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Automated planning has surged in popularity within external beam radiation therapy in recent times. Leveraging insights from previous clinical knowledge could enhance auto-planning quality. In this work, we evaluated the performance of Ethos automated planning with knowledge-based guidance, specifically using Rapidplan (RP). Seventy-four patients with head-and-neck (HN) cancer and 37 patients with prostate cancer were used to construct separate RP models. Additionally, 16 patients from each group (HN and prostate) were selected to assess the performance of Ethos auto-planning results. Initially, a template-based Ethos plan (Non-RP plan) was generated, followed by integrating the corresponding RP model's DVH estimates into the optimization process to generate another plan (RP plan). We compared the target coverage, OAR doses, and total monitor units between the non-RP and RP plans. Both RP and non-RP plans achieved comparable target coverage in HN and Prostate cases, with a negligible difference of less than 0.5% (p > 0.2). RP plans consistently demonstrated lower doses of OARs in both HN and prostate cases. Specifically, the mean doses of OARs were significantly reduced by 9% (p < 0.05). RP plans required slightly higher monitor units in both HN and prostate sites (p < 0.05), however, the plan generation time was almost similar (p > 0.07). The inclusion of the RP model reduced the OAR doses, particularly reducing the mean dose to critical organs compared to non-RP plans while maintaining similar target coverage. Our findings provide valuable insights for clinics adopting Ethos planning, potentially enhancing the auto-planning to operate optimally., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose/Objective . Small-field measurement poses challenges. Although many high-resolution detectors are commercially available, the EPID for small-field dosimetry remains underexplored. This study aimed to evaluate the performance of EPID for small-field measurements and to derive tailored correction factors for precise small-field dosimetry verification. Material/Methods . Six high-resolution radiation detectors, including W2 and W1 plastic scintillators, Edge-detector, microSilicon, microDiamond and EPID were utilized. The output factors, depth doses and profiles, were measured for various beam energies (6 MV-FF, 6 MV-FFF, 10 MV-FF, and 10 MV-FFF) and field sizes (10 × 10 cm 2 , 5 × 5 cm 2 , 4 × 4 cm 2 , 3 × 3 cm 2 , 2 × 2 cm 2 , 1 × 1 cm 2 , 0.5 × 0.5 cm 2 ) using a Varian Truebeam linear accelerator. During measurements, acrylic plates of appropriate depth were placed on the EPID, while a 3D water tank was used with five-point detectors. EPID measured data were compared with W2 plastic scintillator and measurements from other high-resolution detectors. The analysis included percentage deviations in output factors, differences in percentage for PDD and for the profiles, FWHM, maximum difference in the flat region, penumbra, and 1D gamma were analyzed. The output factor and depth dose ratios were fitted using exponential functions and fractional polynomial fitting in STATA 16.2, with W2 scintillator as reference, and corresponding formulae were obtained. The established correction factors were validated using two Truebeam machines. Results . When comparing EPID and W2-PSD across all field-sizes and energies, the deviation for output factors ranged from 1% to 15%. Depth doses, the percentage difference beyond dmax ranged from 1% to 19%. For profiles, maximum of 4% was observed in the 100%-80% region. The correction factor formulae were validated with two independent EPIDs and closely matched within 3%. Conclusion . EPID can effectively serve as small-field dosimetry verification tool with appropriate correction factors., (Creative Commons Attribution license.)

Ultra-hypofractionated radiotherapy (UHF RT) is revolutionizing the treatment approach for low- and intermediate-risk prostate cancer patients. This study reports the planning process of UHF RT utilizing the cone beam computed tomography (CBCT)-based online adaptive radiotherapy (OART) treatment with the Ethos system, focusing on a comparative analysis between OART and image-guided radiotherapy (IGRT) plans. We also assessed the pre-planning capabilities of the Ethos system against the CyberKnife (CK) (Accuray, Sunnyvale, CA) system. A 66-year-old patient, diagnosed with prostatic acinar adenocarcinoma confirmed via biopsy and presenting with elevated prostate-specific antigen (PSA) levels, underwent UHF OART treatment using the Ethos system. The planning encompassed delineating the gross target volume (GTV) as the prostate, while the clinical target volume (CTV) comprised the prostate and proximal seminal vesicle. The planning target volume (PTV) was derived from the CTV with a 5 mm external margin except for a 3 mm posterior margin. A simultaneous integrated boost (SIB) technique was employed, delivering 40 Gy in five fractions (8 Gy per fraction) to the gross tumor volume (GTV) and 36.25 Gy in five fractions (7.25 Gy per fraction) to the remaining part of the planning target volume (PTV), with treatments scheduled biweekly. We compared OART and IGRT plans and conducted a comparative analysis between Ethos planning and the CK system for pre-planning assessment. When comparing Ethos planning and CK plans, Ethos demonstrated slightly better target coverage and organ-at-risk (OAR) sparing. However, CK plans showed superior containment of low-dose spillage, particularly at 50% and 25% iso-doses, due to non-coplanar beam arrangements. Our results demonstrated that OART plans yielded superior target coverage and improved OAR sparing compared to IGRT plans. Notably, the entire OART process, from planning to delivery, was accomplished within 27 minutes. The Ethos OART system's ability to adapt to daily anatomical changes, efficient workflow, and superior OAR-sparing capabilities make it a promising option for prostate cancer treatment using UHF RT., Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Yoganathan et al.)

Purpose . This study aims to introduce an innovative noninvasive method that leverages a single image for both grading and staging prediction. The grade and the stage of cervix cancer (CC) are determined from diffusion-weighted imaging (DWI) in particular apparent diffusion coefficient (ADC) maps using deep convolutional neural networks (DCNN). Methods . datasets composed of 85 patients having annotated tumor stage (I, II, III, and IV), out of this, 66 were with grade (II and III) and the remaining patients with no reported grade were retrospectively collected. The study was IRB approved. For each patient, sagittal and axial slices containing the gross tumor volume (GTV) were extracted from ADC maps. These were computed using the mono exponential model from diffusion weighted images (b-values = 0, 100, 1000) that were acquired prior to radiotherapy treatment. Balanced training sets were created using the Synthetic Minority Oversampling Technique (SMOTE) and fed to the DCNN. EfficientNetB0 and EfficientNetB3 were transferred from the ImageNet application to binary and four-class classification tasks. Five-fold stratified cross validation was performed for the assessment of the networks. Multiple evaluation metrics were computed including the area under the receiver operating characteristic curve (AUC). Comparisons with Resnet50, Xception, and radiomic analysis were performed. Results . for grade prediction, EfficientNetB3 gave the best performance with AUC = 0.924. For stage prediction, EfficientNetB0 was the best with AUC = 0.931. The difference between both models was, however, small and not statistically significant EfficientNetB0-B3 outperformed ResNet50 (AUC = 0.71) and Xception (AUC = 0.89) in stage prediction, and demonstrated comparable results in grade classification, where AUCs of 0.89 and 0.90 were achieved by ResNet50 and Xception, respectively. DCNN outperformed radiomic analysis that gave AUC = 0.67 (grade) and AUC = 0.66 (stage). Conclusion. the prediction of CC grade and stage from ADC maps is feasible by adapting EfficientNet approaches to the medical context., (© 2024 IOP Publishing Ltd.)

Background and Purpose: Accurate CT numbers in Cone Beam CT (CBCT) are crucial for precise dose calculations in adaptive radiotherapy (ART). This study aimed to generate synthetic CT (sCT) from CBCT using deep learning (DL) models in head and neck (HN) radiotherapy., Materials and Methods: A novel DL model, the 'self-attention-residual-UNet' (ResUNet), was developed for accurate sCT generation. ResUNet incorporates a self-attention mechanism in its long skip connections to enhance information transfer between the encoder and decoder. Data from 93 HN patients, each with planning CT (pCT) and first-day CBCT images were used. Model performance was evaluated using two DL approaches (non-adversarial and adversarial training) and two model types (2D axial only vs. 2.5D axial, sagittal, and coronal). ResUNet was compared with the traditional UNet through image quality assessment (Mean Absolute Error (MAE), Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM)) and dose calculation accuracy evaluation (DVH deviation and gamma evaluation (1 %/1mm))., Results: Image similarity evaluation results for the 2.5D-ResUNet and 2.5D-UNet models were: MAE: 46±7 HU vs. 51±9 HU, PSNR: 66.6±2.0 dB vs. 65.8±1.8 dB, and SSIM: 0.81±0.04 vs. 0.79±0.05. There were no significant differences in dose calculation accuracy between DL models. Both models demonstrated DVH deviation below 0.5 % and a gamma-pass-rate (1 %/1mm) exceeding 97 %., Conclusions: ResUNet enhanced CT number accuracy and image quality of sCT and outperformed UNet in sCT generation from CBCT. This method holds promise for generating precise sCT for HN ART., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.)

Real-time tracking of a target volume is a promising solution for reducing the planning margins and both dosimetric and geometric uncertainties in the treatment of thoracic and upper-abdomen cancers. Respiratory motion prediction is an integral part of real-time tracking to compensate for the latency of tracking systems. The purpose of this work was to develop a novel method for accurate respiratory motion prediction using dual deep recurrent neural networks (RNNs). The respiratory motion data of 111 patients were used to train and evaluate the method. For each patient, two models (Network1 and Network2) were trained on 80% of the respiratory wave, and the remaining 20% was used for evaluation. The first network (Network 1) is a 'coarse resolution' prediction of future points and second network (Network 2) provides a 'fine resolution' prediction to interpolate between the future predictions. The performance of the method was tested using two types of RNN algorithms : Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The accuracy of each model was evaluated using the root mean square error (RMSE) and mean absolute error (MAE). Overall, the RNN model with GRU- function had better accuracy than the RNN model with LSTM-function (RMSE (mm): 0.4 ± 0.2 versus 0.6 ± 0.3; MAE (mm): 0.4 ± 0.2 versus 0.6 ± 0.2). The GRU was able to predict the respiratory motion accurately (<1 mm) up to the latency period of 440 ms, and LSTM's accuracy was acceptable only up to 240 ms. The proposed method using GRU function can be used for respiratory-motion prediction up to a latency period of 440 ms., (© 2022 IOP Publishing Ltd.)

Purpose: To fully exploit the benefits of magnetic resonance imaging (MRI) for radiotherapy, it is desirable to develop segmentation methods to delineate patients' MRI images fast and accurately. The purpose of this work is to develop a semi-automatic method to segment organs and tumor within the brain on standard T1- and T2-weighted MRI images., Methods and Materials: Twelve brain cancer patients were retrospectively included in this study, and a simple rigid registration was used to align all the images to the same spatial coordinates. Regions of interest were created for organs and tumor segmentations. The K-nearest neighbor (KNN) classification algorithm was used to characterize the knowledge of previous segmentations using 15 image features (T1 and T2 image intensity, 4 Gabor filtered images, 6 image gradients, and 3 Cartesian coordinates), and the trained models were used to predict organ and tumor contours. Dice similarity coefficient (DSC), normalized surface dice, sensitivity, specificity, and Hausdorff distance were used to evaluate the performance of segmentations., Results: Our semi-automatic segmentations matched with the ground truths closely. The mean DSC value was between 0.49 (optical chiasm) and 0.89 (right eye) for organ segmentations and was 0.87 for tumor segmentation. Overall performance of our method is comparable or superior to the previous work, and the accuracy of our semi-automatic segmentation is generally better for large volume objects., Conclusion: The proposed KNN method can accurately segment organs and tumor using standard brain MRI images, provides fast and accurate image processing and planning tools, and paves the way for clinical implementation of MRI-guided radiotherapy and adaptive radiotherapy., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Journal of Medical Physics.)

Due to the complexity of advanced radiotherapy techniques, treatment planning process is usually time consuming and plan quality can vary considerably among planners and institutions. It is also impractical to generate all possible treatment plans based on available radiotherapy techniques and select the best option for a specific patient. Automatic dose prediction will be very helpful in these situations, while there were a few studies of three-dimensional (3D) dose prediction for patients who received radiotherapy. The purpose of this work was to develop a novel atlas-based method to predict 3D dose prediction and to evaluate its performance. Previously treated nineteen left-sided post-mastectomy breast cancer patients and sixteen prostate cancer patients were included in this study. One patient was arbitrarily chosen as the reference for each type of cancer and all the remaining patients' computed tomography (CT) images and contours were aligned to it using deformable image registration (DIR). Deformable vector field (DVF) for each patient i (DVF i-ref ) was used to deform the original 3D dose matrix of that patient. CT scan of a test patient was also registered with the same reference patient using DIR and both direct DVF (DVF test-ref ) and inverse DVF ([Formula: see text]) were derived. Similarity of atlas patients to the test patient was determined based on the similarity of DVF test-ref to atlas DVFs (DVF i-ref ) and appropriate weighting factors were calculated. Patients' doses in the atlas were deformed again using [Formula: see text] to transform them from the reference patient's coordinates to the test patient's coordinates and the final 3D dose distribution for the test patient was predicted by summing the weighted individual 3D dose distributions. Performance of our method was evaluated and the results revealed that the proposed method was able to predict the 3D dose distributions accurately. The mean dose difference between clinical and predicted 3D dose distributions were 0.9  ±  1.1 Gy and 1.9  ±  1.2 Gy for breast and prostate plans. The proposed dose prediction method can be used to improve planning quality and facilitate plan comparisons.

Background: The dosimetric parameters required in lung cancer radiation therapy are taken from a homogeneous water phantom; however, during treatment, the expected results are being affected because of its inhomogeneity. Therefore, it becomes necessary to quantify these deviations., Objective: The present study has been undertaken to find out inter- and intra- lung density variations and its dosimetric impact on lung cancer radiotherapy using Monte Carlo code FLUKA and PBC algorithms., Material and Methods: Density of 100 lungs was recorded from their CT images along with age. Then, after PDD calculated by FLUKA MC Code and PBC algorithm for virtual phantom having density 0.2 gm/cm3 and 0.4 gm/cm3 (density range obtained from CT images of 100 lungs) using Co-60 10 x10 cm2 beams were compared., Results: Average left and right lung densities were 0.275±0.387 and 0.270±0.383 respectively. The deviation in PBC calculated PDD were (+)216%, (+91%), (+)45%, (+)26.88%, (+)14%, (-)1%, (+)2%, (-)0.4%, (-)1%, (+)1%, (+)4%, (+)4.5% for 0.4 gm/cm3 and (+)311%, (+)177%, (+)118%, (+)90.95%, (+)72.23%, (+)55.83% ,(+)38.85%, (+)28.80%, (+)21.79%, (+)15.95%, (+)1.67%, (-) 2.13%, (+)1.27%, (+)0.35%, (-)1.79%, (-)2.75% for 0.2 gm/cm3 density mediums at depths of 1mm, 2mm, 3mm, 4mm, 5mm, 6 mm, 7 mm, 8mm, 9mm,10mm, 15mm, 30mm, 40mm, 50mm, 80mm and 100 mm, respectively., Conclusion: Large variations in inter- and intra- lung density were recorded. PBC overestimated the dose at air/lung interface as well as inside lung. The results of Monte Carlo simulation can be used to assess the performance of other treatment planning systems used in lung cancer radiotherapy.

Purpose: The aim of this work was to evaluate the various computed tomography (CT) techniques such as fast CT, slow CT, breath-hold (BH) CT, full-fan cone beam CT (FF-CBCT), half-fan CBCT (HF-CBCT), and average CT for delineation of internal target volume (ITV). In addition, these ITVs were compared against four-dimensional CT (4DCT) ITVs., Materials and Methods: Three-dimensional target motion was simulated using dynamic thorax phantom with target insert of diameter 3 cm for ten respiration data. CT images were acquired using a commercially available multislice CT scanner, and the CBCT images were acquired using On-Board-Imager. Average CT was generated by averaging 10 phases of 4DCT. ITVs were delineated for each CT by contouring the volume of the target ball; 4DCT ITVs were generated by merging all 10 phases target volumes. Incase of BH-CT, ITV was derived by boolean of CT phases 0%, 50%, and fast CT target volumes., Results: ITVs determined by all CT and CBCT scans were significantly smaller (P < 0.05) than the 4DCT ITV, whereas there was no significant difference between average CT and 4DCT ITVs (P = 0.17). Fast CT had the maximum deviation (-46.1% ± 20.9%) followed by slow CT (-34.3% ± 11.0%) and FF-CBCT scans (-26.3% ± 8.7%). However, HF-CBCT scans (-12.9% ± 4.4%) and BH-CT scans (-11.1% ± 8.5%) resulted in almost similar deviation. On the contrary, average CT had the least deviation (-4.7% ± 9.8%)., Conclusions: When comparing with 4DCT, all the CT techniques underestimated ITV. In the absence of 4DCT, the HF-CBCT target volumes with appropriate margin may be a reasonable approach for defining the ITV.

Objective: The purpose of this work was to evaluate the four-dimensional cone beam CT (4DCBCT) imaging with different gantry rotation speed., Methods: All the 4DCBCT image acquisitions were carried out in Elekta XVI Symmetry™ system (Elekta AB, Stockholm, Sweden). A dynamic thorax phantom with tumour mimicking inserts of diameter 1, 2 and 3 cm was programmed to simulate the respiratory motion (4 s) of the target. 4DCBCT images were acquired with different gantry rotation speeds (36°, 50°, 75°, 100°, 150° and 200° min(-1)). Owing to the technical limitation of 4DCBCT system, average cone beam CT (CBCT) images derived from the 10 phases of 4DCBCT were used for the internal target volume (ITV) contouring. ITVs obtained from average CBCT were compared with the four-dimensional CT (4DCT). In addition, the image quality of 4DCBCT was also evaluated for various gantry rotation speeds using Catphan(®) 600 (The Phantom Laboratory Inc., Salem, NY)., Results: Compared to 4DCT, the average CBCT underestimated the ITV. The ITV deviation increased with increasing gantry speed (-10.8% vs -17.8% for 36° and 200° min(-1) in 3-cm target) and decreasing target size (-17.8% vs -26.8% for target diameter 3 and 1 cm in 200° min(-1)). Similarly, the image quality indicators such as spatial resolution, contrast-to-noise ratio and uniformity also degraded with increasing gantry rotation speed., Conclusion: The impact of gantry rotation speed has to be considered when using 4DCBCT for ITV definition. The phantom study demonstrated that 4DCBCT with slow gantry rotation showed better image quality and less ITV deviation., Advances in Knowledge: Usually, the gantry rotation period of Elekta 4DCBCT system is kept constant at 4 min (50° min(-1)) for acquisition, and any attempt of decreasing/increasing the acquisition duration requires careful investigation. In this study, the 4DCBCT images with different gantry rotation speed were evaluated.

Objective: The aim of this work was to evaluate the quality of kilovoltage (kV) cone beam CT (CBCT) images acquired during arc delivery., Methods: Arc plans were delivered on a Catphan(®) 600 phantom (The Phantom Laboratory Inc., Salem, NY), and kV CBCT images were acquired during the treatment. The megavoltage (MV) scatter effect on kV CBCT image quality was evaluated using parameters such as Hounsfield unit (HU) accuracy, spatial resolution, contrast-to-noise ratio (CNR) and spatial non-uniformity (SNU). These CBCT images were compared with reference scans acquired with the same acquisition parameters without MV "beam on". This evaluation was carried out for different photon beams (6 and 15 MV), arc types (half vs full arc), static field sizes (10 × 10 and 25 × 25 cm(2)) and source-to-imager distances (SID) (150 and 170 cm)., Results and Conclusion: HU accuracy, CNR and SNU were considerably affected by MV scatter, and this effect was increased with increasing field size and decreasing photon energy, whereas the spatial resolution was almost unchanged. The MV scatter effect was observed to be more for full-rotation arc delivery than for half-arc delivery. In addition, increasing the SID resulted in decreased MV scatter effect and improved the image quality., Advances in Knowledge: Nowadays, volumetric modulated arc therapy (VMAT) is increasingly used in clinics, and this arc therapy enables us to acquire CBCT imaging simultaneously. But, the main issue of concurrent imaging is the "MV scatter" effect on CBCT imaging. This study aims to experimentally quantify the effect of MV scatter on CBCT image quality.

Purpose: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviation and estimate planning delivery quality according to the VMAT plans before delivery. Methods: A total of 744 "dynalog" files for 23 VMAT plans were extracted randomly from treatment database. Multi‐correlation was used to analyzed the potential influences on MLC positional accuracy, including the spatial characteristics and temporal variability of VMAT fluence, and the mechanical wear parameters of MLC. We developed a model to forecast the accuracy of MLC moving position utilizing the random forest (RF) ensemble learning method. Spearman correlation was used to further investigate the associations between MLC positional deviation and dosage deviations as well as gamma passing rates. Results: The MLC positional deviation and effective impact factors show a strong multi‐correlation (R = 0.701, p‐value < 0.05). This leads to the development of a highly accurate prediction model with average variables explained of 95.03% and average MSE of 0.059 in the 5‐fold cross‐validation, and MSE of 0.074 for the test data was obtained. The absolute dose deviations caused by MLC positional deviation ranging from 12.948 to 210.235 cGy, while the relative volume deviation remained small at 0.470%–5.161%. The average MLC positional deviation correlated substantially with gamma passing rates (with correlation coefficient of −0.506 to −0.720 and p‐value < 0.05) but marginally with dosage deviations (with correlation coefficient < 0.498 and p‐value > 0.05). Conclusions: The RF predictive model provides a prior tool for VMAT quality assurance. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the capabilities of DMLC to deliver the respiratory motion-synchronized dynamic IMRT (MS-IMRT) treatments under various dose rates. In order to create MS-IMRT plans, the DMLC leaf motions in dynamic IMRT plans of eight lung patients were synchronized with the respiratory motion of breathing period 4 sec and amplitude 2 cm (peak to peak) using an in-house developed leaf position modification program. The MS-IMRT plans were generated for the dose rates of 100 MU/min, 400 MU/min, and 600 MU/min. All the MS-IMRT plans were delivered in a medical linear accelerator, and the fluences were measured using a 2D ion chamber array, placed over a moving platform. The accuracy of MS-IMRT deliveries was evaluated with respect to static deliveries (no compensation for target motion) using gamma test. In addition, the fluences of gated delivery of 30% duty cycle and non- MS-IMRT deliveries were also measured and compared with static deliveries. The MS-IMRT was better in terms of dosimetric accuracy, compared to gated and non-MS-IMRT deliveries. The dosimetric accuracy was observed to be significantly better for 100 MU/min MS-IMRT. However, the use of high-dose rate in a MS-IMRT delivery introduced dose-rate modulation/beam hold-offs that affected the synchronization between the DMLC leaf motion and target motion. This resulted in more dose deviations in MS-IMRT deliveries at the dose rate of 600 MU/min.

The purpose of this study is to analyze the effect of various dose rates (DR) and maximum allowable MLC leaf velocities (MLV) in dynamic Intensity Modulated Radiotherapy (IMRT) planning and delivery of head and neck patients. Five head and neck patients were retrospectively included in this study. The initial dynamic IMRT 'reference plans' were created for all these patients, using a DR of 400 MU/min and MLV of 2.5 cm/s. Additional plans were generated by varying the DR and MLV values. The DR value was varied from 100 to 600 MU/min, in increments of 100 MU/min, for a MLV of 2.5 cm/s. Also the MLV was varied from 0.5 to 3 cm/s, in increments of 0.5 cm, for a DR of 400 MU/min. In order to maintain the prescribed dose to the PTV, the DR was allowed to vary ('beam hold or DR modulation' during delivery) when the MLV was changed and the MLV was allowed to vary when the DR was changed. The mean doses to the PTV as well as parotids, maximum dose of spinal cord and total MU were recorded for analysis. The effect of DR and MLV on treatment delivery was analyzed using the portal dosimetry for all the above plans. The predicted portal dose fluences of the TPS were compared with the measured EPID fluences using gamma evaluation criteria of 2% dose difference and 2 mm distance to agreement. A small proportional increase in OAR doses with DR was observed. Increases to MLV value resulted in decreases of the OAR doses and this effect was considerable for values below 1.5 cm/s. DR and MLV both resulted in no appreciable dose variation to the target. The total MU to deliver the plan increases with increasing DR and decreasing MLV. When comparing portal images derived from the treatment plans with portal images obtained by delivering the treatments, it was observed that the treatments was most reliably delivered when the DRs were set to lower values and when the MLVs were set to higher values.

Purpose: To correlate phase and R2* derived from susceptibility-weighted magnetic resonance imaging (MRI) with computed tomography-Hounsfield (CT-HU) values in calcified neurocysticercosis and to evaluate phase imaging in the assessment of calcified neurocysticercosis., Materials and Methods: Thirty-five patients with 52 calcified lesions underwent both CT and MRI. Phase and R2* were calculated from multi-echo 3D-T2-star-weighted-angiography data. MRI and CT data were coregistered using mutual information. Spearman's correlation was performed between quantitative phase and CT-HU and R2* values. The Mann-Whitney U-test was used to see differences between CT-HU and R2* values from corresponding positive and negative phase regions., Results: The median values of CT-HU and R2* from regions with positive and negative phase were found to be 142.10 (range: 41.89-491.75) and 68.5/sec (range: 20-110/sec) and 137.30 (range: 30.83-458.88) and 69/sec (range: 0-110/sec), respectively. There was a significant correlation of positive phase values with corresponding CT-HU and R2* values. In addition, there was a significant correlation of R2* and CT-HU with negative phase values., Conclusion: We conclude that there is a significant correlation between negative and positive phase with CT-HU and R2* values, suggesting that the CT hyperdense lesion may have both calcium and other minerals, which can be differentiated using phase imaging. Conventional MRI should include phase imaging to detect calcified neurocysticercosis., (Copyright © 2011 Wiley Periodicals, Inc.)

The purpose of this study was to evaluate the dosimetric effect of the leaf width of a multileaf collimator (MLC) in intensity-modulated radiotherapy (IMRT) delivery techniques for small- and large-volume targets. We retrospectively selected previously treated 5 intracranial and 5 head-neck patients for this study to represent small- (range, 18.37-72.75 cc; mean, 42.99 cc) and large-volume (range, 312.31-472.84 cc; mean, 361.14 cc) targets. A 6-MV photon beam data was configured for Brianlab m3 (3 mm), Varian Millennium 120 (5 mm) and Millennium 80 (10 mm) MLCs in the Eclipse treatment-planning system. Sliding window and step-shoot IMRT plans were generated for intracranial patients using all the above-mentioned MLCs; but due to the field size limitation of Brainlab MLC, we used only 5-mm and 10-mm MLCs in the head-and-neck patients. Target conformity, dose to the critical organs and dose to normal tissues were recorded and evaluated. Although the 3-mm MLC resulted in better target conformity (mean difference of 7.7% over 5-mm MLC and 12.7% over 10-mm MLC) over other MLCs for small-volume targets, it increased the total monitor units of the plans. No appreciable differences in terms of target conformity, organ at risk and normal-tissue sparing were observed between the 5-mm and 10-mm MLCs for large-volume targets. The effect of MLC leaf width was not quantifiably different in sliding window and step and shoot techniques. In addition, we observed that there was no additional benefit to the sliding-window (SW) technique when compared to the step-shoot (SS) technique as a result of reduction of MLC leaf width.

Purpose: To propose a straightforward and time‐efficient quality assurance (QA) approach of beam time delay for respiratory‐gated radiotherapy and validate the proposed method on typical respiratory gating systems, Catalyst™ and AlignRT™. Methods: The QA apparatus was composed of a motion platform and a Winston‐Lutz cube phantom (WL3) embedded with metal balls. The apparatus was first scanned in CT‐Sim and two types of QA plans specific for beam on and beam off time delay, respectively, were designed. Static reference images and motion testing images of the WL3 cube were acquired with EPID. By comparing the position differences of the embedded metal balls in the motion and reference images, beam time delays were determined. The proposed approach was validated on three linacs with either Catalyst™ or AlignRT™ respiratory gating systems. To investigate the impact of energy and dose rate on beam time delay, a range of QA plans with Eclipse (V15.7) were devised with varying energy and dose rates. Results: For all energies, the beam on time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 92.13 ±$ \pm $ 5.79 ms, 123.11 ±$ \pm $ 6.44 ms, and 303.44 ±$ \pm $ 4.28 ms, respectively. The beam off time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 121.87 ±$ \pm $ 1.34 ms, 119.33 ±$ \pm $0.75 ms, and 97.69 ±$ \pm $ 2.02 ms, respectively. Furthermore, the beam on delays decreased slightly as dose rates increased for all gating systems, whereas the beam off delays remained unaffected. Conclusions: The validation results demonstrate the proposed QA approach of beam time delay for respiratory‐gated radiotherapy was both reproducible and time‐efficient to practice for institutions to customize accordingly. [ABSTRACT FROM AUTHOR]

Background: This study investigates the impact of varying dose rates on dosimetric parameters and pretreatment quality assurance (QA) accuracy in intensity-modulated radiotherapy (IMRT) planning using anthropomorphic phantoms. The research explores the dosimetric effects of different dose rates ranging from 100 to 600 monitor units per minute (MU/min) on parameters such as Homogeneity Index (HI), Conformity Index (CI), and dose to organs at risk (OAR). Method: Anthropomorphic phantoms, mimicking human tissues, were employed for simulation. Treatment plans were generated using the Eclipse Treatment Planning System, and dosimetric evaluations were conducted using cumulative dose-volume histograms (DVH). Furthermore, pretreatment patient-specific QA was performed using EPID portal dosimetry and Delta4 dosimetry system. Result: Treatment plans adhere to institutional protocol, ensuring the target receives =95% of the prescription dose while meeting OAR constraints. All plans maintain target homogeneity and conformality, keeping maximum doses within the target below 107%. Low dose plans exhibit superior target coverage, conformality, and homogeneity compared to higher doses. However, increased dose rates elevate delivered Monitor Units and maximum doses to critical structures. Gamma passing rates vary with dose rates, with the lowest at 100 MU/min and the highest at 400 MU/min for 1% 1mm criteria. Dosimetric evaluations using Delta 4 and EPID QA methods confirm plan validity across different dose rates. Conclusion: Low dose rate dosimetry is superior to higher rates for target and organ-at-risk (OAR) delivery, albeit prolonging delivery time and affecting internal organ motion. Portal dosimetry offers a faster, more convenient tool for IMRT pretreatment quality assurance (QA). Optimal results are achieved at 400 MU/min, enhancing gamma agreement between calculated and measured portal doses for complex fields. This improvement in QA enhances IMRT treatment delivery quality. However, patient-specific QA using the DELTA4 dosimetry system and ICRU 83 recommendations are insufficient for discussing dosimetric differences relevant to patient treatment. [ABSTRACT FROM AUTHOR]

Background: Electronic portal imaging devices (EPIDs) are applied to measure the dose and verify patients' position. Objective: The present study aims to evaluate the performance of EPID for measuring dosimetric parameters in small photon fields. Material and Methods: In this experimental study, the output factors and beam profiles were obtained using the amorphous silicon (a-Si) EPID for square field sizes ranging from 1×1 to 10×10 cm² at energies 6 and 18 mega-voltage (MV). For comparison, the dosimetric parameters were measured with the pinpoint, diode, and Semiflex dosimeters. Additionally, the Monaco treatment planning system was selected to calculate the output factors and beam profiles. Results: There was a significant difference between the output factors measured using the EPID and that measured with the other dosimeters for field sizes lower than 8×8 cm². In the energy of 6 MV, the gamma passing rates (3%/3 mm) between EPID and diode profile were 98%, 98%, 95%, 94%, 93%, and 94% for 1×1, 2×2, 3×3, 4×4, 5×5, and 10×10 cm², respectively. The measured penumbra width with EPID was higher compared to that measured by the diode dosimeter for both energies. Conclusion: The EPID can measure the dosimetric parameters in small photon fields, especially for beam profiles and penumbra measurements. [ABSTRACT FROM AUTHOR]

Purpose: To create and evaluate a three‐dimensional (3D) Prompt‐nnUnet module that utilizes the prompts‐based model combined with 3D nnUnet for producing the rapid and consistent autosegmentation of high‐risk clinical target volume (HR CTV) and organ at risk (OAR) in high‐dose‐rate brachytherapy (HDR BT) for patients with postoperative endometrial carcinoma (EC). Methods and materials: On two experimental batches, a total of 321 computed tomography (CT) scans were obtained for HR CTV segmentation from 321 patients with EC, and 125 CT scans for OARs segmentation from 125 patients. The numbers of training/validation/test were 257/32/32 and 87/13/25 for HR CTV and OARs respectively. A novel comparison of the deep learning neural network 3D Prompt‐nnUnet and 3D nnUnet was applied for HR CTV and OARs segmentation. Three‐fold cross validation and several quantitative metrics were employed, including Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of Hausdorff distance (HD95%), and intersection over union (IoU). Results: The Prompt‐nnUnet included two forms of parameters Predict‐Prompt (PP) and Label‐Prompt (LP), with the LP performing most similarly to the experienced radiation oncologist and outperforming the less experienced ones. During the testing phase, the mean DSC values for the LP were 0.96 ± 0.02, 0.91 ± 0.02, and 0.83 ± 0.07 for HR CTV, rectum and urethra, respectively. The mean HD values (mm) were 2.73 ± 0.95, 8.18 ± 4.84, and 2.11 ± 0.50, respectively. The mean HD95% values (mm) were 1.66 ± 1.11, 3.07 ± 0.94, and 1.35 ± 0.55, respectively. The mean IoUs were 0.92 ± 0.04, 0.84 ± 0.03, and 0.71 ± 0.09, respectively. A delineation time < 2.35 s per structure in the new model was observed, which was available to save clinician time. Conclusion: The Prompt‐nnUnet architecture, particularly the LP, was highly consistent with ground truth (GT) in HR CTV or OAR autosegmentation, reducing interobserver variability and shortening treatment time. [ABSTRACT FROM AUTHOR]

Background: A slice of heart, especially the region of left anterior descending coronary vessel (LAD), comes into the beam trajectory when irradiating left sided breast cancers. This may have long term cardiac implications. To keep the heart away from the bi-tangential beams while the radiation beam is on - (DIBH) technique is practiced. We plan to compare this technique with the standard practice of free breathing . Material and Methods Left sided breast cancer patients after BCS or post mastectomy were enrolled as per our institutional DIBH protocol. We have analysed the dosimetric comparision of cardiac and LAD doses and efficiency of the process. Results: We have trained left sided breast cancer patients of age< 65years of age . Seven patients were trained for the procedure and out of these 3 patients underwent treatment according to DIBH technique. Among remaining 4 patients 2 were unable to hold their breath inspite of 3 training sessions, in one on planning scan heart was already out of tangential trajectory in free breathing and in one no dosimetric benefit was observed on plan. Therefore DIBH technique was abandoned in these 4 cases. Training time as an OPD exercise was 15 minutes, to capture free breathing and breath hold scan was 45 minutes. Time taken to plan by the physicist was 30 minutes. First day treatment setup and treatment time was45 minute and rest of days it was 25 minutes. Average of mean dose received by heart in free breathing versus breath hold was 5.5Gy versus 2.5Gy and mean LAD dose received was 33Gy versus 22Gy. Conclusion: Radiotherapy of the left breast in DIBH can be incorporated into daily routine. Although time taken by DIBH technique is more than usual routine patients but is associated with significant dose reduction to the heart and LAD. [ABSTRACT FROM AUTHOR]

Background: The field of artificial intelligence (AI)-based patient-specific quality assurance (PSQA) for volumetric modulated arc therapy (VMAT) faces challenges in terms of developing general models across institutions due to the prevalence of multi-institution data collection and multivariate heterogeneity. Building a general model that is capable of handling diverse multi-institution data is critical for enabling large-scale integration and analysis., Purpose: This study aims to develop a star generative adversarial network (StarGAN) and transformer-based hybrid classification-regression PSQA framework to address unification of heterogeneous data from different institutions., Methods: A StarGAN and transformer-based hybrid classification-regression model was developed as a general PSQA framework to predict gamma passing rates (GPRs) and classify quality assurance (QA) results as "Pass" or "Fail" at multiple institutions. A total of 1815 VMAT plans were collected from eight institutions to develop the general PSQA framework and perform clinical commissioning and implementation. Among them, 20 independent clinical plans from each of eight institutions, for a total of 160 plans, were used for the clinical commissioning, and 205 new clinical plans from eight institutions were used for clinical implementation., Results: For the 3%/3, 3%/2, and 2%/2 mm gamma criteria, the sensitivity of the proposed PSQA framework with pretraining was 90.13%, 92.03%, and 95.84%, respectively, while the specificity was 76.01%, 76.12%, and 85.34%, respectively. The mean absolute errors (MAEs) of the proposed PSQA framework with pretraining were 1.36%, 2.37%, and 3.96%, respectively, while the root-mean-square errors (RMSEs) were 2.31%, 3.89%, and 5.17%, respectively. The results demonstrated visible improvement at multiple institutions. For clinical commissioning, the deviations between the predicted and measured results were all within 3% for 3%/3 and 3%/2 mm at eight institutions. For clinical implementation, all failure plans were correctly identified by the proposed PSQA framework., Conclusions: The general PSQA framework enables diverse clinical data sources to be handled to achieve enhanced model performance and generalizability, and provides a solution to the unification of heterogeneous data from different institutions to construct robust QA models. This approach can be clinically deployed for VMAT QA., (© 2024 American Association of Physicists in Medicine.)

Radiotherapy (RT) for lung or liver tumors can be challenging due to respiration-induced organ motion (RIOM). There are some methodological solutions to minimize RIOM. We explored a new approach to evaluate the feasibility and reproducibility of RIOM during RT with five total client-owned tumor-bearing animals using a remote-triggered breath-hold ventilator under general anesthesia during image acquisition and RT. There was one stereotactic body radiotherapy, one conventionally fractionated definitive intent, and three conventionally fractionated palliative intent RT cases. Based on repeated cone beam CT, there were no treatment table shifts required prior to initiating beam on. No clinically significant complications such as hypotension occurred during anesthesia. This technique appeared to be safe in this group of patients and was easily clinically implemented and highly reproducible. More complete follow-up data and larger studies are needed to evaluate clinical outcomes with this breath-hold ventilator technique in veterinary RT., (© 2024 The Author(s). Veterinary Radiology & Ultrasound published by Wiley Periodicals LLC on behalf of American College of Veterinary Radiology.)

Background: This study aims to validate the effectiveness of linear regression for motion prediction of internal organs or tumors on 2D cine-MR and to present an online gating signal prediction scheme that can improve the accuracy of MR-guided radiotherapy for liver and lung cancer. Materials and methods: We collected 2D cine-MR sequences of 21 liver cancer patients and 10 lung cancer patients to develop a binary gating signal prediction algorithm that forecasts the crossing-time of tumor motion traces relative to the target threshold. Both 0.4 s and 0.6 s prediction windows were tested using three linear predictors and three recurrent neural networks (RNNs), given the system delay of 0.5 s. Furthermore, an adaptive linear regression model was evaluated using only the first 30 s as the burn-in period, during which the model parameters were adapted during the online prediction process. The accuracy of the predicted traces was measured using amplitude metrics (MAE, RMSE, and R2), and in addition, we proposed three temporal metrics, namely crossing error, gating error, and gating accuracy, which are more relevant to the nature of the gating signals. Results: In both 0.6 s and 0.4 s prediction cases, linear regression outperformed other methods, demonstrating significantly smaller amplitude errors compared to the RNNs (P < 0.05). The proposed algorithm with adaptive linear regression had the best performance with an average gating accuracy of 98.3% and 98.0%, a gating error of 44 ms and 45 ms, for liver cancer and lung cancer patients, respectively. Conclusion: A functional online gating control scheme was developed with an adaptive linear regression that is both more cost-efficient and accurate than sophisticated RNN based methods in all studied metrics. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to compare the segmentation performances of the 2D, 2.5D, and 3D networks for maxillary sinuses (MSs) and lesions inside the maxillary sinus (MSL) with variations in sizes, shapes, and locations in cone beam CT (CBCT) images under the same constraint of memory capacity. Methods: The 2D, 2.5D, and 3D networks were compared comprehensively for the segmentation of the MS and MSL in CBCT images under the same constraint of memory capacity. MSLs were obtained by subtracting the prediction of the air region of the maxillary sinus (MSA) from that of the MS. Results: The 2.5D network showed the highest segmentation performances for the MS and MSA compared to the 2D and 3D networks. The performances of the Jaccard coefficient, Dice similarity coefficient, precision, and recall by the 2.5D network of U-net + + reached 0.947, 0.973, 0.974, and 0.971 for the MS, respectively, and 0.787, 0.875, 0.897, and 0.858 for the MSL, respectively. Conclusions: The 2.5D segmentation network demonstrated superior segmentation performance for various MSLs with an ensemble learning approach of combining the predictions from three orthogonal planes. [ABSTRACT FROM AUTHOR]

Background: Real‐time liver imaging is challenged by the short imaging time (within hundreds of milliseconds) to meet the temporal constraint posted by rapid patient breathing, resulting in extreme under‐sampling for desired 3D imaging. Deep learning (DL)‐based real‐time imaging/motion estimation techniques are emerging as promising solutions, which can use a single X‐ray projection to estimate 3D moving liver volumes by solved deformable motion. However, such techniques were mostly developed for a specific, fixed X‐ray projection angle, thereby impractical to verify and guide arc‐based radiotherapy with continuous gantry rotation. Purpose: To enable deformable motion estimation and 3D liver imaging from individual X‐ray projections acquired at arbitrary X‐ray scan angles, and to further improve the accuracy of single X‐ray‐driven motion estimation. Methods: We developed a DL‐based method, X360, to estimate the deformable motion of the liver boundary using an X‐ray projection acquired at an arbitrary gantry angle (angle‐agnostic). X360 incorporated patient‐specific prior information from planning 4D‐CTs to address the under‐sampling issue, and adopted a deformation‐driven approach to deform a prior liver surface mesh to new meshes that reflect real‐time motion. The liver mesh motion is solved via motion‐related image features encoded in the arbitrary‐angle X‐ray projection, and through a sequential combination of rigid and deformable registration modules. To achieve the angle agnosticism, a geometry‐informed X‐ray feature pooling layer was developed to allow X360 to extract angle‐dependent image features for motion estimation. As a liver boundary motion solver, X360 was also combined with priorly‐developed, DL‐based optical surface imaging and biomechanical modeling techniques for intra‐liver motion estimation and tumor localization. Results: With geometry‐aware feature pooling, X360 can solve the liver boundary motion from an arbitrary‐angle X‐ray projection. Evaluated on a set of 10 liver patient cases, the mean (± s.d.) 95‐percentile Hausdorff distance between the solved liver boundary and the "ground‐truth" decreased from 10.9 (±4.5) mm (before motion estimation) to 5.5 (±1.9) mm (X360). When X360 was further integrated with surface imaging and biomechanical modeling for liver tumor localization, the mean (± s.d.) center‐of‐mass localization error of the liver tumors decreased from 9.4 (± 5.1) mm to 2.2 (± 1.7) mm. Conclusion: X360 can achieve fast and robust liver boundary motion estimation from arbitrary‐angle X‐ray projections for real‐time imaging guidance. Serving as a surface motion solver, X360 can be integrated into a combined framework to achieve accurate, real‐time, and marker‐less liver tumor localization. [ABSTRACT FROM AUTHOR]

Background: Modern radiotherapeutic techniques, such as intensity-modulated radiation therapy or stereotactic body radiotherapy, require high-dose delivery precision. However, the precise localization of tumors during patient respiration remains a challenge. Therefore, it is essential to investigate effective methods for monitoring respiration to minimize potential complications. Despite several systems currently in clinical use, there are drawbacks, including the complexity of the setup, the discomfort to the patient, and the high cost., Purpose: This study investigated the feasibility of using a novel pressure sensor array (PSA) as a tool to monitor respiration during radiotherapy treatments. The PSA was positioned between the treatment couch and the back of the patient lying on it and was intended to overcome some limitations of current methods. The main objectives included assessing the PSA's capability in monitoring respiratory behavior and to investigate prospective applications that extend beyond respiratory monitoring., Methods: A PSA with 31 pressure-sensing elements was used in 12 volunteers. The participants were instructed to breathe naturally while lying on a couch without any audio or visual guidance. The performance of the PSA was compared to that of a camera-based respiratory monitoring system (RPM, Varian, USA), which served as a reference. Several metrics, including pressure distribution, weight sensitivity, and correlations between PSA and RPM signals, were analyzed. The PSA's capacity to provide information on potential applications related to patient stability was also investigated., Results: The linear relationship between the weight applied to the PSA and its output was demonstrated in this study, confirming its sensitivity to pressure changes. A comparison of PSA and RPM curves revealed a high correlation coefficient of 0.9391 on average, indicating consistent respiratory cycles. The PSA also effectively measured the weight distribution at the volunteer's back in real-time, which allows for monitoring the patient's movements during the radiotherapy., Conclusion: PSA is a promising candidate for effective respiratory monitoring during radiotherapy treatments. Its performance is comparable to the established RPM system, and its additional capabilities suggest its multifaceted utility. This paper shows the potential use of PSA for patient monitoring in radiotherapy and suggests possibilities for further research, including performance comparisons with other existing systems and real-patient applications with respiratory training., (© 2024 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: The high‐dose rate (HDR) brachytherapy treatment planning workflow for cervical cancer is a labor‐intensive, time‐consuming, and expertise‐driven process. These issues are amplified in low/middle‐income countries with large deficits in experienced healthcare professionals. Automation has the ability to substantially reduce bottlenecks in the planning process but often require a high level of expertise to develop. Purpose: To implement the out of the box self‐configuring nnU‐Net package for the auto‐segmentation of the organs at risk (OARs) and high‐risk CTV (HR CTV) for Ring‐Tandem (R‐T) HDR cervical brachytherapy treatment planning. Methods: The computed tomography (CT) scans of 100 previously treated patients were used to train and test three different nnU‐Net configurations (2D, 3DFR, and 3DCasc). The performance of the models was evaluated by calculating the Sørensen‐dice similarity coefficient, Hausdorff distance (HD), 95th percentile Hausdorff distance, mean surface distance (MSD), and precision score for 20 test patients. The dosimetric accuracy between the manual and predicted contours was assessed by looking at the various dose volume histogram (DVH) parameters and volume differences. Three different radiation oncologists (ROs) scored the predicted bladder, rectum, and HR CTV contours generated by the best performing model. The manual contouring, prediction, and editing times were recorded. Results: The mean DSC, HD, HD95, MSD and precision scores for our best performing model (3DFR) were 0.92/7.5 mm/3.0 mm/ 0.8 mm/0.91 for the bladder, 0.84/13.8 mm/5.3 mm/1.4 mm/0.84 for the rectum, and 0.81/8.5 mm/6.0 mm/2.2 mm/0.80 for the HR CTV. Mean dose differences (D2cc/90%) and volume differences were 0.08 Gy/1.3 cm3 for the bladder, 0.02 Gy/0.7 cm3 for the rectum, and 0.33 Gy/1.5 cm3 for the HR CTV. On average, 65% of the generated contours were clinically acceptable, 33% requiring minor edits, 2% required major edits, and no contours were rejected. Average manual contouring time was 14.0 min, while the average prediction and editing times were 1.6 and 2.1 min, respectively. Conclusion: Our best performing model (3DFR) provided fast accurate auto generated OARs and HR CTV contours with a large clinical acceptance rate. [ABSTRACT FROM AUTHOR]

Background: In respiratory gated radiotherapy, low latency between target motion into and out of the gating window and actual beam‐on and beam‐off is crucial for the treatment accuracy. However, there is presently a lack of guidelines and accurate methods for gating latency measurements. Purpose: To develop a simple and reliable method for gating latency measurements that work across different radiotherapy platforms. Methods: Gating latencies were measured at a Varian ProBeam (protons, RPM gating system) and TrueBeam (photons, TrueBeam gating system) accelerator. A motion‐stage performed 1 cm vertical sinusoidal motion of a marker block that was optically tracked by the gating system. An amplitude gating window was set to cover the posterior half of the motion (0–0.5 cm). Gated beams were delivered to a 5 mm cubic scintillating ZnSe:O crystal that emitted visible light when irradiated, thereby directly showing when the beam was on. During gated beam delivery, a video camera acquired images at 120 Hz of the moving marker block and light‐emitting crystal. After treatment, the block position and crystal light intensity were determined in all video frames. Two methods were used to determine the gate‐on (τon) and gate‐off (τoff) latencies. By method 1, the video was synchronized with gating log files by temporal alignment of the same block motion recorded in both the video and the log files. τon was defined as the time from the block entered the gating window (from gating log files) to the actual beam‐on as detected by the crystal light. Similarly, τoff was the time from the block exited the gating window to beam‐off. By method 2, τon and τoff were found from the videos alone using motion of different sine periods (1–10 s). In each video, a sinusoidal fit of the block motion provided the times Tmin of the lowest block position. The mid‐time, Tmid‐light, of each beam‐on period was determined as the time halfway between crystal light signal start and end. It can be shown that the directly measurable quantity Tmid‐light − Tmin = (τoff+τon)/2, which provided the sum (τoff+τon) of the two latencies. It can also be shown that the beam‐on (i.e., crystal light) duration ΔTlight increases linearly with the sine period and depends on τoff − τon: ΔTlight = constant•period+(τoff − τon). Hence, a linear fit of ΔTlight as a function of the period provided the difference of the two latencies. From the sum (τoff+τon) and difference (τoff − τon), the individual latencies were determined. Results: Method 1 resulted in mean (±SD) latencies of τon = 255 ± 33 ms, τoff = 82 ± 15 ms for the ProBeam and τon = 84 ± 13 ms, τoff = 44 ± 11 ms for the TrueBeam. Method 2 resulted in latencies of τon = 255 ± 23 ms, τoff = 95 ± 23 ms for the ProBeam and τon = 83 ± 8 ms, τoff = 46 ± 8 ms for the TrueBeam. Hence, the mean latencies determined by the two methods agreed within 13 ms for the ProBeam and within 2 ms for the TrueBeam. Conclusions: A novel, simple and low‐cost method for gating latency measurements that work across different radiotherapy platforms was demonstrated. Only the TrueBeam fully fulfilled the AAPM TG‐142 recommendation of maximum 100 ms latencies. [ABSTRACT FROM AUTHOR]

Background: Segmentation of the Gross Tumor Volume (GTV) is a crucial step in the brachytherapy (BT) treatment planning workflow. Currently, radiation oncologists segment the GTV manually, which is time-consuming. The time pressure is particularly critical for BT because during the segmentation process the patient waits immobilized in bed with the applicator in place. Automatic segmentation algorithms can potentially reduce both the clinical workload and the patient burden. Although deep learning based automatic segmentation algorithms have been extensively developed for organs at risk, automatic segmentation of the targets is less common. The aim of this study was to automatically segment the cervical cancer GTV on BT MRI images using a state-of-the-art automatic segmentation framework and assess its performance. Methods: A cohort of 195 cervical cancer patients treated between August 2012 and December 2021 was retrospectively collected. A total of 524 separate BT fractions were included and the axial T2-weighted (T2w) MRI sequence was used for this project. The 3D nnU-Net was used as the automatic segmentation framework. The automatic segmentations were compared with the manual segmentations used for clinical practice with Sørensen–Dice coefficient (Dice), 95th Hausdorff distance (95th HD) and mean surface distance (MSD). The dosimetric impact was defined as the difference in D98 (ΔD98) and D90 (ΔD90) between the manual segmentations and the automatic segmentations, evaluated using the clinical dose distribution. The performance of the network was also compared separately depending on FIGO stage and on GTV volume. Results: The network achieved a median Dice of 0.73 (interquartile range (IQR) = 0.50–0.80), median 95th HD of 6.8 mm (IQR = 4.2–12.5 mm) and median MSD of 1.4 mm (IQR = 0.90–2.8 mm). The median ΔD90 and ΔD98 were 0.18 Gy (IQR = -1.38–1.19 Gy) and 0.20 Gy (IQR =-1.10–0.95 Gy) respectively. No significant differences in geometric or dosimetric performance were observed between tumors with different FIGO stages, however significantly improved Dice and dosimetric performance was found for larger tumors. Conclusions: The nnU-Net framework achieved state-of-the-art performance in the segmentation of the cervical cancer GTV on BT MRI images. Reasonable median performance was achieved geometrically and dosimetrically but with high variability among patients. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to investigate the precision of deep learning (DL)-based auto-reconstruction in localizing interstitial needles in post-operative cervical cancer brachytherapy (BT) using three-dimensional (3D) computed tomography (CT) images. Material and methods: A convolutional neural network (CNN) was developed and presented for automatic reconstruction of interstitial needles. Data of 70 post-operative cervical cancer patients who received CT-based BT were used to train and test this DL model. All patients were treated with three metallic needles. Dice similarity coefficient (DSC), 95% Hausdorff distance (95% HD), and Jaccard coefficient (JC) were applied to evaluate the geometric accuracy of auto-reconstruction for each needle. Dose-volume indexes (DVI) between manual and automatic methods were used to analyze the dosimetric difference. Correlation between geometric metrics and dosimetric difference was evaluated using Spearman correlation analysis. Results: The mean DSC values of DL-based model were 0.88, 0.89, and 0.90 for three metallic needles. Wilcoxon signed-rank test indicated no significant dosimetric differences in all BT planning structures between manual and automatic reconstruction methods (p > 0.05). Spearman correlation analysis demonstrated weak link between geometric metrics and dosimetry differences. Conclusions: DL-based reconstruction method can be used to precisely localize the interstitial needles in 3D-CT images. The proposed automatic approach could improve the consistency of treatment planning for post-operative cervical cancer brachytherapy. [ABSTRACT FROM AUTHOR]

Precision radiotherapy needs to manage organ movements to prevent critical organ injury. The purpose of this study is to examine the feasibility of motion control of the lung by suppressing respiratory motion. The non-invasive high frequency oscillatory ventilation (NIHFOV) is a technique commonly used in the protection of lung for patients with acute lung disease. By using a very high respiratory frequency and a low tidal volume, NIHFOV allows gas exchange, maintains a constant mean airway pressure and minimizes the respiratory movements. We tested healthy volunteers NIHFOV to explore the optimal operational parameter setting and the best possible motion suppression achievable. This study was conducted with the approval of Institutional Review Boards of the Wuwei Cancer hospital (approval number: 2021-39) and carried out in accordance with Declaration of Helsinki. The study comprises two parts. Twenty three healthy volunteers participated in the first part of the study. They had 7 sessions of training with the NIHFOV. The duration of uninterrupted, continuous breathing under the NIHFOV and the optimal operational machine settings were defined. Eight healthy volunteers took part in the second part of the study and underwent 4-dimensional CT (4DCT) scanning with and without NIHFOV. Their respiratory waveform under free breathing (FB) and NIHFOV were recorded. The maximum range of motion of the diaphragm from the two scannings was compared, and the variation of bilateral lung volume was obtained to evaluate the impact of NIHFOV technique on lung volume. The following data were collected: comfort score, transcutaneous partial pressure of oxygen (PtcO2), transcutaneous partial pressure of carbon dioxide (PtcCO2), and pulse rate. Data with and without NIHFOV were compared to evaluate its safety, physiological impacts and effect of lung movement suppression. All the volunteers completed the training sessions eventlessly, demonstrating a good tolerability of the procedure. The median NIHFOV-on time was 32 min (22–45 min), and the maximum range of motion in the cephalic-caudal direction was significantly reduced on NIHFOV compared with FB (1.8 ± 0.8 cm vs 0.3 ± 0.1 cm, t = − 3.650, P = 0.003); the median range of motion was only 0.3 ± 0.1 cm on NIHFOV with a good reproducibility. The variation coefficient under NIHFOV of the right lung volume was 2.4% and the left lung volume was 9.2%. The PtcO2 and PtcCO2 were constantly monitored during NIHFOV. The medium PtcCO2 under NIHFOV increased lightly by 4.1 mmHg (interquartile range [IQR], 4–6 mmHg) compared with FB (t = 17.676, P < 0.001). No hypercapnia was found, PtcO2 increased significantly in all volunteers during NIHFOV (t = 25.453, P < 0.001). There was no significant difference in pulse rate between the two data sets (t = 1.257, P = 0.233). NIHFOV is easy to master in healthy volunteers to minimize respiratory movement with good tolerability and reproducibility. It is a feasible approach for lung motion control and could potentially be applied in accurate radiotherapy including carbon-ion radiotherapy through suppression of respiratory movement. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to investigate the target dose discrepancy caused by intrafraction variation during stereotactic body radiotherapy (SBRT) for lung cancer. Methods: Intensity-modulated radiation therapy (IMRT) plans were designed based on average computed tomography (AVG CT) utilizing the planning target volume (PTV) surrounding the 65% and 85% prescription isodoses in both phantom and patient cases. Variation was simulated by shifting the nominal plan isocenter along six directions from 0.5 mm to 4.5 mm with a 1-mm step size to produce a series of perturbed plans. The dose discrepancy between the initial plan and the perturbed plans was calculated as the percentage of the initial plan. Dose indices, including ΔD99 for internal target volume (ITV) and gross tumor volume (GTV), were adopted as endpoint samples. The mean dose discrepancy was calculated under the 3-dimensional space distribution. Results: We found that motion can lead to serious dose degradation of the target and ITV in lung SBRT, especially during SBRT with PTV surrounding the lower isodose line. Lower isodose line may lead to larger dose discrepancy, while make steeper dose fall-off gradient. This phenomenon was compromised when 3-dimensional space distribution was considered. Discussion: This result may provide a prospective reference for target dose degradation due to motion during lung SBRT treatment. [ABSTRACT FROM AUTHOR]