将基于深度学习的GTV自动分割直接集成到临床放射治疗计划系统中
Direct integration of deep learning-based GTV auto-segmentation into a clinical radiotherapy planning system.
摘要(中文译文)
手动大体肿瘤体积（GTV）勾画用于放疗耗时且易受观察者间差异影响。基于深度学习（DL）的自动分割提供了更快速、更一致的解决方案，但其向临床实践的转化仍然有限。本研究旨在通过将自主研发的GTV自动分割深度学习模型整合至放疗治疗计划系统（TPS）来弥合这一差距。为展示整合过程，一个头颈部GTV分割模型成功部署并在TPS内无缝运行。在此整合中，深度学习生成的GTV轮廓在三分钟内完成，证明了在TPS中进行基于深度学习的GTV分割而不干扰放疗工作流程的可行性。
Abstract
Manual Gross Tumor Volume (GTV) delineation for radiotherapy is time‑consuming and prone to inter‑observer variability. Deep Learning (DL)-based auto‑segmentation offers faster and more consistent solutions, but translation into clinical practice remains limited. This study aimed to bridge this gap by integrating an in‑house GTV auto‑segmentation DL model into a radiotherapy Treatment Planning System (TPS). To demonstrate the integration process, a head‑and‑neck GTV segmentation model was successfully deployed and operated seamlessly within the TPS. In this integration, DL‑generated GTV contours were produced in under three minutes, demonstrating the feasibility of DL‑based GTV segmentation in the TPS without disrupting the radiotherapy workflow.
如何引用
AMA
Bao Ngoc Huynh, Cecilia Marie Futsaether, Oliver Tomic, Kristian Hovde Liland, Manish Kakar, Taran Paulsen Hellebust, et al. Direct integration of deep learning-based GTV auto-segmentation into a clinical radiotherapy planning system.. Physics and imaging in radiation oncology. 2026; doi:10.1016/j.phro.2026.100962.
APA
Bao Ngoc Huynh, Cecilia Marie Futsaether, Oliver Tomic, Kristian Hovde Liland, Manish Kakar, Taran Paulsen Hellebust, et al (2026). Direct integration of deep learning-based GTV auto-segmentation into a clinical radiotherapy planning system.. Physics and imaging in radiation oncology. https://doi.org/10.1016/j.phro.2026.100962
GB/T 7714
Bao Ngoc Huynh, Cecilia Marie Futsaether, Oliver Tomic, Kristian Hovde Liland, Manish Kakar, Taran Paulsen Hellebust, et al. Direct integration of deep learning-based GTV auto-segmentation into a clinical radiotherapy planning system.[J]. Physics and imaging in radiation oncology, 2026 doi:10.1016/j.phro.2026.100962.
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