The Catalyst HD (C‐RAD Positioning AB, Uppsala, Sweden) optical surface imaging (OSI) system is able to manage interfractional patient positioning, intrafractional motion monitoring, and non‐contact respiratory gating without x‐ray exposure for radiation therapy. In recent years, a novel high‐precision surface registration algorithm for stereotactic radiosurgery (SRS algorithm) has been released. This study aimed to evaluate the technical performance of the OSI system using rigid phantoms, by comparing the conventional and SRS algorithms. To determine the system’s technical performance, isocenter displacements were calculated by surface image registration via the OSI system using head, thorax, and pelvis rigid phantoms. The reproducibility of positioning was evaluated by the mean value calculated by repeating the registration 10 times, without moving each phantom. The accuracy of positioning was evaluated by the mean value of the residual error, where the 10 offset values given to each phantom were subtracted from the isocenter displacement values. The stability of motion monitoring was evaluated by measuring isocenter drift during 20 min and averaging it over 10 measurements. For the head phantom, all tests were compared with the mask types and algorithms. As a result, for all sites and both algorithms, the reproducibility, accuracy, and stability for translation and rotation were <0.1 mm and <0.1°, <1.0 mm and <1.0°, and <0.1 mm and <0.1°, respectively. In particular, the SRS algorithm had a small absolute error and standard deviation of calculated isocenter displacement, and a significantly higher reproducibility and accuracy than the conventional algorithm (P < 0.01). There was no difference in the stability between the algorithms (P = 0.0280). The SRS algorithm was found to be suitable for the treatment of rigid body sites with less deformation and small area, such as the head and face.

In this study, we investigate price and quality decisions in a duopoly in the presence of firms’ quality positions , which are determined by the quality levels of their existing core products. Into a standard model of vertical differentiation, we incorporate a “repositioning cost” that is proportional to the quality differences between firms’ current and new products. By varying the levels of quality positions, we analyze the impact of this cost on the equilibrium outcomes. Our results show that the presence of repositioning costs restricts firms’ abilities to improve profitability and differentiate themselves vertically. As a result, a high‐positioned firm does not necessarily have a competitive advantage over a low‐positioned firm, even if the former offers a superior new product in equilibrium. In addition, if a low‐positioned firm is significantly cost‐efficient compared with its rival with regard to repositioning, then that firm can earn higher profits than those of a high‐positioned firm by strategically offering its low‐end product. These results contrast sharply with those based on the standard vertical differentiation model.

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BackgroundRadiomics analysis using on‐board volumetric images has attracted research attention as a method for predicting prognosis during treatment; however, the lack of standardization is still one of the main concerns.PurposeThis study investigated the factors that influence the reproducibility of radiomic features extracted from on‐board volumetric images using an anthropomorphic radiomics phantom. Furthermore, a phantom experiment was conducted with different treatment machines from multiple institutions as external validation to identify reproducible radiomic features.MethodsThe phantom was designed to be 35 × 20 × 20 cm with eight types of heterogeneous spheres (⌀ = 1, 2, and 3 cm). On‐board volumetric images were acquired using 15 treatment machines from eight institutions. Of these, kilovoltage cone‐beam computed tomography (kV‐CBCT) image data acquired from four treatment machines at one institution were used as an internal evaluation dataset to explore the reproducibility of radiomic features. The remaining image data, including kV‐CBCT, megavoltage‐CBCT (MV‐CBCT), and megavoltage computed tomography (MV‐CT) provided by seven different institutions (11 treatment machines), were used as an external validation dataset. A total of 1,302 radiomic features, including 18 first‐order, 75 texture, 465 (i.e., 93 × 5) Laplacian of Gaussian (LoG) filter‐based, and 744 (i.e., 93 × 8) wavelet filter‐based features, were extracted within the spheres. The intraclass correlation coefficient (ICC) was calculated to explore feature repeatability and reproducibility using an internal evaluation dataset. Subsequently, the coefficient of variation (COV) was calculated to validate the feature variability of external institutions. An absolute ICC exceeding 0.85 or COV under 5% was considered indicative of a highly reproducible feature.ResultsFor internal evaluation, ICC analysis showed that the median percentage of radiomic features with high repeatability was 95.2%. The ICC analysis indicated that the median percentages of highly reproducible features for inter‐tube current, reconstruction algorithm, and treatment machine were decreased by 20.8%, 29.2%, and 33.3%, respectively. For external validation, the COV analysis showed that the median percentage of reproducible features was 31.5%. A total of 16 features, including nine LoG filter‐based and seven wavelet filter‐based features, were indicated as highly reproducible features. The gray‐level run‐length matrix (GLRLM) was classified as containing the most frequent features (N = 8), followed by the gray‐level dependence matrix (N = 7) and gray‐level co‐occurrence matrix (N = 1) features.ConclusionsWe developed the standard phantom for radiomics analysis of kV‐CBCT, MV‐CBCT, and MV‐CT images. With this phantom, we revealed that the differences in the treatment machine and image reconstruction algorithm reduce the reproducibility of radiomic features from on‐board volumetric images. Specifically, the most reproducible features for external validation were LoG or wavelet filter‐based GLRLM features. However, the acceptability of the identified features should be examined in advance at each institution before applying the findings to prognosis prediction.

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