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While copepods are important prey for many wild marine fish larvae and are preferred as a live feed for culturing larval fish, their low productivity and the costs associated with culturing them typically limit their use as a live food source in all but experimental situations. In a series of experiments with four readily and commercially available microalgae (Thalassiosira weissflogii Grunow, Rhodomonas salina Wislouch, Tetraselmis suecica Kylin Butch, and Isochrysis galbana Parke), we determine which of them, when exclusively fed to a commonly occurring copepod, Oithona oculata, over a 15-day period, results in the greatest copepod survival and egg production rates. We posit that species of Oithona might be an ideal candidate for copepod taxa for commercial-scale copepod culture and that the microalga Rhodomonas salina is an ideal food source to culture them.

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.

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.

PurposeCarbon-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 methodsWe 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.ResultsAnalysis 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.ConclusionFor 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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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

To clarify temporal variation of in situ egg production of the copepods, Acartia japonica Mori, 1940, in Sagami Bay, central Japan, the species’ abundance, egg morphology and hatching patterns were studied during 2015. Adults of A. japonica were observed in the water column from June to October. Results showed that adults reared in the laboratory produced three types of eggs: subitaneous, delayed-hatching and diapause. All egg types had a surface covered with short spines; diapause eggs had a thicker chorion than subitaneous eggs. During the early period of the species occurrence in the bay, most females produced only subitaneous eggs, which hatched within 72 h. From the end of September, a large proportion of females began to produce diapause eggs. Linear regression analysis showed that the ratio of diapause eggs was negatively correlated with day length and water temperature (P

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

世界の水産物生産量は、1990 年代に漁獲生産が頭打ちとなり、養殖生産によってその後の右肩上がりの需要を賄っている。天然域からの種苗採集に依存しない人工種苗生産においては、仔稚魚が要求する栄養に富む小型動物プランクトンを生産できないことが大きな障壁となっている。海産浮遊性カイアシ類はワムシやアルテミアといった既存の生物餌料よりも高い栄養価を示し、仔稚魚の成長、生残、市場価値を向上させることから、理想的な生物餌料と認識され、水産分野への利用が期待されている。しかし、人工環境下でのカイアシ類の生存率と卵生産は低く、加えて不安定なためにカイアシ類の大量培養は未だ困難とされている。水産餌料として利用する場合、カイアシ類は高個体密度環境で培養されるため、成体による卵の捕食 (共食い) が発生し、槽内で生産された卵の実に30%が共食いによって損失される。そのためカイアシ類の水産分野での利用には共食い防止技術の確立が喫緊の課題である。相模湾真鶴港に優占する浮遊性カイアシ類Acartia steueri とA. japonica を培養候補種とし現場調査を行ったところ、A. steueri の出現個体密度は高く、水温範囲は12-28oC と広く、本邦の魚類種苗生産の水温帯に合致した。そこで本博士研究の目的を、浮遊性カイアシ類A. steueri の集約的な培養技術の確立とし、(1)A. steueri 発達段階前期 (幼生) から後期 (幼体)、成体の好適餌料の検討、(2)培養個体密度がA. steueri の卵生産と代謝速度に与える影響の解明、(3)実験室規模での回分式カイアシ類培養槽の運転と生産性の評価を行った。本論文は5 章から構成されている。第1 章は総合序論として、海産浮遊性カイアシ類の初期生物餌料としての有用性と、その集約的培養において生産性を損なう要因である“成体による卵・幼生の捕食”（共食い）を問題提起し、その解決方法として成体培養槽と幼生・幼体培養槽からなる2 槽式培養法を提案した。第2 章ではA. steueri を複数の餌料藻類条件で培養し、幼生から成体までの生存率と、成体の卵生産速度を測定し、それぞれの好適餌料を検討した。幼生から成体における生存率は、緑藻Tetraselmis suecica と珪藻Chaetoceros gracilis からなる混合餌料を与えた条件で最大化されたため好適餌料として選択した。成体の卵生産は、緑藻T. suecica と珪藻Thalassiosira weissflogii からなる混合餌料を与えた条件で最大化されたため、好適餌料として選択した。これらの餌料検討の結果、幼生から成体までの生存率が20%と高効率な継代培養に成功した。A. steueri を高個体密度で培養した際の生理状態を理解し、適切な培養個体密度を検討するため第3 章では、本種成体を異なる個体密度条件で培養し、生存率、卵生産速度、代謝速度を測定した。生存率と卵生産速度のいずれにおいても、各個体密度条件間で有意な差は認められなかったが、呼吸速度は高個体密度条件で有意に減少した。これらの結果より、本種の生存率と卵生産速度は個体密度によって変化せず、高個体密度条件では代謝速度を減少させ卵生産速度を維持したため、高密度培養に適した生物学的特徴を有していると考えられた。第4 章では、共食い防止機構を備えた成体培養槽を作成後、第2、3 章で決定した好適餌料と培養個体密度を用いた20 日間の運転を実施し、その生産性を評価した。その結果、既往研究の1.6 倍の生産性を達成したため、大型化に向けての方途を見出した。第5 章では総合考察として、本研究手法による大型生産システムを想定した試算を行うとともに、本手法の他カイアシ類種への応用と実利用に向けた今後の課題を論じた。