Institute for Diagnostic and Interventional Radiology, University Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
Philips Research Hamburg, Hamburg, Germany.
Eur J Radiol. 2021 Jun;139:109718. doi: 10.1016/j.ejrad.2021.109718. Epub 2021 Apr 20.
To develop a deep-learning (DL)-based approach for thoracic lymph node (LN) mapping based on their anatomical location.
The training-and validation-dataset included 89 contrast-enhanced computed tomography (CT) scans of the chest. 4201 LNs were semi-automatically segmented and then assigned to LN levels according to their anatomical location. The LN level classification task was addressed by a multi-class segmentation procedure using a fully convolutional neural network. Mapping was performed by firstly determining potential level affiliation for each voxel and then performing majority voting over all voxels belonging to each LN. Mean classification accuracies on the validation data were calculated separately for each level and overall Top-1, Top-2 and Top-3 scores were determined, where a Top-X score describes how often the annotated class was within the top-X predictions. To demonstrate the clinical applicability of our model, we tested its N-staging capabilities in a simulated clinical use case scenario assuming a patient diseased with lung cancer.
The artificial intelligence(AI)-based assignment revealed mean classification accuracies of 86.36 % (Top-1), 94.48 % (Top-2) and 96.10 % (Top-3). Best accuracies were achieved for LNs in the subcarinal level 7 (98.31 %) and axillary region (98.74 %). The highest misclassification rates were observed among LNs in adjacent levels. The proof-of-principle application in a simulated clinical use case scenario for automated tumor N-staging showed a mean classification accuracy of up to 96.14 % (Top-1).
The proposed AI approach for automatic classification of LN levels in chest CT as well as the proof-of-principle-experiment for automatic N-staging, revealed promising results, warranting large-scale validation for clinical application.
开发一种基于解剖位置的基于深度学习（DL）的胸淋巴结（LN）图谱绘制方法。
训练和验证数据集包括 89 例胸部增强 CT 扫描。4201 个 LN 被半自动分割，然后根据其解剖位置分配到 LN 水平。LN 水平分类任务采用全卷积神经网络的多类分割程序解决。映射是通过首先确定每个体素的潜在水平归属，然后对属于每个 LN 的所有体素进行多数投票来完成的。在验证数据上分别计算每个水平的平均分类准确率，并确定总体 Top-1、Top-2 和 Top-3 得分，其中 Top-X 得分描述了注释类在 Top-X 预测中的出现频率。为了展示我们模型的临床适用性，我们在模拟的临床使用案例场景中测试了其 N 分期能力，假设患者患有肺癌。
基于人工智能的分配方法的平均分类准确率为 86.36%（Top-1）、94.48%（Top-2）和 96.10%（Top-3）。在隆突下 7 区和腋窝区域（98.74%）的 LN 中获得了最佳准确率。在相邻水平的 LN 中观察到最高的误分类率。在自动肿瘤 N 分期的模拟临床使用案例场景中的初步应用表明，平均分类准确率高达 96.14%（Top-1）。
提出的用于自动分类胸部 CT 中 LN 水平的人工智能方法以及自动 N 分期的初步应用实验结果令人鼓舞，值得进行大规模验证以用于临床应用。
