Journal Articles

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    Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.
    (Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine, 2024-11-29) Zeng H; Chen Q; E X; Feng Y; Lv M; Zeng S; Shen W; Guan W; Zhang Y; Zhao R; Wang S; Yu J
    Background Lung cancer poses a significant global health challenge. Adaptive radiotherapy (ART) addresses uncertainties due to lung tumor dynamics. We aimed to investigate a comprehensively and systematically validated offline ART regimen with high clinical feasibility for lung cancer. Methods This study enrolled 102 lung cancer patients, who underwent kV iterative cone-beam computed tomography (iCBCT). Data collection included iCBCT and planning CT (pCT) scans. Among these, data from 70 patients were employed for training the UNet++ based deep learning model, while 15 patients were allocated for testing the model. The model transformed iCBCT into adaptive CT (aCT). Clinical radiotherapy feasibility was verified in 17 patients. The dosimetric evaluation encompassed GTV, organs at risk (OARs), and monitor units (MU), while delivery accuracy was validated using ArcCHECK and thermoluminescent dosimeter (TLD) detectors. Results The UNet++ based deep learning model substantially improved image quality, reducing mean absolute error (MAE) by 70.05%, increasing peak signal-to-noise ratio (PSNR) by 17.97%, structural similarity (SSIM) by 7.41%, and the Hounsfield Units (HU) of aCT approaching a closer proximity to pCT compared to kV iCBCT. There were no significant differences observed in the dosimetric parameters of GTV and OARs between the aCT and pCT plans, confirming the accuracy of the dose maps in ART plans. Similarly, MU manifested no notable disparities, underscoring the consistency in treatment efficiency. Gamma passing rates for intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) plans derived from aCT and pCT exceeded 98%, while the deviations in TLD measurements (within 2% to 7%) also exhibited no significant differences, thus corroborating the precision of dose delivery. Conclusion An offline ART regimen utilizing kV iCBCT and UNet++ based deep learning model is clinically feasible for lung cancer treatment. This approach provides enhanced image quality, comparable treatment plans to pCT, and precise dose delivery.
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    Determinants of Gaps in Human Behaviour in Fire Research
    (Springer Nature, 2024-08-08) Ronchi E; Kapalo K; Bode N; Boyce K; Cuesta A; Feng Y; Galea ER; Geoerg P; Gwynne S; Kennedy EB; Kinateder M; Kinsey M; Kuligowski E; Köster G; Lovreglio R; Mossberg A; Ono R; Spearpoint M; Strahan K; Wong SD
    This short communication presents the findings of the work conducted by the human behaviour in fire permanent working group of the International Association for Fire Safety Science. Its aim is to identify determinants of research gaps in the field of human behaviour in fire. Two workshops were conducted in 2023 in which research gaps were identified and discussed by twenty experts. The workshops led experts through a series of questions to determine the reasons (or determinants) for these gaps in human behaviour in building fires and wildfires. Through the questions, the primary identified determinants were (1) researchers’ literacy in the variety of methods adopted in the field, (2) difficulties associated with recruitment of study participants, (3) multi-disciplinary barriers across different research sub-domains, and (4) issues in obtaining funding for addressing fundamental human behaviour in fire research questions. Two key issues emerged from an open discussion during the workshops, namely the difficulties in attracting and training new people in the field (given the limited educational offers around the world on the topic) and the need for more regular opportunities for the community to meet.
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    In the radiance of enlightenment: The influence of nontheistic religions on corporate default risk
    (Elsevier B V, 2024-06) Feng Y; Hao W; Fang J; Wongchoti U
    We investigate whether religious site density around a firm's headquarters is related to corporate default risk in China. We find that public firms surrounded by a higher number of Buddhist and Taoist temples are associated with lower default risk. In contrast to the widely documented impact of Western religiosity on corporate behavior, our mechanism tests indicate that lower default risk related to religious site density is primarily driven by better corporate governance and not by a surge in corporate conservatism. Finally, we find that this default risk lowering effect is more pronounced when firms also possess greater political resources.