and post-delivery CT after dose delivery without changing the applicator position were acquired. Pre- and post-delivery CT were rigidly fused by matching the inserted applicator and planned dose on pre-delivery CT (pre-delivery dose) was mapped on post-delivery CT (post-delivery dose). D2, D1, and D0.1 cm3 of the rectum and bladder were compared between pre- and post-delivery doses with contours on each CT image. Organ movement and deformation was evaluated using deformation vector fields calculated by deformable image registration between pre- and post-delivery CT. We also evaluated dose variation and DVF between with and without a catheter to control filling. Differences in all DVH parameters were <±3% in physical dose and ± 5% in EQD2. However, a > 15% dose difference was found in 13.8% of the fractions in rectum D2 cm3 and in 11.1% of those in bladder D2 cm3. The mean value of DVF for bladder was larger than that of rectum, especially for the superior-inferior (S-I) direction. Insertion catheters in bladder reduced mean dose and DVF variation compared with that of without catheters. In fraction groups with large dose increasing, DVF in the S-I direction was significantly larger than that of other fraction groups. Our results indicated that preparation is needed to reduce changes in the S-I direction affect dose variation.

Multi-atlas-based auto-segmentation for prostatic urethra using novel prediction of deformable image registration accuracy.

PURPOSE: Accurate identification of the prostatic urethra and bladder can help determine dosing and evaluate urinary toxicity during intensity-modulated radiation therapy (IMRT) planning in patients with localized prostate cancer. However, it is challenging to locate the prostatic urethra in planning computed tomography (pCT). In the present study, we developed a multiatlas-based auto-segmentation method for prostatic urethra identification using deformable image registration accuracy prediction with machine learning (ML) and assessed its feasibility. METHODS: We examined 120 patients with prostate cancer treated with IMRT. All patients underwent temporary urinary catheter placement for identification and contouring of the prostatic urethra in pCT images (ground truth). Our method comprises the following three steps: (a) select four atlas datasets from the atlas datasets using the deformable image registration (DIR) accuracy prediction model, (b) deform them by structure-based DIR, (3) and propagate urethra contour using displacement vector field calculated by the DIR. In (a), for identifying suitable datasets, we used the trained support vector machine regression (SVR) model and five feature descriptors (e.g., prostate volume) to increase DIR accuracy. This method was trained/validated using 100 patients and performance was evaluated within an independent test set of 20 patients. Fivefold cross-validation was used to optimize the hype parameters of the DIR accuracy prediction model. We assessed the accuracy of our method by comparing it with those of two others: Acostas method-based patient selection (previous study method, by Acosta et al.), and the Waterman's method (defines the prostatic urethra based on the center of the prostate, by Waterman et al.). We used the centerlines distance (CLD) between the ground truth and the predicted prostatic urethra as the evaluation index. RESULTS: The CLD in the entire prostatic urethra was 2.09 ± 0.89 mm (our proposed method), 2.77 ± 0.99 mm (Acosta et al., P = 0.022), and 3.47 ± 1.19 mm (Waterman et al., P < 0.001); our proposed method showed the highest accuracy. In segmented CLD, CLD in the top 1/3 segment was highly improved from that of Waterman et.al. and was slightly improved from that of Acosta et.al., with results of 2.49 ± 1.78 mm (our proposed method), 2.95 ± 1.75 mm (Acosta et al., P = 0.42), and 5.76 ± 3.09 mm (Waterman et al., P < 0.001). CONCLUSIONS: We developed a DIR accuracy prediction model-based multiatlas-based auto-segmentation method for prostatic urethra identification. Our method identified prostatic urethra with mean error of 2.09 mm, likely due to combined effects of SVR model employment in patient selection, modified atlas dataset characteristics and DIR algorithm. Our method has potential utility in prostate cancer IMRT and can replace use of temporary indwelling urinary catheters.

A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients.

The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity-modulated radiation therapy (IMRT) dose distribution using only contours in prostate cancer. In this study, which included 95 IMRT-treated prostate cancer patients with available dose distributions and contours for planning target volume (PTVs) and organs at risk (OARs), a supervised-learning approach was used for training, where the dose for a voxel set in the dataset was defined as the label. The adaptive moment estimation algorithm was employed for optimizing a 3D U-net similar network. Eighty cases were used for the training and validation set in 5-fold cross-validation, and the remaining 15 cases were used as the test set. The predicted dose distributions were compared with the clinical dose distributions, and the model performance was evaluated by comparison with RapidPlan™. Dose-volume histogram (DVH) parameters were calculated for each contour as evaluation indexes. The mean absolute errors (MAE) with one standard deviation (1SD) between the clinical and CNN-predicted doses were 1.10% ± 0.64%, 2.50% ± 1.17%, 2.04% ± 1.40%, and 2.08% ± 1.99% for D2, D98 in PTV-1 and V65 in rectum and V65 in bladder, respectively, whereas the MAEs with 1SD between the clinical and the RapidPlan™-generated doses were 1.01% ± 0.66%, 2.15% ± 1.25%, 5.34% ± 2.13% and 3.04% ± 1.79%, respectively. Our CNN model could predict dose distributions that were superior or comparable with that generated by RapidPlan™, suggesting the potential of CNN in dose distribution prediction.

Automated noncoplanar treatment planning strategy in stereotactic radiosurgery of multiple cranial metastases: HyperArc and CyberKnife dose distributions.

The purpose of this study was to evaluate and compare the dosimetric effects of HyperArc-based stereotactic radiosurgery (SRS) and a robotic radiosurgery system-based planning using CyberKnife for multiple cranial metastases. In total, 11 cancer patients with multiple cranial metastases (3 to 5 tumors) treated with CyberKnife were examined. These patients were replanned using HyperArc (Varian Medical Systems, Palo Alto, USA). HyperArc plan were designed using 4 noncoplanar arc single-isocenter VMAT in 6 MV flattening filter free mode for simulated delivery with the True beam STx (Varian). The prescription dose was 23 Gy at single fraction. Dosimetric differences and blinded clinician scoring differences were evaluated. Conformity index (CI) and gradient index (GI) were 0.60 ± 0.11 and 3.94 ± 0.74, respectively, for the CyberKnife plan and 0.87 ± 0.08 and 5.31 ± 1.42, respectively, for the HyperArc plan (p < 0.05). Total brain V12-gross tumor volumes (GTVs) for the CyberKnife and HyperArc plans were 5.26 ± 2.83 and 4.02 ± 1.71 cm3, respectively. These results indicate that HyperArc plan showed better CI and total brain V12-GTV, while CyberKnife plan showed better GI. A blinded physician scoring evaluation did not show significant differences between CyberKnife and HyperArc plans. The HyperArc-based SRS plan is comparable with the CyberKnife plan, suggesting a greater potential to emerge as a suitable tool for SRS of multiple brain metastases.

[Field Shape Optimization Technique Based on Dose Volume Histogram Using Daily Cone-beam Computed Tomography in Three-dimensional Conformal Radiation Therapy for Localized Prostate Cancer: Develop and Evaluation].

This study aimed to develop and evaluate field shape optimization technique based on dose calculation using daily cone-beam computed tomography (CBCT) to compensate for interfractional anatomic changes in three-dimensional conformal radiation therapy (3D-CRT) for prostate cancer. For each of 10 patients, 9-10 CBCT images were obtained throughout the treatment course. The prostate, seminal vesicles, and rectum were manually contoured in all CBCT images. Subsequently, plan adaptation was performed with a program developed in-house. This program calculates dose distributions on CBCT images and optimizes field shape to minimize rectal dose while keeping the target at the optimal dose coverage (the planning target volume D95% receives 95% of the prescription dose). To evaluate the adaptive planning approach, we re-calculated dose distributions on CBCT images based on the conventional and adaptive plans. For the entire cohort, plan adaptation improved rectal V50 Gy, V60 Gy, V65 Gy, and V70 Gy by -7.71±8.43%, -8.30±8.90%, -7.91±8.51% and -7.03±7.70% on average (±SD), respectively. Our results demonstrate that adaptive planning approach is superior to the conventional planning approach for optimizing dose distribution, and this adaptive approach can optimize field shape in 3 min. The proposed approach can be an effective solution for the problem of interfractional anatomic changes in 3D-CRT for prostate cancer.

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.

Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network.

The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been conducted to increase the quality of planning and reduce the costs associated with planning through automated planning method; however, few studies have used the deep learning method for optimization of planning. The purpose of this study was to propose an automated method based on a convolutional neural network (CNN) for predicting the dosimetric eligibility of patients with prostate cancer undergoing IMRT. Sixty patients with prostate cancer who underwent IMRT were included in the study. Treatment strategy involved division of the patients into two groups, namely, meeting all dose constraints and not meeting all dose constraints, by experienced medical physicists. We used AlexNet (i.e., one of common CNN architectures) for CNN-based methods to predict the two groups. An AlexNet CNN pre-trained on ImageNet was fine-tuned. Two dataset formats were used as input data: planning computed tomography (CT) images and structure labels. Five-fold cross-validation was used, and performance metrics included sensitivity, specificity, and prediction accuracy. Class activation mapping was used to visualize the internal representation learned by the CNN. Prediction accuracies of the model with the planning CT image dataset and that with the structure label dataset were 56.7 ± 9.7% and 70.0 ± 11.3%, respectively. Moreover, the model with structure labels focused on areas associated with dose constraints. These results revealed the potential applicability of deep learning to the treatment planning of patients with prostate cancer undergoing IMRT.

A photon source model based on particle transport in a parameterized accelerator structure for Monte Carlo dose calculations.

PURPOSE: An accurate source model of a medical linear accelerator is essential for Monte Carlo (MC) dose calculations. This study aims to propose an analytical photon source model based on particle transport in parameterized accelerator structures, focusing on a more realistic determination of linac photon spectra compared to existing approaches. METHODS: We designed the primary and secondary photon sources based on the photons attenuated and scattered by a parameterized flattening filter. The primary photons were derived by attenuating bremsstrahlung photons based on the path length in the filter. Conversely, the secondary photons were derived from the decrement of the primary photons in the attenuation process. This design facilitates these sources to share the free parameters of the filter shape and be related to each other through the photon interaction in the filter. We introduced two other parameters of the primary photon source to describe the particle fluence in penumbral regions. All the parameters are optimized based on calculated dose curves in water using the pencil-beam-based algorithm. To verify the modeling accuracy, we compared the proposed model with the phase space data (PSD) of the Varian TrueBeam 6 and 15 MV accelerators in terms of the beam characteristics and the dose distributions. The EGS5 Monte Carlo code was used to calculate the dose distributions associated with the optimized model and reference PSD in a homogeneous water phantom and a heterogeneous lung phantom. We calculated the percentage of points passing 1D and 2D gamma analysis with 1%/1 mm criteria for the dose curves and lateral dose distributions, respectively. RESULTS: The optimized model accurately reproduced the spectral curves of the reference PSD both on- and off-axis. The depth dose and lateral dose profiles of the optimized model also showed good agreement with those of the reference PSD. The passing rates of the 1D gamma analysis with 1%/1 mm criteria between the model and PSD were 100% for 4 × 4, 10 × 10, and 20 × 20 cm2 fields at multiple depths. For the 2D dose distributions calculated in the heterogeneous lung phantom, the 2D gamma pass rate was 100% for 6 and 15 MV beams. The model optimization time was less than 4 min. CONCLUSION: The proposed source model optimization process accurately produces photon fluence spectra from a linac using valid physical properties, without detailed knowledge of the geometry of the linac head, and with minimal optimization time.

Quantifying the performance of two different types of commercial software programs for 3D patient dose reconstruction for prostate cancer patients: Machine log files vs. machine log files with EPID images.

We clarified the reconstructed 3D dose difference between two different commercial software programs (Mobius3D v2.0 and PerFRACTION v1.6.4). Five prostate cancer patients treated with IMRT (74 Gy/37 Fr) were studied. Log files and cine EPID images were acquired for each fraction. 3D patient dose was reconstructed using log files (Mobius3D) or log files with EPID imaging (PerFRACTION). The treatment planning dose was re-calculated on homogeneous and heterogeneous phantoms, and log files and cine EPID images were acquired. Measured doses were compared with the reconstructed point doses in the phantom. Next, we compared dosimetric metrics (mean dose for PTV, rectum, and bladder) calculated by Mobius3D and PerFRACTION for all fractions from five patients. Dose difference at isocenter between measurement and reconstructed dose for two software programs was within 3.0% in both homogeneous and heterogeneous phantoms. Moreover, the dose difference was larger using skip arc plan than that using full arc plan, especially for PerFRACTION (e.g., dose difference at isocenter for PerFRACTION: 0.34% for full arc plan vs. -4.50% for skip arc plan in patient 1). For patients, differences in dosimetric parameters were within 1% for almost all fractions. PerFRACTION had wider range of dose difference between first fraction and the other fractions than Mobius3D (e.g., maximum difference: 0.50% for Mobius3D vs. 1.85% for PerFRACTION), possibly because EPID may detect some types of MLC positioning errors such as miscalibration errors or mechanical backlash which cannot be detected by log files, or that EPID data might include image acquisition failure and image noise.

Effect of DIR uncertainty on prostate passive-scattering proton therapy dose accumulation.

Deformable image registration (DIR) is important in dose accumulation. Currently, the impact of DIR-algorithm-associated uncertainties in proton therapy is unclear. Here, we quantify the effect of DIR uncertainties on prostate passive-scattering proton therapy (PSPT) dose accumulation. Ten patients with an intermediate risk for prostate cancer formerly treated by PSPT (PTV D95=78GyE) were studied. Dose distributions from all verification CT images (five images per patient) were warped and accumulated in the planning CT geometries with DIR. The dose-volume histogram parameters (Dmean, V40, and V70) for rectum and bladder were calculated. Two commercially available DIR software packages were employed: Velocity AI (Varian Medical Systems) and RayStation (RaySearch Laboratories). The dice similarity coefficient (DSC) and surface distance, which were calculated between planning CT contours and deformed contours, were used for DIR validation, with the relationship between the dose parameter and DIR uncertainty ultimately investigated. On average, when using RayStation, the DSC increased by 0.14 and surface distance decreased by 6.4mm, as compared to Velocity. For Dmean, V40, and V70 to the rectum, the average differences between the RayStation and Velocity were 3.9GyE, 5.5%, and 3.2%, respectively. For the bladder, the differences were 5.2GyE, 5.8%, and 5.4%, respectively. The maximum differences in V40 between RayStation and Velocity were 14.4% and 22.8% for the rectum and bladder, respectively, when the average DSC and surface distance differences were more than 0.14 and 6.4mm, respectively. Such results suggest that DIR uncertainties might significantly affect prostate PSPT dose accumulations.

Evaluation of the performance of deformable image registration between planning CT and CBCT images for the pelvic region: comparison between hybrid and intensity-based DIR.

This study aimed to evaluate the performance of the hybrid deformable image registration (DIR) method in comparison with intensity-based DIR for pelvic cone-beam computed tomography (CBCT) images, using intensity and anatomical information. Ten prostate cancer patients treated with intensity-modulated radiation therapy (IMRT) were studied. Nine or ten CBCT scans were performed for each patient. First, rigid registration was performed between the planning CT and all CBCT images using gold fiducial markers, and then DIR was performed. The Dice similarity coefficient (DSC) and center of mass (COM) displacement were used to evaluate the quantitative DIR accuracy. The average DSCs for intensity-based DIR for the prostate, rectum, bladder, and seminal vesicles were 0.84 ± 0.05, 0.75 ± 0.05, 0.69 ± 0.07 and 0.65 ± 0.11, respectively, whereas those values for hybrid DIR were 0.98 ± 0.00, 0.97 ± 0.01, 0.98 ± 0.00 and 0.94 ± 0.03, respectively (P < 0.05). The average COM displacements for intensity-based DIR for the prostate, rectum, bladder, and seminal vesicles were 2.0 ± 1.5, 3.7 ± 1.4, 7.8 ± 2.2 and 3.6 ± 1.2 mm, whereas those values for hybrid DIR were 0.1 ± 0.0, 0.3 ± 0.2, 0.2 ± 0.1 and 0.6 ± 0.6 mm, respectively (P < 0.05). These results showed that the DSC for hybrid DIR had a higher DSC value and smaller COM displacement for all structures and all patients, compared with intensity-based DIR. Thus, the accumulative dose based on hybrid DIR might be trusted as a high-precision dose estimation method that takes into account organ movement during treatment radiotherapy.