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We assessed the effect of beam size on plan robustness for intensity-modulated proton therapy (IMPT) of head and neck cancer (HNC) and compared the plan quality including robustness with that of intensity-modulated radiation therapy (IMRT). IMPT plans were generated for six HNC patients using six beam sizes (air-sigma 3–17 mm at isocenter for a 70–230 MeV) and two optimization methods for planning target volume-based non-robust optimization (NRO) and clinical target volume (CTV)-based robust optimization (RO). Worst-case dosimetric parameters and plan robustness for CTV and organs-at-risk (OARs) were assessed under different scenarios, assuming a ± 1–5 mm setup error and a ± 3% range error. Statistical comparisons of NRO-IMPT, RO-IMPT and IMRT plans were performed. In regard to CTV-D99%, RO-IMPT with smaller beam size was more robust than RO-IMPT with larger beam sizes, whereas NRO-IMPT showed the opposite (P  < 0.05). There was no significant difference in the robustness of the CTV-D99% and CTV-D95% between RO-IMPT and IMRT. The worst-case CTV coverage of IMRT (±5 mm/3%) for all patients was 96.0% ± 1.4% (D99%) and 97.9% ± 0.3% (D95%). For four out of six patients, the worst-case CTV-D95% for RO-IMPT (±1–5 mm/3%) were higher than those for IMRT. Compared with IMRT, RO-IMPT with smaller beam sizes achieved lower worst-case doses to OARs. In HNC treatment, utilizing smaller beam sizes in RO-IMPT improves plan robustness compared to larger beam sizes, achieving comparable target robustness and lower worst-case OARs doses compared to IMRT. [ABSTRACT FROM AUTHOR]

Background: Coincidence of the treatment and imaging isocenter coordinates is required to safely perform small‐margin treatments, such as stereotactic radiosurgery of multiple brain metastases. A comprehensive and direct methodology for verifying concordance of kilovoltage cone‐beam computed tomography (kV‐CBCT) and treatment coordinates using an x‐ray CT‐based polymer gel dosimeter (dGEL) and onboard kV‐CBCT was previously reported. Using this methodology, we tested the ability of a new commercially available x‐ray CT‐based polymer dGEL with a rapid response to provide efficient quality assurance (QA). Purpose: The aim of this study was to evaluate the robustness of the three‐dimensional geometric QA methodology using dGEL. Methods: The dGEL were commercially manufactured. The prescribed dose for each field was determined by visually identifying the 5, 10, and 20 Gy isodose lines. A linear accelerator was used to irradiate the gels with seven non‐coplanar beams. An in‐house analysis program was used to identify the beam axes and treatment isocenter in kV‐CBCT coordinates by processing the pre‐ and post‐irradiation CBCT images. The impact of the radiation dose on the test reproducibility was examined, and the detectability of an intentional geometric error was assessed. Results: The treatment isocenter was within 0.4 mm of the imaging isocenter for all radiation doses. The residual error of the test with the intentional error was within 0.2 mm. The analysis and image quality variations for a single dGEL introduced displacement errors less than 0.3 mm. Conclusions: The test assessed the coincidence of treatment and kV‐CBCT isocenter coordinates and detected errors with high robustness. Even for a 10 Gy dose, the test yielded results comparable with those obtained using higher radiation doses owing to the rapid response of the dGEL dosimeter. [ABSTRACT FROM AUTHOR]

Background: Administration of external radiation therapy via proton therapy systems carries a risk of occasional collisions between the patient's body and gantry, which is increased by the snout placed near the patient for better dose distribution. Although treatment planning software (TPS) can simulate controlled collisions, the computed tomography (CT) data used for treatment planning are insufficient given that collisions can occur outside the CT imaging region. Thus, imaging the three‐dimensional (3D) surface outside the CT range and combining the data with those obtained by CT are essential for avoiding collisions. Purpose: To construct a prototype for 3D surface imaging and an end‐to‐end framework for preventing collisions between the patient's body and the gantry. Methods: We obtained 3D surface data using a light sectioning method (LSM). By installing only cameras in front of the CT, we achieved LSM using the CT couch motion and preinstalled patient‐positioning lasers. The camera image contained both sagittal and coronal lines, which are unnecessary for LSM and were removed by deep learning. We combined LSM 3D surface data and original CT data to create synthetic Digital Imaging and Communications in Medicine (DICOM) data. Subsequently, we compared the TPS snout auto‐optimization using the original CT data with the synthetic DICOM data. Results: The mean positional error for LSM of the arms and head was 0.7 ± 0.8 and 0.8 ± 0.8 mm for axial and sagittal imaging, respectively. The TPS snout auto‐optimization indicated that the original CT data would cause collisions; however, the synthetic DICOM data prevented these collisions. Conclusions: The prototype system's acquisition accuracy for 3D surface data was approximately 1 mm, which was sufficient for the collision simulation. The use of a TPS with collision avoidance can help optimize the snout position using synthetic DICOM data. Our proposed method requires no external software for collision simulation and can be integrated into the clinical workflow to improve treatment planning efficiency. [ABSTRACT FROM AUTHOR]

Byline: Tetsuya. Tomita, Tomonori. Isobe, Yoshinobu. Furuyama, Hideyuki. Takei, Daisuke. Kobayashi, Yutaro. Mori, Toshiyuki. Terunuma, Eisuke. Sato, Hiroshi. Yokota, Takeji. Sakae Purpose: The purpose of this study is to evaluate [...]

This study aims to provide a model that compounds historically proposed ideas regarding cell survival irradiated with X rays or particles. The parameters used in this model have simple meanings and are closely related to cell death-related phenomena. The model is adaptable to a wide range of doses and dose rates and thus can consistently explain previously published cell survival data. The formulas of the model were derived by using five basic ideas: 1. "Poisson's law"; 2. "DNA affected damage"; 3. "repair"; 4. "clustered affected damage"; and 5. "saturation of reparability". The concept of affected damage is close to but not the same as the effect caused by the double-strand break (DSB). The parameters used in the formula are related to seven phenomena: 1. "linear coefficient of radiation dose"; 2. "probability of making affected damage"; 3. "cell-specific repairability", 4. "irreparable damage by adjacent affected damage"; 5. "recovery of temporally changed repairability"; 6. "recovery of simple damage which will make the affected damage"; 7. "cell division". By using the second parameter, this model includes cases where a single hit results in repairable–lethal and double-hit results in repairable–lethal. The fitting performance of the model for the experimental data was evaluated based on the Akaike information criterion, and practical results were obtained for the published experiments irradiated with a wide range of doses (up to several 10 Gy) and dose rates (0.17 Gy/h to 55.8 Gy/h). The direct association of parameters with cell death-related phenomena has made it possible to systematically fit survival data of different cell types and different radiation types by using crossover parameters. [ABSTRACT FROM AUTHOR]

The linac-based neutron source “iBNCT” developed by the Tsukuba team has been shown to generate a large intensity of neutrons. To confirm the applicability of the device to BNCT, several characteristic measurements have been implemented. In a water phantom experiment, when the accelerator was operated with an average current of 1.4 mA, the maximum thermal neutron flux was approximately 7.8 × 108 (cm-2s-1). From estimation of the stability of the linac, the results demonstrate its applicability to actual treatment.

Byline: Daisuke. Kobayashi, Tomonori. Isobe, Kenta. Takada, Yutaro. Mori, Hideyuki. Takei, Hiroaki. Kumada, Satoshi. Kamizawa, Tetsuya. Tomita, Eisuke. Sato, Hiroshi. Yokota, Takeji. Sakae Purpose: The purpose of this study is [...]

Introduction: Despite the development of DOSIRIS™, an eye lens dosimeter, the characteristics of DOSIRIS™ in the area of radiotherapy have not been investigated. The purpose of this study was to evaluate the basic characteristics of the 3‐mm dose equivalent measuring instrument DOSIRIS™ in radiotherapy. Methods: Dose linearity and energy dependence were evaluated for the irradiation system based on the calibration method of the monitor dosimeter. The angle dependence was measured by irradiating from a total of 18 directions. Interdevice variation was repeated three times by simultaneously irradiating five dosimeters. The measurement accuracy was based on the absorbed dose measured by the monitor dosimeter of the radiotherapy equipment. Absorbed doses were converted to 3‐mm dose equivalents and compared with DOSIRIS™ measurements. Results: Dose linearity was evaluated using the determination coefficient (R2) R2 = 0.9998 and 0.9996 at 6 and 10 MV, respectively. For energy dependence, although the therapeutic photons evaluated in this study had higher energies than in the previous studies and had a continuous spectrum, the response was equivalent to 0.2–1.25 MeV, well below the IEC 62387 limits. The maximum error at all angles was 15% (angle of 140°) and the coefficient of variation at all angles was 4.70%, which satisfies the standard of the thermoluminescent dosimeter measuring instrument. Accuracy of measurement was determined in terms of the measurement errors for DOSIRIS™ (3.2% and 4.3% at 6 and 10 MV, respectively,) using the 3‐mm dose equivalent obtained from the theoretical value as a reference. The DOSIRIS™ measurements met the IEC standard which defines the measurement error of ±30% of the irradiance value in IEC 62387. Conclusions: We found that the characteristics of the 3‐mm dose equivalent dosimeter in a high‐energy radiation satisfy the IEC standards and have the same measurement accuracy as diagnostic areas such as Interventional Radiology. [ABSTRACT FROM AUTHOR]

Background: We reported the concept of patient‐specific deep learning (DL) for real‐time markerless tumor segmentation in image‐guided radiotherapy (IGRT). The method was aimed to control the attention of convolutional neural networks (CNNs) by artificial differences in co‐occurrence probability (CoOCP) in training datasets, that is, focusing CNN attention on soft tissues while ignoring bones. However, the effectiveness of this attention‐based data augmentation has not been confirmed by explainable techniques. Furthermore, compared to reasonable ground truths, the feasibility of tumor segmentation in clinical kilovolt (kV) X‐ray fluoroscopic (XF) images has not been confirmed. Purpose: The first aim of this paper was to present evidence that the proposed method provides an explanation and control of DL behavior. The second purpose was to validate the real‐time lung tumor segmentation in clinical kV XF images for IGRT. Methods: This retrospective study included 10 patients with lung cancer. Patient‐specific and XF angle‐specific image pairs comprising digitally reconstructed radiographs (DRRs) and projected‐clinical‐target‐volume (pCTV) images were calculated from four‐dimensional computer tomographic data and treatment planning information. The training datasets were primarily augmented by random overlay (RO) and noise injection (NI): RO aims to differentiate positional CoOCP in soft tissues and bones, and NI aims to make a difference in the frequency of occurrence of local and global image features. The CNNs for each patient‐and‐angle were automatically optimized in the DL training stage to transform the training DRRs into pCTV images. In the inference stage, the trained CNNs transformed the test XF images into pCTV images, thus identifying target positions and shapes. Results: The visual analysis of DL attention heatmaps for a test image demonstrated that our method focused CNN attention on soft tissue and global image features rather than bones and local features. The processing time for each patient‐and‐angle‐specific dataset in the training stage was ∼30 min, whereas that in the inference stage was 8 ms/frame. The estimated three‐dimensional 95 percentile tracking error, Jaccard index, and Hausdorff distance for 10 patients were 1.3–3.9 mm, 0.85–0.94, and 0.6–4.9 mm, respectively. Conclusions: The proposed attention‐based data augmentation with both RO and NI made the CNN behavior more explainable and more controllable. The results obtained demonstrated the feasibility of real‐time markerless lung tumor segmentation in kV XF images for IGRT. [ABSTRACT FROM AUTHOR]