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

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

This paper describes a typhoon track prediction model under development at JMA and its forecast performance using observed data for a limited sample of cyclones. The experimental model uses primitive equations in * coordinate. The model utilizes a limited area grid and is nested in a one-way sense to the forecasts of a hemispheric model. It has a uniform grid system with a horizontal resolution of about 50km and 8 vertical levels and covers the area of 4000km*4000km.The cumulus convective process is parameterized using Kuo's framework. The numerical schemes are specially tuned to realistically maintain the model typhoon. The initial condition consists of a well-formed model typhoon superposed upon a large-scale objective analysis.This model predicts the central pressures and movements of several typhoons observed in 1985 with a fairly good skill. In addition, this model simulates a complex rainfall distribution under the existence of a strong interaction between a typhoon and Baiu front quite reasonably as compared with satellite cloud pictures.Some impact studies suggest that particular attention should be paid for the accuracy of the manual analyses and the performance of the hemispheric model (background model), because they have large effects on the track performance of this model.

The photoresponsive copolymer microspheres [poly(MAIP-co-MMA)] were prepared from the emulsifier-free emulsion copolymerization of 2-[2-(methacryloyloxy) ethyldimethylammonio]-ethyl indolinonaphthooxazine phosphate (MAIP) and methyl methacrylate (MMA). From the kinetics of the copolymerization of MAIP and MMA, it was found that the initial rate of polymerization of MMA increased by the addition of a small amount of MAIP. From the X-ray photoelectron spectroscopy (XPS) measurements MAIP moiety was found to be located on the surface of a particle. The introduction of a MAIP moiety into poly(MMA) microspheres results in a decrease in the amount of bovine serum albumin (BSA) adsorbed. A photoresponsive adsorption of BSA on poly(MA1P-co-MMA) microspheres was observed with spirooxazine-merocyanine photoisomerization caused by UV irradiation.