Simulation-Free Radiation Therapy: An Emerging Form of Treatment Planning to Expedite Plan Generation for Patients Receiving Palliative Radiation Therapy

Purpose: Herein we report the clinical and dosimetric experience for patients with metastases treated with palliative simulation-free radiation therapy (SFRT) at a single institution. Methods and Materials: SFRT was performed at a single institution. Multiple fractionation regimens were used. Diagnostic imaging was used for treatment planning. Patient characteristics as well as planning and treatment time points were collected. A matched cohort of patients with conventional computed tomography simulation radiation therapy (CTRT) was acquired to evaluate for differences in planning and treatment time. SFRT dosimetry was evaluated to determine the fidelity of SFRT. Descriptive statistics were calculated on all variables and statistical significance was evaluated using the Wilcoxon signed rank test and t test methods. Results: Thirty sessions of SFRT were performed and matched with 30 sessions of CTRT. Seventy percent of SFRT and 63% of CTRT treatments were single fraction. The median time to plan generation was 0.88 days (0.19-1.47) for SFRT and 1.90 days (0.39-5.23) for CTRT (P = .02). The total treatment time was 41 minutes (28-64) for SFRT and 30 minutes (21-45) for CTRT (P = .02). In the SFRT courses, the maximum and mean deviations in the actual delivered dose from the approved plans for the maximum dose were 4.1% and 0.07%, respectively. All deliveries were within a 5% threshold and deemed clinically acceptable. Conclusions: Palliative SFRT is an emerging technique that allowed for a statistically significant lower time to plan generation and was dosimetrically acceptable. This benefit must be weighed against increased total treatment time for patients receiving SFRT compared (c) 2022 The Author(s). Published by Elsevier Inc. on behalf of American Society for Radiation Oncology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Conformity Consumer Behavior and External Threats: An Empirical Analysis in China During the COVID-19 Pandemic

Conformity consumer behavior refers to a preference of using the behaviors or expectations of others as a guideline for one's own consumption patterns. Significant characteristics of conformity consumer behavior have been observed during the COVID-19 pandemic, and it has greatly hindered resource allocation and pandemic management. Nonetheless, the reasons why a public health emergency, exemplified by COVID-19, triggers conformity consumer behavior remain unclear. This study proposes and tests a theoretical framework to explore the psychological mechanisms of conformity consumer behavior during the COVID-19 pandemic. Results indicate that pandemic severity positively affect conformity consumer behavior, sense of fear plays a mediating role between pandemic severity and conformity consumer behavior, and sense of control does not play a moderating role. This implies that fear drives conformity consumer behavior and people may tend to consume in this way when they perceive a strong sense of fear no matter how strong their sense of control is. The conclusion will help managers to guide consumer behavior during social crisis and emergencies.

DeepTrack: Monitoring and Exploring Spatio-Temporal Data - A Case of Tracking COVID-19

Spatio-temporal data analysis is very important in many time-critical applications. We take Coronavirus disease (COVID-19) as an example, and the key questions that everyone will ask every day are: how does Coronavirus spread? where are the high-risk areas? where have confirmed cases around me? Interactive data analytics, which allows general users to easily monitor and explore such events, plays a key role. However, some emerging cases, such as COVID-19, bring many new challenges: (C1) New information may come with different formats: basic structured data such as confirmed/suspected/serious/death/recovered cases, unstructured data from newspapers for travel history of confirmed cases, and so on. (C2) Discovering new insights: data visualization is widely used for storytelling;however, the challenge here is how to automatically find "interesting stories", which might be different from day to day. We propose DEEPTRACK, a system that monitors spatio-temporal data, using the case of COVID-19. For (C1), we describe (a) how we integrate and clean data from different sources by existing modules. For (C2), we discuss (b) how to build new modules for ad-hoc data sources and requirements, (c) what are the basic (or static) charts used;and (d) how to generate recommended (or dynamic) charts that are based on new incoming data. The attendees can use DeepTrack to interactively explore various COVID-19 cases.