Identification of Aquatic Organisms Using a Magneto-Optical Element.

In recent advanced information society, it is important to individually identify products or living organisms automatically and quickly. However, with the current identifying technology such as RFID tag or biometrics, it is difficult to apply to amphibians such as frogs or newts because of its size, stability, weakness under a wet environment and so on. Thus, this research aims to establish a system that can trace small amphibians easily even in a wet environment and keep stable sensing for a long time. The magnetism was employed for identification because it was less influenced by water for a long time. Here, a novel magnetization-free micro-magnetic tag is proposed and fabricated with low cost for installation to a living target sensed by Magneto-Optical sensor for high throughput sensing. The sensing ability of the proposed method, which was evaluated by image analysis, indicated that it was less than half of the target value (1 mm) both in the water and air. The FEM analysis showed that it is approximately twice the actual identification ability under ideal conditions, which suggests that the actual sensing ability can be extended by further improvement of the sensing system. The developed magnetization-free micro-magnetic tag can contribute to keep up the increasing demand to identify a number of samples under a wet environment especially with the development of gene technology.

A rare instance of latent systematic error in volumetric-modulated arc therapy with field-extended multi-isocentre irradiation leading to a serious dose-delivery accident.

Volumetric-modulated arc therapy (VMAT) with field-extended multi-isocentre irradiation (VMAT-FEMII) is an effective irradiation technique, particularly for large planning target volumes in the craniocaudal direction. A variety of treatment planning techniques have been reported to reduce the dosimetric impact. However, there is no guarantee that unexpected latent systematic errors would not occur. Herein, we report the experience with a rare case that could have led to a serious VMAT-FEMII-related accident. A patient with uterine cervical carcinoma was scheduled for VMAT-FEMII to the whole pelvis and the para-aortic lymph node region. A combination of the two sets of field groups with different isocentres was planned: one to cover the para-aortic lymph nodes and the other to cover the whole pelvis. Measurements based on the pretreatment dose delivery quality assurance (QA) revealed an unexpected overdose of >20% in the field overlap region. This overdose phenomenon is not reflected in the calculated dose distribution in the radiotherapy treatment planning system. Therefore, the plan was altered; a homogeneous dose distribution inside the dose junction was achieved. Several analyses were performed to elucidate the overdosing phenomenon. However, no conclusive answer was found to why non-reflection at the calculated dose distribution was found. The limitations to VMAT-FEMII are primarily related to systematic errors in the positional setup from patient-derived and/or mechanical sources. However, this report highlights a rare case of overdosing caused by inverse optimization and dose calculation. We recommend checking the aperture status of the jaw and multi-leaf collimator at each control point of the treatment plan and using a high-resolution image measurement system on a VMAT-FEMII QA to confirm the dose junction status.

Virtual clinical trial based on outcome modeling with iteratively redistributed extrapolation data.

Virtual clinical trials (VCTs) can potentially simulate clinical trials on a computer, but their application with a limited number of past clinical cases is challenging due to the biased estimation of the statistical population. In this study, we developed ExMixup, a novel training technique based on machine learning, using iteratively redistributed extrapolated data. Information obtained from 100 patients with prostate cancer and 385 patients with oropharyngeal cancer was used to predict the recurrence after radiotherapy. Model performance was evaluated by developing outcome prediction models based on three types of training methods: training with original data (baseline), interpolation data (Mixup), and interpolation + extrapolation data (ExMixup). Two types of VCTs were conducted to predict the treatment response of patients with distinct characteristics compared to the training data obtained from patient cohorts categorized under risk classification or cancer stage. The prediction models developed with ExMixup yielded concordance indices (95% confidence intervals) of 0.751 (0.719-0.818) and 0.752 (0.734-0.785) for VCTs on the prostate and oropharyngeal cancer datasets, respectively, which significantly outperformed the baseline and Mixup models (P < 0.01). The proposed approach could enhance the ability of VCTs to predict treatment results in patients excluded from past clinical trials.