We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N patients/planning CTs with a total of 449 LNs. In the deep learning method, a previously developed nnU-Net 3D/2D ensemble model is used to autosegment 20 H/N levels, with each LN subsequently being algorithmically assigned to the closest-level autosegmentation. In the nonrigid-registration-based mapping method, LNs are mapped into a calculated template CT representing the cohort-average patient anatomy, and kernel density estimation is employed to estimate the underlying average 3D-LN probability distribution allowing for analysis and visualization without prespecified level definitions. Multireader assessment by three radio-oncologists with majority voting was used to evaluate the deep learning method and obtain the ground-truth distribution. For the mapping technique, the proportion of LNs predicted by the 3D probability distribution for each level was calculated and compared to the deep learning and ground-truth distributions. As determined by a multireader review with majority voting, the deep learning method correctly categorized all 449 LNs to their respective levels. Level 2 showed the highest LN involvement (59.0%). The level involvement predicted by the mapping technique was consistent with the ground-truth distribution (p for difference 0.915). Application of the proposed methods to multicenter cohorts with selected H/N tumor subtypes for informing optimal RT target volume design is promising.
本研究引入一种基于深度学习和图像配准的方法，用于自动分析头颈部癌症队列中淋巴结转移的空间分布，以指导放射治疗靶区设计。这两种方法在包含193例头颈部癌症患者/计划CT图像及共计449枚淋巴结的队列中进行评估。在深度学习方法中，采用先前开发的nnU-Net三维/二维集成模型自动分割20个头颈部淋巴引流区，随后通过算法将每个淋巴结分配至距离最近的自动分割区域。在基于非刚性配准的映射方法中，淋巴结被映射至代表队列平均解剖结构的计算模板CT中，并采用核密度估计法推算潜在的平均三维淋巴结概率分布，从而无需预设分区定义即可实现分析和可视化。通过三位放射肿瘤科医师采用多数表决法进行多阅片者评估，以验证深度学习方法的准确性并获取基准分布数据。对于映射技术，计算各分区三维概率分布预测的淋巴结比例，并与深度学习方法和基准分布进行比较。经多数表决的多阅片者评审确认，深度学习方法将全部449枚淋巴结准确归类至相应分区（准确率100%）。第二区淋巴结转移率最高（59.0%）。映射技术预测的分区转移率与基准分布具有一致性（差异p值=0.915）。将所提方法应用于多中心队列中特定头颈部肿瘤亚型的研究，有望为优化放射治疗靶区设计提供重要依据。
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