Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea.
Department of Otolaryngology, Ajou University School of Medicine, Suwon, Republic of Korea.
Clin Imaging. 2024 Oct;114:110254. doi: 10.1016/j.clinimag.2024.110254. Epub 2024 Aug 9.
This study proposed a three-dimensional (3D) multi-modal learning-based model for the automated prediction and classification of lymph node metastasis in patients with non-small cell lung cancer (NSCLC) using computed tomography (CT) images and clinical information.
We utilized clinical information and CT image data from 4239 patients with NSCLC across multiple institutions. Four deep learning algorithm-based multi-modal models were constructed and evaluated for lymph node classification. To further enhance classification performance, a soft-voting ensemble technique was applied to integrate the outcomes of multiple multi-modal models.
A comparison of the classification performance revealed that the multi-modal model, which integrated CT images and clinical information, outperformed the single-modal models. Among the four multi-modal models, the Xception model demonstrated the highest classification performance, with an area under the curve (AUC) of 0.756 for the internal test dataset and 0.736 for the external validation dataset. The ensemble model (SEResNet50_DenseNet121_Xception) exhibited even better performance, with an AUC of 0.762 for the internal test dataset and 0.751 for the external validation dataset, surpassing the multi-modal model's performance.
Integrating CT images and clinical information improved the performance of the lymph node metastasis prediction models in patients with NSCLC. The proposed 3D multi-modal lymph node prediction model can serve as an auxiliary tool for evaluating lymph node metastasis in patients with non-pretreated NSCLC, aiding in patient screening and treatment planning.
本研究提出了一种基于三维（3D）多模态学习的模型，用于使用计算机断层扫描（CT）图像和临床信息自动预测和分类非小细胞肺癌（NSCLC）患者的淋巴结转移。
我们利用了来自多个机构的 4239 名 NSCLC 患者的临床信息和 CT 图像数据。构建并评估了四个基于深度学习算法的多模态模型以进行淋巴结分类。为了进一步提高分类性能，应用软投票集成技术整合了多个多模态模型的结果。
对分类性能的比较表明，整合 CT 图像和临床信息的多模态模型优于单模态模型。在四个多模态模型中，Xception 模型表现出最高的分类性能，内部测试数据集的曲线下面积（AUC）为 0.756，外部验证数据集的 AUC 为 0.736。集成模型（SEResNet50_DenseNet121_Xception）的表现甚至更好，内部测试数据集的 AUC 为 0.762，外部验证数据集的 AUC 为 0.751，超过了多模态模型的性能。
整合 CT 图像和临床信息可提高 NSCLC 患者淋巴结转移预测模型的性能。所提出的 3D 多模态淋巴结预测模型可作为评估未经预处理的 NSCLC 患者淋巴结转移的辅助工具，有助于患者筛选和治疗计划。
