Background: Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective: Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods: The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results: We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions: Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.

中文翻译：

---

放射治疗临床适用的头颈解剖结构分割：深度学习算法开发和验证研究

背景：全球每年有超过 50 万人被诊断患有头颈癌。放射治疗是这种疾病的重要治疗方法，但需要手动时间来描绘处于危险中的放射敏感器官。这种计划过程可能会延迟治疗，同时还会引入操作者间的差异，从而导致下游辐射剂量差异。尽管自动分割算法提供了一种可能节省时间的解决方案，但定义、量化和实现专家性能方面的挑战仍然存在。目标：采用深度学习方法，我们的目标是展示 3D U-Net 架构，该架构在描绘临床实践中通常分割的 21 个不同的处于危险中的头颈器官方面实现了专家级的性能。方法：该模型在常规临床实践中获得的 663 个去识别化计算机断层扫描数据集上进行训练，其中的分割来自临床实践，也有经验丰富的放射技师作为本研究的一部分创建的分割，所有这些都符合共识的风险器官定义。结果：我们通过评估其在临床实践中的 21 个计算机断层扫描测试集上的性能来证明该模型的临床适用性，每个扫描集由 2 名独立专家分割出 21 个处于危险中的器官。我们还引入了表面骰子相似系数，这是一种用于比较器官勾画的新指标，用于量化危险器官表面轮廓而不是体积之间的偏差，更好地反映纠正自动器官分割中的错误的临床任务。然后，该模型的通用性在两个不同的开源数据集上得到了证明，反映了不同的中心和国家的模型培训情况。结论：深度学习是一种有效且临床适用的放射治疗头颈解剖分割技术。通过适当的验证研究和监管部门的批准，该系统可以提高放射治疗途径的效率、一致性和安全性。

这只是摘要。在 JMIR 网站上阅读全文。JMIR 是互联网时代电子健康和医疗保健领域领先的开放获取期刊。